changeset 0:054b2522a933 draft default tip

planemo upload for repository https://github.com/Marie59/Sentinel_2A/srs_tools commit b32737c1642aa02cc672534e42c5cb4abe0cd3e7
author ecology
date Mon, 09 Jan 2023 13:38:38 +0000 (23 months ago)
parents
children
files Lib_preprocess_S2.r alpha_beta.r biodiv_indices_global.r comparison_div.r functions.r indices_spectral.r macro.xml pca_raster.r preprocess_S2.r prosail-master/R/Lib_PROSAIL.R prosail-master/R/Lib_PROSAIL_HybridInversion.R prosail-master/R/Lib_SpectralIndices.R test-data/12a0b625-9ad5-4251-a57a-305e22edef2e.xml test-data/Metadata_validation.txt test-data/Mission.csv test-data/NDVI.tabular test-data/S2A_MSIL2A_20200306T015621_N0214_R117_T51JXN_20200306T034744.zip test-data/S2A_Subset test-data/S2A_Subset.hdr test-data/S2A_T33NUD_Plots.zip val_metadata.r val_metadata.xml
diffstat 22 files changed, 15539 insertions(+), 0 deletions(-) [+]
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--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/Lib_preprocess_S2.r	Mon Jan 09 13:38:38 2023 +0000
@@ -0,0 +1,1335 @@
+# == == == == == == == == == == == == == == == == == == == == == == == == == == ==
+# preprocS2
+# Lib_preprocess_S2.R
+# == == == == == == == == == == == == == == == == == == == == == == == == == == ==
+# PROGRAMMERS:
+# Jean-Baptiste FERET <jb.feret@teledetection.fr>
+# Copyright 2021/08 Jean-Baptiste FERET
+# == == == == == == == == == == == == == == == == == == == == == == == == == == ==
+# This Library contains functions to preprocess Sentinel-2 images downloaded from
+# different data hubs, such as THEIA, PEPS or SCIHUB
+# == == == == == == == == == == == == == == == == == == == == == == == == == == ==
+
+#" This function adjusts information from ENVI header
+#"
+#" @param dsn character. path where to store the stack
+#" @param bands list. should include "bandname", and if possible "wavelength"
+#" @param sensor character. Name of the sensor used to acquire the image
+#" @param stretch boolean. Set TRUE to get 10% stretching at display for reflectance, mentioned in hdr only
+#"
+#" @return None
+#" @importFrom utils read.table
+#" @importFrom raster hdr raster
+#" @export
+adjust_envi_hdr <- function(dsn, bands, sensor = "Unknown", stretch = FALSE) {
+
+  # Edit hdr file to add metadata
+  hdr <- read_envi_header(get_hdr_name(dsn))
+  hdr$`band names` <- bands$bandname
+  if (length(bands$wavelength) == length(bands$bandname)) {
+    hdr$wavelength <- bands$wavelength
+  }else {
+    hdr$wavelength <- NULL
+  }
+  if (stretch == TRUE) {
+    hdr$`default stretch` <- "0.000000 1000.000000 linear"
+  }
+  hdr$`z plot range` <- NULL
+  hdr$`data ignore value` <- "-Inf"
+  hdr$`sensor type` <- sensor
+  write_envi_header(hdr = hdr, hdrpath = get_hdr_name(dsn))
+
+  # remove unnecessary files
+  file2remove <- paste(dsn, ".aux.xml", sep = "")
+  if (file.exists(file2remove)) file.remove(file2remove)
+  file2remove <- paste(dsn, ".prj", sep = "")
+  if (file.exists(file2remove)) file.remove(file2remove)
+  file2remove <- paste(dsn, ".stx", sep = "")
+  if (file.exists(file2remove)) file.remove(file2remove)
+  return(invisible())
+}
+
+#" This function saves reflectance files
+#"
+#" @param s2sat character. Sentinel-2 mission ("2A" or "2B")
+#" @param tile_s2 character. S2 tile name (2 numbers + 3 letters)
+#" @param dateacq_s2 double. date of acquisition
+#"
+#" @return s2mission character. name of the S2 mission (2A or 2B)
+#" @importFrom sen2r safe_getMetadata check_scihub_connection s2_list
+#" @export
+check_s2mission <- function(s2sat, tile_s2, dateacq_s2) {
+
+  # is mission already defined by user?
+  if (!is.null(s2sat)) {
+    if (s2sat == "2A") {
+      s2mission <- "2A"
+    }else if (s2sat == "2B") {
+      s2mission <- "2B"
+    }else {
+      message("Could not identify if image from Sentinel-2A or -2B")
+      message("Defining central wavelength of spectral bands based on S2A")
+      s2mission <- "2A"
+    }
+  }else {
+    message("Could not identify if image from Sentinel-2A or -2B")
+    message("Defining central wavelength of spectral bands based on S2A")
+    s2mission <- "2A"
+  }
+  return(s2mission)
+}
+
+#" this function aims at computing directory size
+#" @param path character. path for directory
+#" @param recursive boolean . set T if recursive
+#"
+#" @return size_files numeric. size in bytes
+#" - image stack
+#" - path for individual band files corresponding to the stack
+#" - path for vector (reprojected if needed)
+#"
+#" @importFrom raster raster
+#" @importFrom tools file_path_sans_ext file_ext
+#" @export
+dir_size <- function(path, recursive = TRUE) {
+  stopifnot(is.character(path))
+  files <- list.files(path, full.names = TRUE, recursive = recursive)
+  vect_size <- sapply(files, function(x) file.size(x))
+  size_files <- sum(vect_size)
+  return(size_files)
+}
+
+#" This function reads S2 data from L2A directories downloaded from
+#" various data hubs including THEIA, PEPS & SCIHUB (SAFE format & LaSRC)
+#" @param path_dir_s2 character. path for S2 directory
+#" @param path_vector character. path for vector file
+#" @param s2source character. type of directory format (depends on atmospheric correction: SAFE produced from Sen2Cor)
+#" @param resolution numeric. buffer applied to vector file (in meters)
+#" @param interpolation character. method for resampling. default = "bilinear"
+#" @param fre_sre character. SRE or FRE products from THEIA
+#"
+#" @return listout list.
+#" - image stack
+#" - path for individual band files corresponding to the stack
+#" - path for vector (reprojected if needed)
+#"
+#" @importFrom raster raster
+#" @importFrom tools file_path_sans_ext file_ext
+#" @export
+extract_from_s2_l2a <- function(path_dir_s2, path_vector = NULL, s2source = "SAFE",
+                                resolution = 10, interpolation = "bilinear", fre_sre = "FRE") {
+  # Get list of paths corresponding to S2 bands and depending on S2 directory
+  s2_bands <- get_s2_bands(path_dir_s2 = path_dir_s2,
+                           s2source = s2source,
+                           resolution = resolution,
+                           fre_sre = fre_sre)
+
+  if (length(s2_bands$s2bands_10m) > 0) {
+    rastmp <- raster::raster(s2_bands$s2bands_10m[[1]])
+  } else if (length(s2_bands$s2bands_20m) > 0) {
+    rastmp <- raster::raster(s2_bands$s2bands_20m[[1]])
+  }
+  # check if vector and raster share the same projection. if not, re-project vector
+  if (!is.null(path_vector)) {
+    raster_proj <- raster::projection(rastmp)
+    path_vector_reproj <- paste(tools::file_path_sans_ext(path_vector), "_reprojected.shp", sep = "")
+    path_vector <- reproject_shp(path_vector_init = path_vector,
+                                 newprojection = raster_proj,
+                                 path_vector_reproj = path_vector_reproj)
+  }
+  # Extract data corresponding to the vector footprint (if provided) & resample data if needed
+  if (length(s2_bands$s2bands_10m) > 0) {
+    stack_10m <- read_s2bands(s2_bands = s2_bands$s2bands_10m, path_vector = path_vector,
+                              resampling = 1, interpolation = interpolation)
+  }
+  if (length(s2_bands$s2bands_20m) > 0) {
+    if (resolution == 10 && s2source != "LaSRC") {
+      resampling <- 2
+    }else {
+      resampling <- 1
+    }
+    stack_20m <- read_s2bands(s2_bands = s2_bands$s2bands_20m, path_vector = path_vector,
+                              resampling = resampling, interpolation = interpolation)
+  }
+  # get full stack including 10m and 20m spatial resolution
+  if (length(s2_bands$s2bands_10m) > 0 && length(s2_bands$s2bands_20m) > 0) {
+    diffxstart <- attributes(stack_10m)$dimensions[[1]]$from - attributes(stack_20m)$dimensions[[1]]$from
+    diffxstop <- attributes(stack_10m)$dimensions[[1]]$to - attributes(stack_20m)$dimensions[[1]]$to
+    diffystart <- attributes(stack_10m)$dimensions[[2]]$from - attributes(stack_20m)$dimensions[[2]]$from
+    diffystop <- attributes(stack_10m)$dimensions[[2]]$to - attributes(stack_20m)$dimensions[[2]]$to
+    if (!diffxstop == 0) {
+      # size of 20m > size of 10m --> reduce 20m
+      # size of 10m > size of 20m --> reduce 10m
+      if (diffxstop > 0) {
+        stack_10m <- stack_10m[, 1:(dim(stack_10m)[1] - diffxstop), , ]
+      }else if (diffxstop < 0) {
+        stack_20m <- stack_20m[, 1:(dim(stack_20m)[1] + diffxstop), , ]
+      }
+    }
+    if (!diffystop == 0) {
+      if (diffystop > 0) {
+        stack_10m <- stack_10m[, , 1:(dim(stack_10m)[2] - diffystop), ]
+      }else if (diffystop < 0) {
+        stack_20m <- stack_20m[, , 1:(dim(stack_20m)[2] + diffystop), ]
+      }
+    }
+    if (!diffxstart == 0) {
+      if (diffxstart > 0) {
+        stack_20m <- stack_20m[, (1 + diffxstart):dim(stack_20m)[1], , ]
+      }else if (diffxstart < 0) {
+        stack_10m <- stack_10m[, (1 - diffxstart):dim(stack_10m)[1], , ]
+      }
+    }
+    if (!diffystart == 0) {
+      if (diffystart > 0) {
+        stack_20m <- stack_20m[, , (1 + diffystart):dim(stack_20m)[2], ]
+      }else if (diffystart < 0) {
+        stack_10m <- stack_10m[, , (1 - diffystart):dim(stack_10m)[2], ]
+      }
+    }
+    # reorder bands with increasing wavelength
+    s2bands <- c("B02", "B03", "B04", "B05", "B06", "B07", "B08", "B8A", "B11", "B12", "Cloud")
+    namebands <- c(names(s2_bands$s2bands_10m), names(s2_bands$s2bands_20m))
+    reorder_bands <- match(s2bands, namebands)
+    namebands <- namebands[reorder_bands]
+    listfiles <- c(stack_10m$attr, stack_20m$attr)[reorder_bands]
+
+    # adjust size to initial vector footprint without buffer
+    # --> buffer is needed in order to ensure that extraction following
+    # footprint of vector matches for images of different spatial resolution
+    # get bounding box corresponding to footprint of image or image subset
+    bb_xycoords <- get_bb(path_raster = listfiles[1],
+                          path_vector = path_vector, buffer = 0)
+
+    # prepare reading data for extent defined by bounding box
+    nxoff <- bb_xycoords$UL$col
+    nyoff <- bb_xycoords$UL$row
+    nxsize <- bb_xycoords$UR$col - bb_xycoords$UL$col + 1
+    nysize <- bb_xycoords$LR$row - bb_xycoords$UR$row + 1
+    nbufxsize <- nxsize
+    nbufysize <- nysize
+    s2_stack <- stars::read_stars(listfiles, along = "band",
+                                  RasterIO = list(nXOff = nxoff, nYOff = nyoff,
+                                                  nXSize = nxsize, nYSize = nysize,
+                                                  nBufXSize = nbufxsize, nBufYSize = nbufysize,
+                                                  resample = "nearest_neighbour"), proxy = TRUE)
+
+
+    names(s2_stack$attr) <- namebands
+  }else if (length(s2_bands$s2bands_10m) > 0) {
+    s2_stack <- stack_10m
+    namebands <- names(s2_bands$s2bands_10m)
+    names(s2_stack$attr) <- namebands
+  }else if (length(s2_bands$s2bands_20m) > 0) {
+    s2_stack <- stack_20m
+    namebands <- names(s2_bands$s2bands_20m)
+    names(s2_stack$attr) <- namebands
+  }
+
+  listout <- list("s2_stack" = s2_stack, "s2_bands" = s2_bands, "path_vector" = path_vector,
+                  "namebands" = namebands)
+  return(listout)
+}
+
+#" This function gets coordinates of a bounding box defined by a vector (optional) and a raster
+#"
+#" @param path_raster character. path for raster file
+#" @param path_vector character. path for vector file
+#" @param buffer numeric. buffer applied to vector file (in meters)
+#"
+#" @return bb_xycoords list. Coordinates (in pixels) of the upper/lower right/left corners of bounding box
+#" @export
+get_bb <- function(path_raster, path_vector = NULL, buffer = 0) {
+
+  if (!is.null(path_vector)) {
+    # get bounding box with a 50m buffer in order to allow for interpolation
+    bb_xycoords <- get_bb_from_vector(path_raster = path_raster,
+                                      path_vector = path_vector,
+                                      buffer = buffer)
+  }else if (is.null(path_vector)) {
+    bb_xycoords <- get_bb_from_fullimage(path_raster)
+  }
+  return(bb_xycoords)
+}
+
+#" This function gets extreme coordinates of a bounding box corresponding to a full image
+#"
+#" @param path_raster character. path for raster file
+#"
+#" @return bb_xycoords list. Coordinates (in pixels) of the upper/lower right/left corners of bounding box
+#" @importFrom raster raster
+#" @export
+get_bb_from_fullimage <- function(path_raster) {
+  # get raster coordinates corresponding to Full image
+  rasterobj <- raster::raster(path_raster)
+  bb_xycoords <- list()
+  bb_xycoords[["UL"]] <- data.frame("row" = 1, "col" = 1)
+  bb_xycoords[["UR"]] <- data.frame("row" = 1, "col" = dim(rasterobj)[2])
+  bb_xycoords[["LL"]] <- data.frame("row" = dim(rasterobj)[1], "col" = 1)
+  bb_xycoords[["LR"]] <- data.frame("row" = dim(rasterobj)[1], "col" = dim(rasterobj)[2])
+  return(bb_xycoords)
+}
+
+#" This gets bounding box corresponding to a vector from a raster (UL, UR, LL, LR corners)
+#"
+#" @param path_raster character. path for raster file
+#" @param path_vector character. path for vector file
+#" @param buffer numeric. buffer applied to vector file (in meters)
+#"
+#" @return bb_xycoords list. Coordinates (in pixels) of the upper/lower right/left corners of bounding box
+#" @importFrom sf st_read st_bbox st_crop
+#" @importFrom rgeos gbuffer bbox2SP
+#" @importFrom sp SpatialPoints bbox
+#" @importFrom raster projection extract extent raster
+#" @importFrom methods as
+#" @export
+get_bb_from_vector <- function(path_raster, path_vector, buffer = 0) {
+
+  data_raster <- raster::raster(path_raster)
+  # extract BB coordinates from vector
+  bb_vector <- rgeos::gbuffer(spgeom = as(sf::st_read(dsn = path_vector, quiet = TRUE), "Spatial"),
+                              width = buffer, byid = TRUE)
+  # extract BB coordinates from raster
+  bb_raster <- rgeos::bbox2SP(bbox = bbox(data_raster))
+  # compute intersection
+  intersect <- rgeos::gIntersection(bb_vector, bb_raster)
+  bbext <- raster::extent(intersect)
+  xmin <- bbext[1]
+  xmax <- bbext[2]
+  ymin <- bbext[3]
+  ymax <- bbext[4]
+  # get coordinates of bounding box corresponding to vector
+  corners <- list()
+  corners[["UR"]] <- sp::SpatialPoints(coords = cbind(xmax, ymax))
+  corners[["LR"]] <- sp::SpatialPoints(coords = cbind(xmax, ymin))
+  corners[["UL"]] <- sp::SpatialPoints(coords = cbind(xmin, ymax))
+  corners[["LL"]] <- sp::SpatialPoints(coords = cbind(xmin, ymin))
+  raster::projection(corners[["UL"]]) <- raster::projection(corners[["UR"]]) <-
+    raster::projection(corners[["LL"]]) <- raster::projection(corners[["LR"]]) <-
+    raster::projection(sf::st_read(dsn = path_vector, quiet = TRUE))
+  # get coordinates for corners of bounding box
+  bb_xycoords <- list()
+  for (corner in names(corners)) {
+    ex_df <- as.data.frame(raster::extract(data_raster, corners[[corner]], cellnumbers = TRUE))
+    colrow <- ind2sub(data_raster, ex_df$cell)
+    bb_xycoords[[corner]] <- data.frame("row" = colrow$row, "col" = colrow$col)
+  }
+  return(bb_xycoords)
+}
+
+#" get hdr name from image file name, assuming it is BIL format
+#"
+#" @param impath path of the image
+#"
+#" @return corresponding hdr
+#" @import tools
+#" @export
+get_hdr_name <- function(impath) {
+  if (tools::file_ext(impath) == "") {
+    impathhdr <- paste(impath, ".hdr", sep = "")
+  }else if (tools::file_ext(impath) == "bil") {
+    impathhdr <- gsub(".bil", ".hdr", impath)
+  }else if (tools::file_ext(impath) == "zip") {
+    impathhdr <- gsub(".zip", ".hdr", impath)
+  }else {
+    impathhdr <- paste(tools::file_path_sans_ext(impath), ".hdr", sep = "")
+  }
+
+  if (!file.exists(impathhdr)) {
+    message("WARNING : COULD NOT FIND hdr FILE")
+    print(impathhdr)
+    message("Process may stop")
+  }
+  return(impathhdr)
+}
+
+#" This function returns path for the spectral bands to be used
+#"
+#" @param path_dir_s2 character. Path for the directory containing S2 data. either L2A .SAFE S2 file or THEIA directory
+#" @param s2source character. defines if data comes from SciHub as SAFE directory, from THEIA or from LaSRC
+#" @param resolution numeric. spatial resolution of the final image: 10m or 20m
+#" @param fre_sre character. SRE or FRE products from THEIA
+#"
+#" @return listbands list. contains path for spectral bands corresponding to 10m and 20m resolution
+#" @export
+get_s2_bands <- function(path_dir_s2, s2source = "SAFE", resolution = 10, fre_sre = "FRE") {
+
+  if (s2source == "SAFE" || s2source == "Sen2Cor") {
+    listbands <- get_s2_bands_from_sen2cor(path_dir_s2 = path_dir_s2, resolution = resolution)
+  }else if (s2source == "THEIA") {
+    listbands <- get_s2_bands_from_theia(path_dir_s2 = path_dir_s2, resolution = resolution,
+                                         fre_sre = fre_sre)
+  }else if (s2source == "LaSRC") {
+    listbands <- get_s2_bands_from_lasrc(path_dir_s2 = path_dir_s2, resolution = resolution)
+  }else {
+    message("The data source (Atmospheric correction) for Sentinel-2 image is unknown")
+    message("Please provide S2 images from one of the following data sources:")
+    message("- LaSRC (atmospheric correction: LaSRC)")
+    message("- THEIA (atmospheric correction: MAJA)")
+    message("- SAFE (atmospheric correction: Sen2Cor)")
+    s2bands_10m <- s2bands_20m <- granule <- mtdfile <- metadata_msi <- metadata_lasrc <- NULL
+    listbands <- list("s2bands_10m" = s2bands_10m, "s2bands_20m" = s2bands_20m, "GRANULE" = granule,
+                      "metadata" = mtdfile, "metadata_MSI" = metadata_msi,
+                      "metadata_lasrc" = metadata_lasrc)
+  }
+  return(listbands)
+}
+
+#" This function returns path for the spectral bands in SAFE / sen2Cor directory
+#"
+#" @param path_dir_s2 character. Path for the SAFE directory containing S2 data
+#" @param resolution numeric. spatial resolution of the final image: 10m or 20m
+#"
+#" @return listbands list. contains path for spectral bands corresponding to 10m and 20m resolution, as well name of as granule
+#" @export
+get_s2_bands_from_sen2cor <- function(path_dir_s2, resolution = 10) {
+  # build path for all bands
+  if (resolution == 10) {
+    b10m <- c("B02", "B03", "B04", "B08")
+    b20m <- c("B05", "B06", "B07", "B8A", "B11", "B12")
+  }else {
+    b10m <- c()
+    b20m <- c("B02", "B03", "B04", "B05", "B06", "B07", "B08", "B8A", "B11", "B12")
+  }
+  # get granule directory & path for corresponding metadata XML file
+  granule <- list.dirs(list.dirs(path_dir_s2, recursive = FALSE)[grep(pattern = "GRANULE",
+                                                                     x = list.dirs(path_dir_s2, recursive = FALSE))], recursive = FALSE)
+  mtdfile <- file.path(granule, "MTD_TL.xml")
+  if (file.exists(file.path(path_dir_s2, "MTD_MSIL2A.xml"))) {
+    mtd_msi_file <- file.path(path_dir_s2, "MTD_MSIL2A.xml")
+  } else {
+    mtd_msi_file <- NULL
+  }
+
+  # Define path for bands
+  s2bands_20m_dir <- file.path(granule, "IMG_DATA", "R20m")
+  s2bands_10m_dir <- file.path(granule, "IMG_DATA", "R10m")
+  s2bands_10m <- s2bands_20m <- list()
+  for (band in b20m) {
+    s2bands_20m[[band]] <- file.path(s2bands_20m_dir, list.files(s2bands_20m_dir, pattern = band))
+  }
+  for (band in b10m) {
+    s2bands_10m[[band]] <- file.path(s2bands_10m_dir, list.files(s2bands_10m_dir, pattern = band))
+  }
+  # get cloud mask
+  cloud <- "MSK_CLDPRB_20m"
+  cloud_20m_dir <- file.path(granule, "QI_DATA")
+  s2bands_20m[["Cloud"]] <- file.path(cloud_20m_dir, list.files(cloud_20m_dir, pattern = cloud))
+  listbands <- list("s2bands_10m" = s2bands_10m,
+                    "s2bands_20m" = s2bands_20m,
+                    "GRANULE" = granule,
+                    "metadata" = mtdfile,
+                    "metadata_MSI" = mtd_msi_file,
+                    "metadata_lasrc" = NULL)
+  return(listbands)
+}
+
+#" This function returns path for the spectral bands in LaSRC directory
+#"
+#" @param path_dir_s2 character. Path for the SAFE directory containing S2 data
+#" @param resolution numeric. spatial resolution of the final image: 10m or 20m
+#"
+#" @return listbands list. contains path for spectral bands corresponding to 10m and 20m resolution, as well name of as granule
+#" @importFrom stringr str_subset
+#" @export
+get_s2_bands_from_lasrc <- function(path_dir_s2, resolution = 10) {
+
+  # get granule directory & path for corresponding metadata XML file
+  granule <- path_dir_s2
+  mtdfile <- file.path(granule, "MTD_TL.xml")
+  if (file.exists(file.path(path_dir_s2, "MTD_MSIL1C.xml"))) {
+    mtd_msi_file <- file.path(path_dir_s2, "MTD_MSIL1C.xml")
+  } else {
+    mtd_msi_file <- NULL
+  }
+
+  # build path for all bands
+  b10m <- c("band2", "band3", "band4", "band5", "band6", "band7", "band8", "band8a", "band11", "band12")
+  b10m_standard <- c("B02", "B03", "B04", "B05", "B06", "B07", "B08", "B8A", "B11", "B12")
+  # Define path for bands
+  s2bands_10m <- s2bands_20m <- list()
+  for (i in 1:seq_along(b10m)) {
+    s2bands_10m[[b10m_standard[i]]] <- file.path(path_dir_s2,
+                                                 list.files(path_dir_s2,
+                                                            pattern = paste(b10m[i], ".tif", sep = "")))
+  }
+
+  # get metadata file containing offset
+  mtd_lasrc <- str_subset(list.files(path_dir_s2, pattern = "S2"), ".xml$")
+  if (file.exists(file.path(path_dir_s2, mtd_lasrc))) {
+    metadata_lasrc <- file.path(path_dir_s2, mtd_lasrc)
+  } else {
+    metadata_lasrc <- NULL
+  }
+  # get cloud mask
+  cloud <- "CLM"
+  s2bands_10m[["Cloud"]] <- file.path(path_dir_s2, list.files(path_dir_s2, pattern = cloud))
+  listbands <- list("s2bands_10m" = s2bands_10m,
+                    "s2bands_20m" = s2bands_20m,
+                    "GRANULE" = granule,
+                    "metadata" = mtdfile,
+                    "metadata_MSI" = mtd_msi_file,
+                    "metadata_lasrc" = metadata_lasrc)
+  return(listbands)
+}
+
+#" This function returns path for the spectral bands in THEIA directory
+#"
+#" @param path_dir_s2 character. Path for the SAFE directory containing S2 data
+#" @param resolution numeric. spatial resolution of the final image: 10m or 20m
+#" @param fre_sre character. SRE or FRE products from THEIA
+#"
+#" @return listbands list. contains path for spectral bands corresponding to 10m and 20m resolution, as well name of as granule
+#" @export
+get_s2_bands_from_theia <- function(path_dir_s2, resolution = 10, fre_sre = "FRE") {
+
+  # build path for all bands
+  if (resolution == 10) {
+    b10m <- c("B02", "B03", "B04", "B08")
+    b20m <- c("B05", "B06", "B07", "B8A", "B11", "B12")
+  } else {
+    b10m <- c()
+    b20m <- c("B02", "B03", "B04", "B05", "B06", "B07", "B08", "B8A", "B11", "B12")
+  }
+
+  # get path_tile_s2 directory & path for corresponding metadata XML file
+  path_tile_s2 <- list.dirs(path_dir_s2, recursive = FALSE)
+  files_tile_s2 <- list.files(path_tile_s2, recursive = FALSE)
+  mtdfile <- file.path(path_tile_s2, files_tile_s2[grep(pattern = "MTD_ALL.xml", x = files_tile_s2)])
+
+  # Define path for bands
+  s2bands_10m_dir <- s2bands_20m_dir <- path_tile_s2
+  s2bands_10m <- s2bands_20m <- list()
+  for (band in b20m) {
+    band_20m_pattern <- paste0(gsub("0", "", band), ".tif") # for THEAI band 2 is "B2" ("B02" for SAFE)
+    list_files_20m <- list.files(s2bands_20m_dir, pattern = band_20m_pattern)
+    s2bands_20m[[band]] <- file.path(s2bands_20m_dir, list_files_20m)[grep(pattern = fre_sre,
+                                                                           x = file.path(s2bands_20m_dir, list_files_20m))]
+  }
+  for (band in b10m) {
+    band_10m_pattern <- paste0(gsub("0", "", band), ".tif") # for THEAI band 2 is "B2" ("B02" for SAFE)
+    list_files_10m <- list.files(s2bands_10m_dir, pattern = band_10m_pattern)
+    s2bands_10m[[band]] <- file.path(s2bands_10m_dir, list_files_10m)[grep(pattern = fre_sre,
+                                                                           x = file.path(s2bands_10m_dir, list_files_10m))]
+  }
+
+  # get cloud mask 10m
+  cloud_10m <- "CLM_R1"
+  cloud_10m_dir <- file.path(path_tile_s2, "MASKS")
+  s2bands_10m[["Cloud"]] <- file.path(cloud_10m_dir, list.files(cloud_10m_dir, pattern = cloud_10m))
+
+  # get cloud mask 20m
+  cloud_20m <- "CLM_R2"
+  cloud_20m_dir <- file.path(path_tile_s2, "MASKS")
+  s2bands_20m[["Cloud"]] <- file.path(cloud_20m_dir, list.files(cloud_20m_dir, pattern = cloud_20m))
+
+  # return list bands
+  listbands <- list("s2bands_10m" = s2bands_10m,
+                    "s2bands_20m" = s2bands_20m,
+                    "path_tile_s2" = path_tile_s2,
+                    "metadata" = mtdfile)
+  return(listbands)
+}
+
+#" This function check S2 data level:
+#" - L2A: already atmospherically corrected
+#" - L1C: requires atmospheric corrections with sen2cor
+#"
+#" @param prodname character. original name for the S2 image
+#"
+#" @return s2level character. S2 level: L1C or L2A
+#" @export
+get_s2_level <- function(prodname) {
+  prodname <- basename(prodname)
+  if (length(grep(pattern = "L1C_", x = prodname)) == 1) {
+    s2level <- "L1C"
+  } else if (length(grep(pattern = "L2A_", x = prodname)) == 1) {
+    s2level <- "L2A"
+  }
+  return(s2level)
+}
+
+#" This function gets tile from S2 image
+#"
+#" @param prodname character. original name for the S2 image
+#"
+#" @return tilename character
+#" @importFrom tools file_path_sans_ext
+#" @export
+get_tile <- function(prodname) {
+  prodname <- basename(prodname)
+  tilename <- tools::file_path_sans_ext(gsub("_.*", "", gsub(".*_T", "", prodname)))
+  return(tilename)
+}
+
+#" This function gets acquisition date from S2 image
+#"
+#" @param prodname character. original name for the S2 image
+#"
+#" @return dateacq character
+#" @export
+get_date <- function(prodname) {
+  prodname <- basename(prodname)
+  dateacq <- as.Date(gsub("T.*", "", gsub(".*_20", "20", prodname)), format = "%Y%m%d")
+  return(dateacq)
+}
+
+#" download S2 L1C data from Copernicus hub or Google cloud
+#"
+#" @param list_safe safe object. produced with sen2r::s2_list
+#" @param l1c_path character. path for storage of L1C image
+#" @param path_vector path for a vector file
+#" @param time_interval dates. time interval for S2 query
+#" @param googlecloud boolean. set to TRUE if google cloud SDK is installed and
+#" @param forcegoogle boolean. set to TRUE if only google requested
+#" sen2r configured as an alternative hub for S2 download
+#"
+#" @return prodname character. S2 Product name
+#" @importFrom sen2r safe_is_online s2_list s2_download s2_order check_gcloud
+#" @export
+get_s2_l1c_image <- function(list_safe, l1c_path, path_vector, time_interval,
+                             googlecloud = FALSE, forcegoogle = FALSE) {
+  # Check if available from Copernicus hub first
+  copernicus_avail <- sen2r::safe_is_online(list_safe)
+  # if available: download
+  prodname <- attr(list_safe, which = "name")
+  if (file.exists(file.path(l1c_path, prodname))) {
+    message("L1C file already downloaded")
+    message(file.path(l1c_path, prodname))
+  } else {
+    if (copernicus_avail == TRUE && forcegoogle == FALSE) {
+      sen2r::s2_download(list_safe, outdir = l1c_path)
+    } else if (copernicus_avail == FALSE || forcegoogle == TRUE) {
+      # if not available and googlecloud==TRUE
+      if (googlecloud == TRUE) {
+        # check if google cloud SDK available from this computer
+        ggc <- sen2r::check_gcloud()
+        if (ggc == TRUE) {
+          message("downloading from Google cloud")
+          list_safe_ggc <- sen2r::s2_list(spatial_extent = sf::st_read(dsn = path_vector),
+                                          time_interval = time_interval,
+                                          server = "gcloud")
+          prodname <- attr(list_safe_ggc, which = "name")
+          if (file.exists(file.path(l1c_path, prodname))) {
+            message("L1C file already downloaded")
+            message(file.path(l1c_path, prodname))
+          } else {
+            sen2r::s2_download(list_safe_ggc, outdir = l1c_path)
+            # check if QI_DATA exists in DATASTRIP, and create it if not the case
+            datastrip_path <- file.path(l1c_path, prodname, "DATASTRIP")
+            dsdir <- list.dirs(datastrip_path, recursive = FALSE)
+            if (length(match(list.dirs(dsdir, recursive = FALSE, full.names = FALSE), "QI_DATA")) == 0) {
+              dir.create(file.path(dsdir, "QI_DATA"))
+            }
+          }
+        } else if (ggc == FALSE) {
+          message("googlecloud set to TRUE but missing")
+          message("Please install Google cloud SDK")
+          message("https://cloud.google.com/sdk/docs/install")
+          message("and/or set configuration of sen2r following instructions")
+          message("https://www.r-bloggers.com/2021/06/downloading-sentinel-2-archives-from-google-cloud-with-sen2r/")
+        }
+      }
+    }
+    if (copernicus_avail == FALSE && googlecloud == FALSE) {
+      message("S2 image in Long Term Archive (LTA)")
+      message("Ordering image from  LTA")
+      message("This may take 1 day, please run your script later")
+      orders2 <- sen2r::s2_order(list_safe)
+      message("An alternative is possible with Google cloud SDK")
+      message("https://cloud.google.com/sdk/docs/install")
+      message("and/or set configuration of sen2r following instructions")
+      message("https://www.r-bloggers.com/2021/06/downloading-sentinel-2-archives-from-google-cloud-with-sen2r/")
+    }
+  }
+  return(prodname)
+}
+
+#" download S2 L2A data from Copernicus hub or convert L1C to L2A
+#"
+#" @param l2a_path character. path for storage of L2A image
+#" @param spatial_extent path for a vector file
+#" @param dateacq character. date of acquisition
+#" @param deletel1c Boolean. set TRUE to delete L1C images
+#" @param Sen2Cor Boolean. set TRUE to automatically perform atmospheric corrections using sen2Cor
+#" @param googlecloud boolean. set to TRUE if google cloud SDK is installed and
+#" sen2r configured as an alternative hub for S2 download
+#"
+#" @return pathl2a character. Path for L2A image
+#" @importFrom sen2r s2_list s2_download
+#" @importFrom R.utils getAbsolutePath
+
+#" @export
+get_s2_l2a_image <- function(l2a_path, spatial_extent, dateacq,
+                             deletel1c = FALSE, sen2cor = TRUE,
+                             googlecloud = FALSE) {
+
+  # Needs to be updated: define path for L1c data
+  l1c_path <- l2a_path
+  # define time interval
+  time_interval <- as.Date(c(dateacq, dateacq))
+  # get list S2 products corresponding to study area and date of interest using sen2r package
+  if (googlecloud == TRUE) {
+    server <- c("scihub", "gcloud")
+  } else if (googlecloud == FALSE) {
+    server <- "scihub"
+  }
+  list_safe <- sen2r::s2_list(spatial_extent = sf::st_read(dsn = spatial_extent),
+                              time_interval = time_interval,
+                              server = server, availability = "check")
+  # download products
+  sen2r::s2_download(list_safe, outdir = l2a_path)
+  # name all products
+  prodname <- attr(list_safe, which = "name")
+  prodfullpath <- file.path(l2a_path, prodname)
+  if (sen2cor == TRUE) {
+    for (imgname in prodname) {
+      s2level <- get_s2_level(imgname)
+      if (s2level == "L1C") {
+        datepattern <- gsub(pattern = "-", replacement = "", x = dateacq)
+        pathl2a <- s2_from_l1c_to_l2a(prodname = imgname, l1c_path = l2a_path, l2a_path = l2a_path,
+                                      datepattern = datepattern, tmp_path = NULL)
+        if (deletel1c == TRUE) {
+          unlink(x = R.utils::getAbsolutePath(file.path(l1c_path, prodname)),
+                 recursive = TRUE, force = TRUE)
+          # delete from full path and add atmospherically corrected
+          whichimg <- grep(x = prodfullpath, pattern = imgname)
+          dateacq <- get_date(imgname)
+          tilename <- get_tile(imgname)
+          pathl2a <- list.files(path = l2a_path, pattern = tilename, full.names = TRUE)
+          pathl2a <- pathl2a[grep(x = pathl2a, pattern = dateacq)]
+          pathl2a <- pathl2a[grep(x = basename(pathl2a), pattern = "L2A")]
+          prodfullpath[whichimg] <- pathl2a
+        }
+      }
+    }
+  }
+
+  return(prodfullpath)
+}
+
+#" convert image coordinates from index to X-Y
+#"
+#" @param Raster image raster object
+#" @param image_index coordinates corresponding to the raster
+ind2sub <- function(data_raster, image_index) {
+  c <- ((image_index - 1) %% data_raster@ncols) + 1
+  r <- floor((image_index - 1) / data_raster@ncols) + 1
+  my_list <- list("col" = c, "row" = r)
+  return(my_list)
+}
+
+#" mosaicing a set of rasters
+#"
+#" @param list_rasters character. list of paths corresponding to rasters to mosaic
+#" @param dst_mosaic character. path and name of mosaic produced
+#" @param stretch boolean. Set TRUE to get 10% stretching at display for reflectance, mentioned in hdr only
+#"
+#" @return None
+#" @importFrom gdalUtils mosaic_rasters
+#" @importFrom raster hdr raster
+#" @export
+mosaic_rasters <- function(list_rasters, dst_mosaic, stretch = FALSE) {
+
+  # produce mosaic
+  gdalUtils::mosaic_rasters(gdalfile = list_rasters, dst_dataset = dst_mosaic,
+                            separate = FALSE, of = "Ehdr", verbose = TRUE)
+
+  # convert hdr to ENVI format
+  raster::hdr(raster(dst_mosaic), format = "ENVI")
+  # add info to hdr based on initial rasters
+  hdr_init <- read_envi_header(get_hdr_name(list_rasters[1]))
+  hdr <- read_envi_header(get_hdr_name(dst_mosaic))
+  hdr$`band names` <- hdr_init$`band names`
+  hdr$wavelength <- hdr_init$wavelength
+  if (stretch == TRUE) {
+    hdr$`default stretch` <- "0.000000 1000.000000 linear"
+  }
+  hdr$`z plot range` <- NULL
+  hdr$`data ignore value` <- "-Inf"
+  hdr$`sensor type` <- hdr_init$`sensor type`
+  hdr$`coordinate system string` <- read.table(paste(file_path_sans_ext(dst_mosaic), ".prj", sep = ""))
+  write_envi_header(hdr = hdr, hdrpath = get_hdr_name(dst_mosaic))
+  return(invisible())
+}
+
+#" Reads ENVI hdr file
+#"
+#" @param hdrpath Path of the hdr file
+#"
+#" @return list of the content of the hdr file
+#" @export
+read_envi_header <- function(hdrpath) {
+  if (!grepl(".hdr$", hdrpath)) {
+    stop("File extension should be .hdr")
+  }
+  hdr <- readLines(hdrpath)
+  ## check ENVI at beginning of file
+  if (!grepl("ENVI", hdr[1])) {
+    stop("Not an ENVI header (ENVI keyword missing)")
+  } else {
+    hdr <- hdr [-1]
+  }
+  ## remove curly braces and put multi-line key-value-pairs into one line
+  hdr <- gsub("\\{([^}]*)\\}", "\\1", hdr)
+  l <- grep("\\{", hdr)
+  r <- grep("\\}", hdr)
+
+  if (length(l) != length(r)) {
+    stop("Error matching curly braces in header (differing numbers).")
+  }
+
+  if (any(r <= l)) {
+    stop("Mismatch of curly braces in header.")
+  }
+
+  hdr[l] <- sub("\\{", "", hdr[l])
+  hdr[r] <- sub("\\}", "", hdr[r])
+
+  for (i in rev(seq_along(l))) {
+    hdr <- c(
+      hdr [seq_len(l [i] - 1)],
+      paste(hdr [l [i]:r [i]], collapse = "\n"),
+      hdr [-seq_len(r [i])]
+    )
+  }
+
+  ## split key = value constructs into list with keys as names
+  hdr <- sapply(hdr, split_line, "=", USE.NAMES = FALSE)
+  names(hdr) <- tolower(names(hdr))
+
+  ## process numeric values
+  tmp <- names(hdr) %in% c(
+    "samples", "lines", "bands", "header offset", "data type",
+    "byte order", "default bands", "data ignore value",
+    "wavelength", "fwhm", "data gain values"
+  )
+  hdr [tmp] <- lapply(hdr [tmp], function(x) {
+    as.numeric(unlist(strsplit(x, ", ")))
+  })
+
+  return(hdr)
+}
+
+#" This function reads a list of files corresponding to S2 bands
+#" S2 bands are expected to have uniform spatial resolution and footprint
+#" @param s2_bands list. list of S2 bands obtained from get_s2_bands
+#" @param path_vector path for a vector file
+#" @param resampling numeric. resampling factor (default = 1, set to resampling = 2 to convert 20m into 10m resolution)
+#" @param interpolation character. method for resampling. default = "bilinear"
+#"
+#" @return stack_s2 list. contains stack of S2 bands
+#"
+#" @importFrom stars read_stars
+#" @importFrom sf st_bbox st_read st_crop
+#" @export
+
+read_s2bands <- function(s2_bands, path_vector = NULL,
+                         resampling = 1, interpolation = "bilinear") {
+  # get bounding box corresponding to footprint of image or image subset
+  bb_xycoords <- get_bb(path_raster = s2_bands[[1]],
+                        path_vector = path_vector, buffer = 50)
+
+  # prepare reading data for extent defined by bounding box
+  nxoff <- bb_xycoords$UL$col
+  nyoff <- bb_xycoords$UL$row
+  nxsize <- bb_xycoords$UR$col - bb_xycoords$UL$col + 1
+  nysize <- bb_xycoords$LR$row - bb_xycoords$UR$row + 1
+  nbufxsize <- resampling * nxsize
+  nbufysize <- resampling * nysize
+  if (resampling == 1) {
+    interpolation <- "nearest_neighbour"
+  }
+  # write interpolated individual bands in temp directory
+  tmpdir <- tempdir()
+  tmpfile <- list()
+  for (band in names(s2_bands)) {
+    stack_s2_tmp <- stars::read_stars(s2_bands[[band]], along = "band",
+                                      RasterIO = list(nXOff = nxoff, nYOff = nyoff,
+                                                      nXSize = nxsize, nYSize = nysize,
+                                                      nBufXSize = nbufxsize, nBufYSize = nbufysize,
+                                                      resample = interpolation), proxy = FALSE)
+    if (!is.null(path_vector)) {
+      stack_s2_tmp <- sf::st_crop(x = stack_s2_tmp, y = st_bbox(st_read(dsn = path_vector, quiet = TRUE)))
+    }
+    tmpfile[[band]] <- file.path(tmpdir, tools::file_path_sans_ext(basename(s2_bands[[band]])))
+    if (band == "Cloud") {
+      stars::write_stars(obj = stack_s2_tmp, dsn = tmpfile[[band]],
+                         driver =  "ENVI", type = "Byte", overwrite = TRUE)
+    } else {
+      stars::write_stars(obj = stack_s2_tmp, dsn = tmpfile[[band]],
+                         driver =  "ENVI", type = "Int16", overwrite = TRUE)
+    }
+    gc()
+  }
+
+  stack_s2 <- stars::read_stars(tmpfile, along = "band", proxy = TRUE)
+  return(stack_s2)
+}
+
+#" This function reads a raster stack, and gets footprint as pixel coordinates or vector file as input
+#" @param path_raster character. path for raster file
+#" @param path_vector character. path for vector file
+#" @param bbpix list. coordinates of pixels corresponding to a bounding box
+#"
+#" @return starsobj stars object corresponding to raster or raster subset
+#"
+#" @importFrom stars read_stars
+#" @importFrom sf st_bbox st_read st_crop
+#" @export
+read_raster <- function(path_raster, path_vector = NULL, bbpix = NULL) {
+  # get bounding box corresponding to footprint of image or image subset
+  if (is.null(bbpix)) {
+    bb_xycoords <- get_bb(path_raster = path_raster,
+                          path_vector = path_vector, buffer = 0)
+  } else {
+    bb_xycoords <- bbpix
+  }
+  # prepare reading data for extent defined by bounding box
+  nxoff <- bb_xycoords$UL$col
+  nyoff <- bb_xycoords$UL$row
+  nxsize <- bb_xycoords$UR$col - bb_xycoords$UL$col + 1
+  nysize <- bb_xycoords$LR$row - bb_xycoords$UR$row + 1
+  nbufxsize <- nxsize
+  nbufysize <- nysize
+  starsobj <- stars::read_stars(path_raster, along = "band",
+                                RasterIO = list(nXOff = nxoff, nYOff = nyoff,
+                                                  nXSize = nxsize, nYSize = nysize,
+                                                  nBufXSize = nbufxsize, nBufYSize = nbufysize),
+                                proxy = FALSE)
+  return(starsobj)
+}
+
+#" This function reprojects a shapefile and saves reprojected shapefile
+#"
+#" @param path_vector_init character. path for a shapefile to be reprojected
+#" @param newprojection character. projection to be applied to path_vector_init
+#" @param path_vector_reproj character. path for the reprojected shapefile
+#"
+#" @return path_vector character. path of the shapefile
+#" - path_vector_init if the vector did not need reprojection
+#" - path_vector_reproj if the vector needed reprojection
+#"
+#" @importFrom rgdal readOGR writeOGR
+#" @importFrom sp spTransform
+#" @importFrom raster projection
+#" @export
+reproject_shp <- function(path_vector_init, newprojection, path_vector_reproj) {
+
+  dir_vector_init <- dirname(path_vector_init)
+  # shapefile extension
+  fileext <- file_ext(basename(path_vector_init))
+  if (fileext == "shp") {
+    name_vector_init <- file_path_sans_ext(basename(path_vector_init))
+    vector_init_ogr <- rgdal::readOGR(dir_vector_init, name_vector_init, verbose = FALSE)
+  } else if (fileext == "kml") {
+    vector_init_ogr <- rgdal::readOGR(path_vector_init, verbose = FALSE)
+  }
+  vector_init_proj <- raster::projection(vector_init_ogr)
+
+  if (!vector_init_proj == newprojection) {
+    dir_vector_reproj <- dirname(path_vector_reproj)
+    name_vector_reproj <- file_path_sans_ext(basename(path_vector_reproj))
+    vector_reproj <- sp::spTransform(vector_init_ogr, newprojection)
+    rgdal::writeOGR(obj = vector_reproj, dsn = dir_vector_reproj, layer = name_vector_reproj,
+                    driver = "ESRI Shapefile", overwrite_layer = TRUE)
+    path_vector <- path_vector_reproj
+  } else {
+    path_vector <- path_vector_init
+  }
+  return(path_vector)
+}
+
+
+#" perform atmospheric corrections to convert L1C to L2A data with Sen2cor
+#"
+#" @param prodname character. produced with sen2r::s2_list
+#" @param l1c_path character. path of directory where L1C image is stored
+#" @param l2a_path character. path of directory where L2A image is stored
+#" @param datepattern character. pattern corresponding to date of acquisition to identify L2A directory
+#" @param tmp_path character. path of temporary directory where L2A image is stored
+#" sen2r configured as an alternative hub for S2 download
+#"
+#" @return pathl2a character. S2 Product name
+#" @importFrom sen2r safe_is_online s2_list s2_download s2_order
+#" @importFrom R.utils getAbsolutePath
+#"
+#" @export
+s2_from_l1c_to_l2a <- function(prodname, l1c_path, l2a_path, datepattern, tmp_path = NULL) {
+
+  # define path for tmp directory
+  if (is.null(tmp_path)) {
+    tmp_path <- tempdir(check = TRUE)
+  }
+  tmp_prodlist <- prodname
+  # perform Sen2Cor atmospheric corrections
+  binpath <- sen2r::load_binpaths()
+  # 2- open a command prompt and directly run sen2cor with following command line
+  cmd <- paste(binpath$sen2cor,
+               "--output_dir", R.utils::getAbsolutePath(l2a_path),
+               R.utils::getAbsolutePath(file.path(l1c_path, prodname)), sep = " ")
+  system(cmd)
+  pathl2a <- list.files(path = l2a_path, pattern = datepattern, full.names = TRUE)
+
+  return(pathl2a)
+}
+
+#" This function saves cloud masks.
+#" "cloudMask_Binary" is default binary mask with 0 where clouds are detected and 1 for clean pixels
+#" "cloudMask_RAW" is the original cloud layer produced by atmospheric correction algorithm
+#" --> may be useful to refine cloud mask
+#"
+#" @param s2_stars list. stars object containing raster data. Can be produced with function extract_from_s2_l2a
+#" @param cloud_path character.
+#" @param s2source character.
+#" @param footprint character. path for vector file defining footprint of interest in the image
+#" @param saveraw boolean. should the original cloud mask layer be saved?
+#" @param maxchunk numeric. Size of individual chunks to be written (in Mb)
+#"
+#" @return list of cloudmasks (binary mask, and raw mask if required)
+#" @importFrom sf st_read
+#" @importFrom stars write_stars
+#" @importFrom raster raster
+#" @export
+save_cloud_s2 <- function(s2_stars, cloud_path, s2source = "SAFE",
+                          footprint = NULL, saveraw = FALSE, maxchunk = 256) {
+
+  whichcloud <- which(names(s2_stars$attr) == "Cloud")
+  # Save cloud mask
+  if (saveraw == TRUE) {
+    cloudraw <- file.path(cloud_path, "CloudMask_RAW")
+    obj <- stars::read_stars(s2_stars$attr[whichcloud], proxy = TRUE)
+    sizeobj <- dim(obj)[1] * dim(obj)[2] / (1024**2)
+    nbchunks <- ceiling(sizeobj / maxchunk)
+    stars::write_stars(obj,
+                       dsn = cloudraw,
+                       driver =  "ENVI",
+                       type = "Byte",
+                       chunk_size = c(dim(obj)[1], dim(obj)[2] / nbchunks),
+                       progress = TRUE)
+  } else {
+    cloudraw <- NULL
+  }
+  # Save cloud mask as in biodivMapR (0 = clouds, 1 = pixel ok)
+  cloudmask <- stars::read_stars(s2_stars$attr[whichcloud], proxy = FALSE)
+  if (s2source == "SAFE" || s2source == "THEIA") {
+    cloudy <- which(cloudmask[[1]] > 0)
+    sunny <- which(cloudmask[[1]] == 0)
+  } else if (s2source == "LaSRC") {
+    cloudy <- which(is.na(cloudmask[[1]]))
+    sunny <- which(cloudmask[[1]] == 1)
+  }
+
+  cloudmask[[1]][cloudy] <- 0
+  cloudmask[[1]][sunny] <- 1
+  cloudbin <- file.path(cloud_path, "CloudMask_Binary")
+  stars::write_stars(cloudmask, dsn = cloudbin, driver =  "ENVI", type = "Byte", overwrite = TRUE)
+  cloudmasks <- list("BinaryMask" = cloudbin, "RawMask" = cloudraw)
+  # delete temporary file
+  file.remove(s2_stars$attr[whichcloud])
+  if (file.exists(paste(s2_stars$attr[whichcloud], ".hdr", sep = ""))) file.remove(paste(s2_stars$attr[whichcloud], ".hdr", sep = ""))
+  gc()
+  return(cloudmasks)
+}
+
+#" This function saves reflectance files
+#"
+#" @param s2_stars list. stars object containing raster data. Can be produced with function extract_from_s2_l2a
+#" @param refl_path character. path for reflectance file to be stored
+#" @param format character. file format for reflectance data
+#" @param datatype character. data type (integer, float, 16bits, 32bits...)
+#" @param s2sat character. Sentinel-2 mission ("2A" or "2B")
+#" @param tile_s2 character. S2 tile name (2 numbers + 3 letters)
+#" @param dateacq_s2 double. date of acquisition
+#" @param MTD character. path for metadata file
+#" @param MTD_MSI character. path for metadata MSI file
+#" @param mtd_lasrc character. path for metadata LaSRC file
+#" @param maxchunk numeric. Size of individual chunks to be written (in Mb)
+#"
+#" @return None
+#" @importFrom stars write_stars st_apply
+#" @importFrom XML xml
+#" @export
+save_reflectance_s2 <- function(s2_stars, refl_path, format = "ENVI", datatype = "Int16",
+                                s2sat = NULL, tile_s2 = NULL, dateacq_s2 = NULL,
+                                mtd = NULL, mtd_msi = NULL, mtd_lasrc = NULL,
+                                maxchunk = 256) {
+  # identify if S2A or S2B, if possible
+  s2mission <- check_s2mission(s2sat = s2sat, tile_s2 = tile_s2, dateacq_s2 = dateacq_s2)
+
+  # define central wavelength corresponding to each spectral band
+  if (s2mission == "2A") {
+    wl_s2 <- list("B02" = 496.6, "B03" = 560.0, "B04" = 664.5,
+                  "B05" = 703.9, "B06" = 740.2, "B07" = 782.5, "B08" = 835.1,
+                  "B8A" = 864.8, "B11" = 1613.7, "B12" = 2202.4)
+  } else if (s2mission == "2B") {
+    wl_s2 <- list("B02" = 492.1, "B03" = 559.0, "B04" = 665.0,
+                  "B05" = 703.8, "B06" = 739.1, "B07" = 779.7, "B08" = 833.0,
+                  "B8A" = 864.0, "B11" = 1610.4, "B12" = 2185.7)
+  }
+  if (s2mission == "2A") {
+    sensor <- "Sentinel_2A"
+  } else if (s2mission == "2B") {
+    sensor <- "Sentinel_2B"
+  }
+
+  # apply offset when necessary
+  listbands_bis <- c("B2", "B3", "B4", "B5", "B6", "B7", "B8", "B8A", "B11", "B12")
+  if (!is.null(mtd_msi) && is.null(mtd_lasrc)) {
+    # read XML file containing info about geometry of acquisition
+    s2xml <- XML::xmlToList(mtd_msi)
+    xml_offset <- s2xml$General_Info$Product_Image_Characteristics$BOA_ADD_offset_VALUES_LIST
+    bands <- lapply(s2xml$General_Info$Product_Image_Characteristics$Spectral_Information_List, "[[", 4)
+    if (!is.null(xml_offset) && !is.null(bands)) {
+      bandid  <- lapply(bands, "[[", 1)
+      bandname  <- lapply(bands, "[[", 2)
+      offset <- data.frame("bandname" = unlist(bandname),
+                           "bandid" = unlist(bandid),
+                           "offset" = unlist(lapply(xml_offset, "[[", 1)))
+      selbands <- match(listbands_bis, offset$bandname)
+      offset <- offset[selbands, ]
+      boa_quantval <- as.numeric(s2xml$General_Info$Product_Image_Characteristics$QUANTIFICATION_VALUES_LIST$BOA_QUANTIFICATION_VALUE[1])
+    } else {
+      offset <- data.frame("bandname" = listbands_bis,
+                           "bandid" = c(1, 2, 3, 4, 5, 6, 7, 8, 11, 12),
+                           "offset" = 0)
+      boa_quantval <- 10000
+    }
+  } else if (!is.null(mtd_lasrc)) {
+    # read XML file containing info about geometry of acquisition
+    s2xml <- XML::xmlToList(mtd_lasrc)
+    attributes_lasrc <- s2xml$bands[[14]]$.attrs
+    attributes_lasrc_df <- data.frame(attributes_lasrc)
+    if (match("add_offset", rownames(attributes_lasrc_df)) > 0 && match("scale_factor", rownames(attributes_lasrc_df)) > 0) {
+      xml_offset <- as.numeric(attributes_lasrc[["add_offset"]])
+      boa_quantval <- 1 / as.numeric(attributes_lasrc[["scale_factor"]])
+      offset <- data.frame("bandname" = listbands_bis,
+                           "bandid" = c(1, 2, 3, 4, 5, 6, 7, 8, 11, 12),
+                           "offset" = xml_offset)
+    } else {
+      offset <- data.frame("bandname" = listbands_bis,
+                           "bandid" = c(1, 2, 3, 4, 5, 6, 7, 8, 11, 12),
+                           "offset" = 0)
+      boa_quantval <- 10000
+    }
+  } else {
+    offset <- data.frame("bandname" = listbands_bis,
+                         "bandid" = c(1, 2, 3, 4, 5, 6, 7, 8, 11, 12),
+                         "offset" = 0)
+    boa_quantval <- 10000
+  }
+
+  # identify where spectral bands are in the stars object
+  stars_spectral <- list()
+  starsnames <- names(s2_stars$attr)
+  stars_spectral$bandname <- starsnames[which(!starsnames == "Cloud")]
+  stars_spectral$wavelength <- wl_s2[stars_spectral$bandname]
+
+  sortedwl <- names(wl_s2)
+  reorder <- match(sortedwl, stars_spectral$bandname)
+  elim <- which(is.na(reorder))
+  if (length(elim) > 0) {
+    reorder <- reorder[-elim]
+  }
+  pathr <- s2_stars$attr[reorder]
+
+  names(pathr) <- NULL
+  s2_stars2 <- stars::read_stars(pathr, along = "band", proxy = TRUE)
+  stars_spectral$bandname <- stars_spectral$bandname[reorder]
+  stars_spectral$wavelength <- stars_spectral$wavelength[reorder]
+
+  uniqueoffset <- as.numeric(unique(offset$offset))
+  if (length(uniqueoffset) > 1) {
+    message("Warning: BOA offset differs between bands.")
+    message("offset will not be applied to the final S2 reflectance raster")
+    message("check metadata file to identify the offset applied on each band")
+    print(mtd_msi)
+  } else {
+    message("applying offset to reflectance data")
+    if (is.null(mtd_lasrc) || uniqueoffset == 0) {
+      offsets2 <- function(x) (round(x + uniqueoffset) * (10000 / boa_quantval))
+      s2_stars2 <- stars::st_apply(X = s2_stars2, MARGIN = "band", FUN = offsets2)
+    } else {
+      offsets2 <- function(x) (round(10000 * ((x + uniqueoffset * boa_quantval) / boa_quantval)))
+      s2_stars2 <- stars::st_apply(X = s2_stars2, MARGIN = "band", FUN = offsets2)
+    }
+  }
+  write_stack_s2(stars_s2 = s2_stars2, stars_spectral = stars_spectral, refl_path = refl_path,
+                 format = format, datatype = datatype, sensor = sensor, maxchunk = maxchunk)
+  # save metadata file as well if available
+  if (!is.null(mtd)) {
+    if (file.exists(mtd)) {
+      file.copy(from = mtd, to = file.path(dirname(refl_path), basename(mtd)), overwrite = TRUE)
+    }
+  }
+  # save metadata file as well if available
+  if (!is.null(mtd_msi)) {
+    if (file.exists(mtd_msi)) {
+      file.copy(from = mtd_msi, to = file.path(dirname(refl_path), basename(mtd_msi)), overwrite = TRUE)
+    }
+  }
+  # save LaSRC metadata file as well if available
+  if (!is.null(mtd_lasrc)) {
+    if (file.exists(mtd_lasrc)) {
+      file.copy(from = mtd_lasrc, to = file.path(dirname(refl_path), basename(mtd_lasrc)), overwrite = TRUE)
+    }
+  }
+  # delete temporary file
+  for (pathtemp in pathr) {
+    file.remove(pathtemp)
+    if (file.exists(paste(pathtemp, ".hdr", sep = ""))) file.remove(paste(pathtemp, ".hdr", sep = ""))
+  }
+  gc()
+  return(invisible())
+}
+
+#" ENVI functions
+#"
+#" based on https://github.com/cran/hyperSpec/blob/master/R/read.ENVI.R
+#" added wavelength, fwhm, ... to header reading
+#" Title
+#"
+#" @param x character.
+#" @param separator character
+#" @param trim_blank boolean.
+#"
+#" @return list.
+#" @export
+split_line <- function(x, separator, trim_blank = TRUE) {
+  tmp <- regexpr(separator, x)
+  key <- substr(x, 1, tmp - 1)
+  value <- substr(x, tmp + 1, nchar(x))
+  if (trim_blank) {
+    blank_pattern <- "^[[:blank:]]*([^[:blank:]]+.*[^[:blank:]]+)[[:blank:]]*$"
+    key <- sub(blank_pattern, "\\1", key)
+    value <- sub(blank_pattern, "\\1", value)
+  }
+  value <- as.list(value)
+  names(value) <- key
+  return(value)
+}
+
+#" save raster footprint as vector file
+#"
+#" @param path_raster character. path for a raster file
+#" @param path_vector character. path for a vector file
+#" @param driver character. driver for vector
+#"
+#" @return None
+#" @importFrom raster raster extent projection
+#" @importFrom sf st_as_sf st_write
+#" @export
+vectorize_raster_extent <- function(path_raster, path_vector, driver = "ESRI Shapefile") {
+  rast <- raster(path_raster)
+  e <- extent(rast)
+  # coerce to a SpatialPolygons object
+  p <- as(e, "SpatialPolygons")
+  projection(p) <- projection(rast)
+  p <- sf::st_as_sf(p)
+  sf::st_write(obj = p, path_vector, driver = driver)  # create to a shapefile
+  return(invisible())
+}
+
+#" writes ENVI hdr file
+#"
+#" @param hdr content to be written
+#" @param hdrpath Path of the hdr file
+#"
+#" @return None
+#" @importFrom stringr str_count
+#" @export
+write_envi_header <- function(hdr, hdrpath) {
+  h <- lapply(hdr, function(x) {
+    if (length(x) > 1 || (is.character(x) && stringr::str_count(x, "\\w+") > 1)) {
+      x <- paste0("{", paste(x, collapse = ", "), "}")
+    }
+    # convert last numerics
+    x <- as.character(x)
+  })
+  writeLines(c("ENVI", paste(names(hdr), h, sep = " = ")), con = hdrpath)
+  return(invisible())
+}
+
+#" This function writes a raster Stack object into a ENVI raster file
+#"
+#" @param stackobj list. raster stack
+#" @param stackpath character. path where to store the stack
+#" @param bands list. should include "bandname", and if possible "wavelength"
+#" @param datatype character. should be INT2S or FLT4S for example
+#" @param sensor character. Name of the sensor used to acquire the image
+#" @param stretch boolean. Set TRUE to get 10% stretching at display for reflectance, mentioned in hdr only
+#"
+#" @return None
+#" @importFrom utils read.table
+#" @export
+write_rasterstack_envi <- function(stackobj, stackpath, bands, datatype = "INT2S",
+                                   sensor = "Unknown", stretch = FALSE) {
+
+  r <- raster::writeRaster(x = stackobj, filename = stackpath, format = "Ehdr", overwrite = TRUE, datatype = datatype)
+  raster::hdr(r, format = "ENVI")
+  # Edit hdr file to add metadata
+  hdr <- read_envi_header(get_hdr_name(stackpath))
+  hdr$`band names` <- bands$bandname
+  if (length(bands$wavelength) == length(bands$bandname)) {
+    hdr$wavelength <- bands$wavelength
+  } else {
+    hdr$wavelength <- NULL
+  }
+  if (stretch == TRUE) {
+    hdr$`default stretch` <- "0.000000 1000.000000 linear"
+  }
+  hdr$`z plot range` <- NULL
+  hdr$`data ignore value` <- "-Inf"
+  hdr$`coordinate system string` <- read.table(paste(stackpath, ".prj", sep = ""))
+  proj <- strsplit(x = strsplit(x = projection(stackobj), split = " ")[[1]][1], split = "=")[[1]][2]
+  zone <- strsplit(x = strsplit(x = projection(stackobj), split = " ")[[1]][2], split = "=")[[1]][2]
+  datum <- strsplit(x = strsplit(x = projection(stackobj), split = " ")[[1]][3], split = "=")[[1]][2]
+  oldproj <- hdr$`map info`
+  newproj <- gsub(pattern = "projection", replacement = proj, x = oldproj)
+  newproj <- paste(newproj, zone, datum, sep = ", ")
+  hdr$`map info` <- newproj
+  hdr$`sensor type` <- sensor
+  write_envi_header(hdr = hdr, hdrpath = get_hdr_name(stackpath))
+
+  # remove unnecessary files
+  file2remove <- paste(stackpath, ".aux.xml", sep = "")
+  file.remove(file2remove)
+  file2remove <- paste(stackpath, ".prj", sep = "")
+  file.remove(file2remove)
+  file2remove <- paste(stackpath, ".stx", sep = "")
+  file.remove(file2remove)
+  return(invisible())
+}
+
+
+#" This function writes a stars object into a raster file
+#"
+#" @param stars_s2 list. stars object containing raster data. Can be produced with function Crop_n_resample_S2
+#" @param stars_spectral list. band name to be saved in the stack and spectral bands corresponding to the image
+#" @param refl_path character. path where to store the image
+#" @param format character. default = ENVI BSQ. otherwise use gdal drivers
+#" @param datatype character. should be Int16 or Float64 for example
+#" @param sensor character. Name of the sensor used to acquire the image
+#" @param maxchunk numeric. Size of individual chunks to be written (in Mb)
+#"
+#" @return None
+#" @export
+write_stack_s2 <- function(stars_s2, stars_spectral, refl_path, format = "ENVI",
+                           datatype = "Int16", sensor = "Unknown", maxchunk = 256) {
+
+  # write raster file from proxy using chunks
+  sizeobj <- 2 * dim(stars_s2)[1] * dim(stars_s2)[2] * dim(stars_s2)[3] / (1024**2)
+  nbchunks <- ceiling(sizeobj / maxchunk)
+  stars::write_stars(obj = stars_s2,
+                     dsn = refl_path,
+                     driver =  format,
+                     type = datatype,
+                     chunk_size = c(dim(stars_s2)[1], ceiling(dim(stars_s2)[2] / nbchunks)),
+                     progress = TRUE)
+
+  if (format == "ENVI") {
+    adjust_envi_hdr(dsn = refl_path, bands = stars_spectral,
+                    sensor = sensor, stretch = TRUE)
+  }
+  return(invisible())
+}
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/alpha_beta.r	Mon Jan 09 13:38:38 2023 +0000
@@ -0,0 +1,142 @@
+#Rscript
+
+###########################################
+##    Mapping alpha and beta diversity   ##
+###########################################
+
+#####Packages : stars
+#               utils
+#               biodivmapr
+#               raster
+#               sf
+#               mapview
+#               leafpop
+#               RColorBrewer
+#               labdsv
+#               rgdal
+#               ggplot2
+#               gridExtra
+
+#####Load arguments
+
+args <- commandArgs(trailingOnly = TRUE)
+
+#####Import the S2 data
+
+if (length(args) < 1) {
+    stop("This tool needs at least 1 argument")
+}else {
+    data_raster <- args[1]
+    rasterheader <- args[2]
+    data <- args[3]
+    # type of PCA:
+    # PCA: no rescaling of the data
+    # SPCA: rescaling of the data
+    typepca <- as.character(args[4])
+    alpha <- as.logical(args[5])
+    beta <- as.logical(args[6])
+    funct <- as.logical(args[7])
+    all <- as.logical(args[8])
+    nbcpu <- as.integer(args[9])
+    source(args[10])
+}
+
+################################################################################
+##              DEFINE PARAMETERS FOR DATASET TO BE PROCESSED                 ##
+################################################################################
+if (data_raster == "") {
+  #Create a directory where to unzip your folder of data
+  dir.create("data_dir")
+  unzip(data, exdir = "data_dir")
+  # Path to raster
+  data_raster <- list.files("data_dir/results/Reflectance", pattern = "_Refl")
+  input_image_file <- file.path("data_dir/results/Reflectance", data_raster[1])
+  input_header_file <- file.path("data_dir/results/Reflectance", data_raster[2])
+
+} else {
+  input_image_file <- file.path(getwd(), data_raster, fsep = "/")
+  input_header_file <- file.path(getwd(), rasterheader, fsep = "/")
+}
+
+################################################################################
+##                              PROCESS IMAGE                                 ##
+################################################################################
+# 1- Filter data in order to discard non vegetated / shaded / cloudy pixels
+
+print("PERFORM PCA ON RASTER")
+pca_output <- biodivMapR::perform_PCA(Input_Image_File = input_image_file, Input_Mask_File = input_mask_file,
+                          Output_Dir = output_dir, TypePCA = typepca, FilterPCA = filterpca, nbCPU = nbcpu, MaxRAM = maxram)
+
+pca_files <- pca_output$PCA_Files
+pix_per_partition <- pca_output$Pix_Per_Partition
+nb_partitions <- pca_output$nb_partitions
+# path for the updated mask
+input_mask_file <- pca_output$MaskPath
+
+
+selected_pcs <- seq(1, dim(raster::stack(input_image_file))[3])
+
+selected_pcs <- all(selected_pcs)
+################################################################################
+##                      MAP ALPHA AND BETA DIVERSITY                          ##
+################################################################################
+print("MAP SPECTRAL SPECIES")
+
+kmeans_info <- biodivMapR::map_spectral_species(Input_Image_File = input_image_file, Output_Dir = output_dir, PCA_Files = pca_files, Input_Mask_File = input_mask_file, SelectedPCs = selected_pcs, Pix_Per_Partition = pix_per_partition, nb_partitions = nb_partitions, TypePCA = typepca, nbCPU = nbcpu, MaxRAM = maxram, nbclusters = nbclusters)
+
+image_name <- tools::file_path_sans_ext(basename(input_image_file))
+if (alpha == TRUE || beta == TRUE || all == TRUE) {
+## alpha
+  print("MAP ALPHA DIVERSITY")
+  index_alpha <- c("Shannon")
+  alpha_div <- biodivMapR::map_alpha_div(Input_Image_File = input_image_file, Output_Dir = output_dir, TypePCA = typepca, window_size = window_size, nbCPU = nbcpu, MaxRAM = maxram, Index_Alpha = index_alpha, nbclusters = nbclusters, FullRes = TRUE, LowRes = FALSE, MapSTD = FALSE)
+
+  alpha_zip <- file.path(output_dir, image_name, typepca, "ALPHA", "Shannon_10_Fullres.zip")
+  alpha_path <- file.path(output_dir, image_name, typepca, "ALPHA")
+  unzip(alpha_zip, exdir = alpha_path)
+  alpha_path <- file.path(output_dir, image_name, typepca, "ALPHA", "Shannon_10_Fullres")
+  alpha_raster <- raster::raster(alpha_path)
+  get_alpha <- convert_raster(alpha_raster)
+
+ if (alpha == TRUE || all == TRUE) {
+   colnames(get_alpha) <- c("Alpha", "longitude", "latitude")
+   plot_indices(get_alpha, titre = "Alpha")
+
+   write.table(get_alpha, file = "alpha.tabular", sep = "\t", dec = ".", na = " ", row.names = FALSE, col.names = TRUE, quote = FALSE)
+}
+  if (beta == TRUE || all == TRUE) {
+## beta
+  print("MAP BETA DIVERSITY")
+  beta_div <- biodivMapR::map_beta_div(Input_Image_File = input_image_file, Output_Dir = output_dir, TypePCA = typepca, window_size = window_size, nb_partitions = nb_partitions, nbCPU = nbcpu, MaxRAM = maxram, nbclusters = nbclusters)
+
+  beta_path <- file.path(output_dir, image_name, typepca, "BETA", "BetaDiversity_BCdiss_PCO_10")
+  beta_raster <- raster::raster(beta_path)
+  get_beta <- convert_raster(beta_raster)
+
+  colnames(get_beta) <- c("Beta", "longitude", "latitude")
+  plot_indices(get_beta, titre = "Beta")
+
+  write.table(get_beta, file = "beta.tabular", sep = "\t", dec = ".", na = " ", row.names = FALSE, col.names = TRUE, quote = FALSE)
+  }
+}
+
+
+################################################################################
+##          COMPUTE ALPHA AND BETA DIVERSITY FROM FIELD PLOTS                 ##
+################################################################################
+
+if (funct == TRUE || all == TRUE) {
+  mapper <- biodivMapR::map_functional_div(Original_Image_File = input_image_file, Functional_File = pca_files,  Selected_Features = selected_pcs, Output_Dir = output_dir, window_size = window_size, nbCPU = nbcpu, MaxRAM = maxram, TypePCA = typepca, FullRes = TRUE, LowRes = FALSE, MapSTD = FALSE)
+
+  funct_zip <- file.path(output_dir, image_name, typepca, "FUNCTIONAL", "FunctionalDiversity_Map_MeanFilter_Fullres.zip")
+  funct_path <- file.path(output_dir, image_name, typepca, "FUNCTIONAL")
+  unzip(funct_zip, exdir = funct_path)
+  funct_path <- file.path(output_dir, image_name, typepca, "FUNCTIONAL", "FunctionalDiversity_Map_MeanFilter_Fullres")
+  funct_raster <- raster::raster(funct_path)
+  get_funct <- convert_raster(funct_raster)
+
+  colnames(get_funct) <- c("Functionnal", "longitude", "latitude")
+  plot_indices(get_funct, titre = "Functionnal")
+
+  write.table(get_funct, file = "Functionnal.tabular", sep = "\t", dec = ".", na = " ", row.names = FALSE, col.names = TRUE, quote = FALSE)
+}
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/biodiv_indices_global.r	Mon Jan 09 13:38:38 2023 +0000
@@ -0,0 +1,112 @@
+#Rscript
+
+###########################################
+##    Mapping  biodiversity indicators   ##
+###########################################
+
+#####Packages :  raster,
+#                rgdal,
+#                sp,
+#                rasterdiv,
+#                ggplot2,
+
+#####Load arguments
+
+args <- commandArgs(trailingOnly = TRUE)
+
+#####Import the S2 data
+
+if (length(args) < 1) {
+    stop("This tool needs at least 1 argument")
+}else {
+    data_raster <- args[1]
+    data_header <- args[2]
+    data <- args[3]
+    alpha <- as.numeric(args[4])
+    source(args[5])
+
+}
+
+########################################################################
+##                   COMPUTE BIODIVERSITY INDICES                     ##
+########################################################################
+
+if (data_raster == "") {
+  #Create a directory where to unzip your folder of data
+  dir.create("data_dir")
+  unzip(data, exdir = "data_dir")
+  # Path to raster
+  data_raster <- list.files("data_dir/results/Reflectance", pattern = "_Refl")
+  data_raster <- file.path("data_dir/results/Reflectance", data_raster[1])
+}
+
+# Read raster
+copndvi <- raster::raster(data_raster)
+
+copndvilr <- raster::reclassify(copndvi, cbind(252, 255, NA), right = TRUE)
+
+#Resample using raster::aggregate and a linear factor of 10
+copndvilr <- raster::aggregate(copndvilr, fact = 20)
+#Set float numbers as integers to further speed up the calculation
+storage.mode(copndvilr[]) <- "integer"
+
+# Convert raster to SpatialPointsDataFrame
+r_pts <- convert_raster(copndvilr)
+
+#Shannon's Diversity
+sha <- rasterdiv::Shannon(copndvilr, window = 9, np = 1)
+sha_df <- data.frame(raster::rasterToPoints(sha, spatial = TRUE))
+sha_name <- "Shannon"
+r_pts[, sha_name] <- sha_df[, 1]
+
+#Renyi's Index
+ren <- rasterdiv::Renyi(copndvilr, window = 9, alpha = alpha, np = 1)
+ren_df <- data.frame(raster::rasterToPoints(ren[[1]]))
+ren_name <- "Renyi"
+r_pts[, ren_name] <- ren_df[, 3]
+
+#Berger-Parker's Index
+ber <- rasterdiv::BergerParker(copndvilr, window = 9, np = 1)
+ber_df <- data.frame(raster::rasterToPoints(ber, spatial = TRUE))
+ber_name <- "Berger-Parker"
+r_pts[, ber_name] <- ber_df[, 1]
+
+#Pielou's Evenness
+pie <- rasterdiv::Pielou(copndvilr, window = 9, np = 1)
+pie_df <- data.frame(raster::rasterToPoints(pie))
+if (length(pie_df[, 1]) == length(r_pts[, 1])) {
+  pie_name <- "Pielou"
+  r_pts[, pie_name] <- pie_df[, 1]
+}
+
+#Hill's numbers
+hil <- rasterdiv::Hill(copndvilr, window = 9, alpha = alpha, np = 1)
+hil_df <- data.frame(raster::rasterToPoints(hil[[1]]))
+hil_name <- "Hill"
+r_pts[, hil_name] <- hil_df[, 3]
+
+#Parametric Rao's quadratic entropy with alpha ranging from 1 to 5
+prao <- rasterdiv::paRao(copndvilr, window = 9, alpha = alpha, dist_m = "euclidean", np = 1)
+prao_df <- data.frame(raster::rasterToPoints(prao$window.9[[1]]))
+prao_name <- "Prao"
+r_pts[, prao_name] <- prao_df[, 3]
+
+#Cumulative Residual Entropy
+cre <- rasterdiv::CRE(copndvilr, window = 9, np = 1)
+cre_df <- data.frame(raster::rasterToPoints(cre))
+if (length(cre_df[, 1]) == length(r_pts[, 1])) {
+  cre_name <- "CRE"
+  r_pts[, cre_name] <- cre_df[, 1]
+}
+
+if (length(cre_df[, 1]) == length(r_pts[, 1]) || length(pie_df[, 1]) == length(r_pts[, 1])) {
+list_indice <- list("Shannon", "Renyi", "Berger-Parker", "Pielou", "Hill", "Prao", "CRE")
+} else {
+list_indice <- list("Shannon", "Renyi", "Berger-Parker", "Hill", "Prao")
+}
+## Plotting all the graph and writing a tabular
+for (indice in list_indice) {
+  plot_indices(data = r_pts, titre = indice)
+}
+
+write.table(r_pts, file = "BiodivIndex.tabular", sep = "\t", dec = ".", na = " ", row.names = FALSE, col.names = TRUE, quote = FALSE)
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/comparison_div.r	Mon Jan 09 13:38:38 2023 +0000
@@ -0,0 +1,190 @@
+#Rscript
+
+###########################################
+##    Mapping alpha and beta diversity   ##
+###########################################
+
+#####Packages : stars
+#               utils
+#               biodivmapr
+#               raster
+#               sf
+#               mapview
+#               leafpop
+#               RColorBrewer
+#               labdsv
+#               rgdal
+#               ggplot2
+#               gridExtra
+##remotes::install_github("jbferet/biodivMapR")
+#####Load arguments
+
+args <- commandArgs(trailingOnly = TRUE)
+
+#####Import the S2 data
+
+if (length(args) < 1) {
+    stop("This tool needs at least 1 argument")
+}else {
+    data_raster <- args[1]
+    rasterheader <- args[2]
+    data <- args[3]
+    plots_zip <- args[4]
+    choice <- as.character(args[5])
+    source(args[6])
+    # type of PCA:
+    # PCA: no rescaling of the data
+    # SPCA: rescaling of the data
+    typepca <- as.character(args[7])
+}
+
+################################################################################
+##              DEFINE PARAMETERS FOR DATASET TO BE PROCESSED                 ##
+################################################################################
+if (data_raster == "") {
+  #Create a directory where to unzip your folder of data
+  dir.create("data_dir")
+  unzip(data, exdir = "data_dir")
+  # Path to raster
+  data_raster <- list.files("data_dir/results/Reflectance", pattern = "_Refl")
+  input_image_file <- file.path("data_dir/results/Reflectance", data_raster[1])
+  input_header_file <- file.path("data_dir/results/Reflectance", data_raster[2])
+
+} else {
+  input_image_file <- file.path(getwd(), data_raster, fsep = "/")
+  input_header_file <- file.path(getwd(), rasterheader, fsep = "/")
+}
+
+################################################################################
+##                              PROCESS IMAGE                                 ##
+################################################################################
+# 1- Filter data in order to discard non vegetated / shaded / cloudy pixels
+
+print("PERFORM PCA ON RASTER")
+pca_output <- biodivMapR::perform_PCA(Input_Image_File = input_image_file, Input_Mask_File = input_mask_file,
+                          Output_Dir = output_dir, TypePCA = typepca, FilterPCA = filterpca, nbCPU = nbcpu, MaxRAM = maxram)
+
+pca_files <- pca_output$PCA_Files
+pix_per_partition <- pca_output$Pix_Per_Partition
+nb_partitions <- pca_output$nb_partitions
+# path for the updated mask
+input_mask_file <- pca_output$MaskPath
+
+# 3- Select principal components from the PCA raster
+# Select components from the PCA/SPCA/MNF raster
+sel_compo <- c("1\n", "2\n", "3\n", "4\n", "5\n", "6\n", "7\n", "8")
+image_name <- tools::file_path_sans_ext(basename(input_image_file))
+output_dir_full <- file.path(output_dir, image_name, typepca, "PCA")
+
+write.table(sel_compo, paste0(output_dir_full, "/Selected_Components.txt"))
+sel_pc <-  file.path(output_dir_full, "Selected_Components.txt")
+
+
+################################################################################
+##                      MAP ALPHA AND BETA DIVERSITY                          ##
+################################################################################
+print("MAP SPECTRAL SPECIES")
+
+kmeans_info <- biodivMapR::map_spectral_species(Input_Image_File = input_image_file, Output_Dir = output_dir, PCA_Files = pca_files, Input_Mask_File = input_mask_file, Pix_Per_Partition = pix_per_partition, nb_partitions = nb_partitions, nbCPU = nbcpu, MaxRAM = maxram, nbclusters = nbclusters, TypePCA = typepca)
+
+################################################################################
+##          COMPUTE ALPHA AND BETA DIVERSITY FROM FIELD PLOTS                 ##
+################################################################################
+## read selected features from dimensionality reduction
+
+## path for selected components
+
+# location of the directory where shapefiles used for validation are saved
+dir.create("VectorDir")
+unzip(plots_zip, exdir = "VectorDir")
+
+# list vector data
+path_vector <- biodivMapR::list_shp("VectorDir")
+name_vector <- tools::file_path_sans_ext(basename(path_vector))
+
+# location of the spectral species raster needed for validation
+path_spectralspecies <- kmeans_info$SpectralSpecies
+# get diversity indicators corresponding to shapefiles (no partitioning of spectral dibversity based on field plots so far...)
+biodiv_indicators <- biodivMapR::diversity_from_plots(Raster_SpectralSpecies = path_spectralspecies, Plots = path_vector, nbclusters = nbclusters, Raster_Functional = pca_files, Selected_Features = FALSE)
+
+shannon_rs <- c(biodiv_indicators$Shannon)[[1]]
+fric <- c(biodiv_indicators$FunctionalDiversity$FRic)
+feve <- c(biodiv_indicators$FunctionalDiversity$FEve)
+fdiv <- c(biodiv_indicators$FunctionalDiversity$FDiv)
+# if no name for plots
+biodiv_indicators$Name_Plot <- seq(1, length(biodiv_indicators$Shannon[[1]]), by = 1)
+
+
+####################################################
+# write RS indicators                              #
+####################################################
+# write a table for Shannon index
+
+# write a table for all spectral diversity indices corresponding to alpha diversity
+results <- data.frame(name_vector, biodiv_indicators$Richness, biodiv_indicators$Fisher,
+                      biodiv_indicators$Shannon, biodiv_indicators$Simpson,
+                      biodiv_indicators$FunctionalDiversity$FRic,
+                      biodiv_indicators$FunctionalDiversity$FEve,
+                      biodiv_indicators$FunctionalDiversity$FDiv)
+
+names(results) <- c("ID_Plot", "Species_Richness", "Fisher", "Shannon", "Simpson", "fric", "feve", "fdiv")
+write.table(results, file = "Diversity.tabular", sep = "\t", dec = ".", na = " ", row.names = FALSE, col.names = TRUE, quote = FALSE)
+
+if (choice == "Y") {
+# write a table for Bray Curtis dissimilarity
+bc_mean <- biodiv_indicators$BCdiss
+bray_curtis <- data.frame(name_vector, bc_mean)
+colnames(bray_curtis) <- c("ID_Plot", bray_curtis[, 1])
+write.table(bray_curtis, file = "BrayCurtis.tabular", sep = "\t", dec = ".", na = " ", row.names = FALSE, col.names = TRUE, quote = FALSE)
+
+####################################################
+# illustrate results
+####################################################
+# apply ordination using PCoA (same as done for map_beta_div)
+
+mat_bc_dist <- as.dist(bc_mean, diag = FALSE, upper = FALSE)
+betapco <- labdsv::pco(mat_bc_dist, k = 3)
+
+# assign a type of vegetation to each plot, assuming that the type of vegetation
+# is defined by the name of the shapefile
+
+nbsamples <- shpname <- c()
+for (i in 1:length(path_vector)) {
+  shp <- path_vector[i]
+  nbsamples[i] <- length(rgdal::readOGR(shp, verbose = FALSE))
+  shpname[i] <- tools::file_path_sans_ext(basename(shp))
+}
+
+type_vegetation <- c()
+for (i in 1: length(nbsamples)) {
+  for (j in 1:nbsamples[i]) {
+    type_vegetation <- c(type_vegetation, shpname[i])
+  }
+}
+
+#data frame including a selection of alpha diversity metrics and beta diversity expressed as coordinates in the PCoA space
+results <- data.frame("vgtype" = type_vegetation, "pco1" = betapco$points[, 1], "pco2" = betapco$points[, 2], "pco3" = betapco$points[, 3], "shannon" = shannon_rs, "fric" = fric, "feve" = feve, "fdiv" = fdiv)
+
+#plot field data in the PCoA space, with size corresponding to shannon index
+g1 <- ggplot2::ggplot(results, ggplot2::aes(x = pco1, y = pco2, color = vgtype, size = shannon)) + ggplot2::geom_point(alpha = 0.6) + ggplot2::scale_color_manual(values = c("#e6140a", "#e6d214", "#e68214", "#145ae6"))
+
+g2 <- ggplot2::ggplot(results, ggplot2::aes(x = pco1, y = pco3, color = vgtype, size = shannon)) + ggplot2::geom_point(alpha = 0.6) + ggplot2::scale_color_manual(values = c("#e6140a", "#e6d214", "#e68214", "#145ae6"))
+
+g3 <- ggplot2::ggplot(results, ggplot2::aes(x = pco2, y = pco3, color = vgtype, size = shannon)) + ggplot2::geom_point(alpha = 0.6) + ggplot2::scale_color_manual(values = c("#e6140a", "#e6d214", "#e68214", "#145ae6"))
+
+#extract legend
+get_legend <- function(a_gplot) {
+    tmp <- ggplot2::ggplot_gtable(ggplot2::ggplot_build(a_gplot))
+    leg <- which(sapply(tmp$grobs, function(x) x$name) == "guide-box")
+    legend <- tmp$grobs[[leg]]
+    return(legend)
+}
+
+legend <- get_legend(g3)
+gall <- gridExtra::grid.arrange(gridExtra::arrangeGrob(g1 + ggplot2::theme(legend.position = "none"), g2 + ggplot2::theme(legend.position = "none"), g3 + ggplot2::theme(legend.position = "none"), nrow = 1), legend, nrow = 2, heights = c(3, 2))
+
+
+filename <- ggplot2::ggsave("BetaDiversity_PcoA1_vs_PcoA2_vs_PcoA3.png", gall, scale = 0.65, width = 12, height = 9, units = "in", dpi = 200, limitsize = TRUE)
+
+filename
+}
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/functions.r	Mon Jan 09 13:38:38 2023 +0000
@@ -0,0 +1,79 @@
+#Rscript
+
+#########################
+##      Functions      ##
+#########################
+
+#####Packages : raster
+#               sp
+#               ggplot2
+
+####Set paramaters for all tools using BiodivMapR
+
+# path for the Mask raster corresponding to image to process
+# expected to be in ENVI HDR format, 1 band, integer 8bits
+# expected values in the raster: 0 = masked, 1 = selected
+# set to FALSE if no mask available
+input_mask_file <- FALSE
+
+# relative or absolute path for the Directory where results will be stored
+# For each image processed, a subdirectory will be created after its name
+output_dir <- "RESULTS"
+
+# SPATIAL RESOLUTION
+# resolution of spatial units for alpha and beta diversity maps (in pixels), relative to original image
+# if Res.Map = 10 for images with 10 m spatial resolution, then spatial units will be 10 pixels x 10m = 100m x 100m surfaces
+# rule of thumb: spatial units between 0.25 and 4 ha usually match with ground data
+# too small window_size results in low number of pixels per spatial unit, hence limited range of variation of diversity in the image
+window_size <- 10
+
+# PCA FILTERING: Set to TRUE if you want second filtering based on PCA outliers to be processed. Slower
+filterpca <- TRUE
+
+################################################################################
+##                    DEFINE PARAMETERS FOR METHOD                            ##
+################################################################################
+nbcpu <- 4
+maxram <- 0.5
+nbclusters <- 50
+
+################################################################################
+##                              PROCESS IMAGE                                 ##
+################################################################################
+# 1- Filter data in order to discard non vegetated / shaded / cloudy pixels
+ndvi_thresh <- 0.5
+blue_thresh <- 500
+nir_thresh  <- 1500
+continuum_removal <- TRUE
+
+
+
+#### Convert raster to dataframe
+
+# Convert raster to SpatialPointsDataFrame
+convert_raster <- function(data_raster) {
+r_pts <- raster::rasterToPoints(data_raster, spatial = TRUE)
+
+# reproject sp object
+geo_prj <- "+proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0"
+r_pts <- sp::spTransform(r_pts, sp::CRS(geo_prj))
+
+
+# Assign coordinates to @data slot, display first 6 rows of data.frame
+r_pts@data <- data.frame(r_pts@data, longitude = sp::coordinates(r_pts)[, 1],
+                         latitude = sp::coordinates(r_pts)[, 2])
+
+return(r_pts@data)
+}
+
+
+#### Potting indices
+
+plot_indices <- function(data, titre) {
+  graph_indices <- ggplot2::ggplot(data) +
+  ggplot2::geom_point(ggplot2::aes_string(x = data[, 2], y = data[, 3], color = data[, titre]), shape = "square", size = 2) + ggplot2::scale_colour_gradient(low = "blue", high = "orange", na.value = "grey50") +
+  ggplot2::xlab("Longitude") + ggplot2::ylab("Latitude") +
+  ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 90, vjust = 0.5, hjust = 1), plot.title = ggplot2::element_text(color = "black", size = 12, face = "bold")) + ggplot2::ggtitle(titre)
+
+ggplot2::ggsave(paste0(titre, ".png"), graph_indices, width = 12, height = 10, units = "cm")
+}
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/indices_spectral.r	Mon Jan 09 13:38:38 2023 +0000
@@ -0,0 +1,105 @@
+#Rscript
+
+###########################################
+##    Mapping alpha and beta diversity   ##
+###########################################
+
+#####Packages :  expint,
+#                pracma,
+#                R.utils,
+#                raster,
+#                sp,
+#                matrixStats,
+#                ggplot2,
+#                expandFunctions,
+#                stringr,
+#                XML,
+#                rgdal,
+#                stars,
+#####Load arguments
+
+args <- commandArgs(trailingOnly = TRUE)
+
+#####Import the S2 data
+
+if (length(args) < 1) {
+    stop("This tool needs at least 1 argument")
+}else {
+    data_raster <- args[1]
+    data_header <- args[2]
+    data <- args[3]
+    source(args[4])
+    source(args[5])
+    source(args[6])
+    indice_choice <- strsplit(args[7], ",")[[1]]
+    source(args[8])
+    output_raster <- as.character(args[9])
+
+}
+
+########################################################################
+##                  COMPUTE SPECTRAL INDEX : NDVI                     ##
+########################################################################
+
+if (data != "") {
+  #Create a directory where to unzip your folder of data
+  dir.create("data_dir")
+  unzip(data, exdir = "data_dir")
+
+  # Read raster
+  data_raster <- list.files("data_dir/results/Reflectance", pattern = "_Refl")
+  data_raster <- file.path("data_dir/results/Reflectance", data_raster[1])
+  refl <- raster::brick(data_raster)
+  refl2 <- raster::raster(data_raster)
+} else {
+  # Read raster
+  refl <- raster::brick(data_raster)
+  refl2 <- raster::raster(data_raster)
+}
+# get raster band name and clean format. Expecting band name and wav
+# reflFactor = 10000 when reflectance is coded as INT16
+refl <- raster::aggregate(refl, fact = 10)
+
+# Convert raster to SpatialPointsDataFrame
+refl2 <- raster::aggregate(refl2, fact = 10)
+r_pts <- convert_raster(refl2)
+table_ind <- r_pts
+# create directory for Spectralelength to be documented in image
+hdr_refl <- read_envi_header(get_hdr_name(data_raster))
+sensorbands <- hdr_refl$wavelength
+# compute a set of spectral indices defined by indexlist from S2 data indices
+si_path <- file.path("SpectralIndices")
+dir.create(path = si_path, showWarnings = FALSE, recursive = TRUE)
+# Save spectral indices
+
+indices <- lapply(indice_choice, function(x) {
+  indices_list <- computespectralindices_raster(refl = refl, sensorbands = sensorbands,
+                                                  sel_indices = x,
+                                                  reflfactor = 10000, stackout = FALSE)
+
+  index_path <- file.path(si_path, paste(basename(data_raster), "_", x, sep = ""))
+  spec_indices <- stars::write_stars(stars::st_as_stars(indices_list$spectralindices[[1]]), dsn = index_path, driver = "ENVI", type = "Float32")
+
+  # write band name in HDR
+  hdr <- read_envi_header(get_hdr_name(index_path))
+  hdr$`band names` <- x
+  write_envi_header(hdr = hdr, hdrpath = get_hdr_name(index_path))
+  # Writting the tabular and the plot
+  r_pts[, x] <- as.data.frame(sapply(spec_indices, c))
+  plot_indices(data = r_pts, titre = x)
+  return(r_pts)
+})
+
+new_table <- as.data.frame(indices)
+new_table <- new_table[, !grepl("longitude", names(new_table))]
+new_table <- new_table[, !grepl("latitude", names(new_table))]
+new_table <- new_table[, !grepl(basename(data_raster), names(new_table))]
+
+table_ind <- cbind(table_ind, new_table)
+if (length(indice_choice) == 1) {
+  colnames(table_ind) <- c(basename(data_raster), "longitude", "latitude", indice_choice)
+}
+
+write.table(table_ind, file = "Spec_Index.tabular", sep = "\t", dec = ".", na = " ", row.names = FALSE, col.names = TRUE, quote = FALSE)
+
+# Get the raster layer of the indice as an output
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/macro.xml	Mon Jan 09 13:38:38 2023 +0000
@@ -0,0 +1,102 @@
+<macros>
+    <token name="@VERSION@">0.0.1</token>
+    <xml name="SRS_requirements">
+        <requirements>
+            <requirement type="package" version="4.2.2">r-base</requirement>
+            <requirement type="package" version="2.12.2">r-r.utils</requirement>
+            <requirement type="package" version="1.0_7">r-sf</requirement>
+            <requirement type="package" version="1.5_32">r-rgdal</requirement>
+            <requirement type="package" version="0.6_1">r-rgeos</requirement>
+            <requirement type="package" version="0.5_5">r-stars</requirement>
+            <requirement type="package" version="1.5.0">r-stringr</requirement>
+            <requirement type="package" version="1.9.8">r-biodivmapr</requirement>
+            <requirement type="package" version="0.36">r-xfun</requirement>
+            <requirement type="package" version="0.51.4">r-rastervis</requirement>
+            <requirement type="package" version="3.4.0">r-ggplot2</requirement>
+            <requirement type="package" version="2.11.0">r-mapview</requirement>
+            <requirement type="package" version="2.3">r-gridextra</requirement>
+            <requirement type="package" version="3.0.0">r-emstreer</requirement>
+            <requirement type="package" version="3.2.3">r-filesstrings</requirement>
+            <!--requirement type="package" version="9.0.1">proj</requirement-->
+            <yield/>
+        </requirements>
+    </xml>
+    <xml name="SRS_input">
+       <param name="input" type="data" format="tabular,csv,txt" label="Input table"/>
+       <param name="colnames" type="boolean" label="First line is a header line" checked="true"/>
+    </xml>
+    <xml name="SRS_bibref">
+       <citations>
+            <citation type="bibtex">
+            @Manual{,
+            title = {obisindicators: Develop marine biodiversity indicators from OBIS data},
+            author = {Ben Ben and Pieter Provoost and Tylar Murray},
+            year = {2022},
+            note = {https://marinebon.org/obisindicators,
+            https://github.com/marinebon/obisindicators},
+            }
+            </citation>
+        </citations>
+    </xml>
+    <xml name="SRS_S2ref">
+        <citations>
+            <citation type="bibtex">
+            @Manual{,
+            title = {preprocS2: preprocessing of Sentinel-2 data downloaded from various data hubs / produced from various atmospheric correction methods},
+            author = {Jean-Baptiste Feret},
+            year = {2022},
+            note = {R package version 1.1.3},
+            url = {https://gitlab.com/jbferet/preprocS2},
+            }
+            </citation>   
+        </citations>
+    </xml>
+    <xml name="SRS_BDMRref">
+        <citations>
+            <citation type="bibtex">
+            @Manual{,
+            title = {biodivMapR: biodivMapR: an R package for a- and ß-diversity mapping using remotely-sensed images},
+            author = {Jean-Baptiste Feret and Florian {de Boissieu}},
+            year = {2022},
+            note = {R package version 1.9.4},
+            url = {https://github.com/jbferet/biodivMapR},
+            }
+            </citation>   
+        </citations>
+    </xml>
+    <xml name="SRS_rasterdivref">
+        <citations>
+            <citation type="doi">doi:10.1016/j.ecolind.2016.07.039</citation> 
+            <citation type="doi">doi:10.1101/2021.01.23.427872</citation>     
+        </citations>
+    </xml>
+    <xml name="SRS_prosailref">
+        <citations>
+            <citation type="bibtex">
+            @Manual{,
+            title = {prosail: PROSAIL leaf and canopy radiative transfer model and inversion routines},
+            author = {Jean-Baptiste Feret and Florian {de Boissieu}},
+            year = {2022},
+            note = {R package version 1.1.1},
+            url = {https://gitlab.com/jbferet/prosail},
+            }
+            </citation>   
+        </citations>
+    </xml>
+    <xml name="SRS_metaref">
+        <citations>
+            <citation type="doi">doi.org/10.5281/zenodo.5907920</citation>
+        </citations>
+    </xml>
+    <xml name="SRS_vegetpheno">
+        <citations>
+            <citation type="doi">doi.org/10.1016/j.rse.2013.09.022</citation>
+        </citations>
+    </xml>
+    <xml name="topic">
+        <edam_topics>
+           <edam_topic>topic_0610</edam_topic>
+           <edam_topic>topic_3050</edam_topic>
+        </edam_topics>
+    </xml>
+</macros>
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/pca_raster.r	Mon Jan 09 13:38:38 2023 +0000
@@ -0,0 +1,75 @@
+#Rscript
+
+###########################################
+##    Getting PCA raster   ##
+###########################################
+
+#####Packages : stars
+#               utils
+#               biodivmapr
+#               raster
+#               sf
+#               mapview
+#               leafpop
+#               RColorBrewer
+#               labdsv
+#               rgdal
+#               ggplot2
+#               gridExtra
+## remotes::install_github("jbferet/biodivMapR")
+#####Load arguments
+
+args <- commandArgs(trailingOnly = TRUE)
+
+#####Import the S2 data
+
+if (length(args) < 1) {
+    stop("This tool needs at least 1 argument")
+}else {
+    data_raster <- args[1]
+    rasterheader <- args[2]
+    data <- args[3]
+    typepca <- as.character(args[4])
+    source(args[5])
+}
+
+################################################################################
+##              DEFINE PARAMETERS FOR DATASET TO BE PROCESSED                 ##
+################################################################################
+# expected to be in ENVI HDR
+
+if (data_raster == "") {
+  #Create a directory where to unzip your folder of data
+  dir.create("data_dir")
+  unzip(data, exdir = "data_dir")
+  # Path to raster
+  data_raster <- list.files("data_dir/results/Reflectance", pattern = "_Refl")
+  input_image_file <- file.path("data_dir/results/Reflectance", data_raster[1])
+  input_header_file <- file.path("data_dir/results/Reflectance", data_raster[2])
+
+} else {
+  input_image_file <- file.path(getwd(), data_raster, fsep = "/")
+  input_header_file <- file.path(getwd(), rasterheader, fsep = "/")
+}
+
+################################################################################
+##                              PROCESS IMAGE                                 ##
+################################################################################
+# 1- Filter data in order to discard non vegetated / shaded / cloudy pixels
+print("PERFORM PCA ON RASTER")
+pca_output <- biodivMapR::perform_PCA(Input_Image_File = input_image_file, Input_Mask_File = input_mask_file,
+                          Output_Dir = output_dir, TypePCA = typepca, FilterPCA = filterpca, nbCPU = nbcpu, MaxRAM = maxram)
+
+
+pca_path <- file.path(output_dir, basename(data_raster), typepca, "PCA", "OutputPCA_8_PCs")
+pca_raster <- raster::raster(pca_path)
+get_pca <- convert_raster(pca_raster)
+
+colnames(get_pca) <- c("PCA", "longitude", "latitude")
+plot_indices(get_pca, titre = "PCA")
+
+write.table(get_pca, file = "PCA.tabular", sep = "\t", dec = ".", na = " ", row.names = FALSE, col.names = TRUE, quote = FALSE)
+#### Get the raster layer files
+pca_files <- file.path("RESULTS", basename(data_raster), typepca, "PCA")
+to_dir_short <- output_dir
+file.copy(pca_files, to_dir_short) #copy files from long to short paths
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/preprocess_S2.r	Mon Jan 09 13:38:38 2023 +0000
@@ -0,0 +1,121 @@
+#Rscript
+
+###########################################
+##     Preprocessing Sentinel 2 data     ##
+###########################################
+
+#####Packages : sen2r,
+#    jqr,
+#    protolite,
+#    raster,
+#    sf,
+#    rgeos,
+#    sp,
+#    raster,
+#    stars,
+#    stringr,
+#    progress,
+#    rgdal,
+#    R.utils,
+#    gdalUtils,
+#    fasterize,
+#    XML,
+#    XML2
+
+#####Load arguments
+
+args <- commandArgs(trailingOnly = TRUE)
+
+if (length(args) < 1) {
+    stop("This tool needs at least 1 argument")
+}else {
+    data <- args[1]
+    source(args[2])
+    data_source <- as.character(args[3])
+    sat_type <- as.character(args[4])
+}
+
+##____________________________________________________________________##
+##        Define where data is stored and where to write results      ##
+##--------------------------------------------------------------------##
+
+#Create a directory where to unzip your folder of data
+dir.create("data_dir")
+unzip(data, exdir = "data_dir")
+
+# Result directory
+result_path <- "results"
+dir.create(path = result_path, showWarnings = FALSE, recursive = TRUE)
+
+#Csv file for output useless but needed for linter
+write.csv(data_source, "Mission.csv")
+
+# define raster path
+if (data_source == "SAFE") {
+    path_s2 <- file.path("data_dir", list.files("data_dir", pattern = ".SAFE"))
+    #To define the level and know if a correction is needed (convert not ready yet)
+    level_info <- get_s2_level(path_s2)
+    if (level_info == "L1C") {
+        stop("! This tool works for data of L2A level and NOT for the L1C level which is currently a work in progress !")
+    }
+}else {
+    path_s2 <- file.path("data_dir")
+}
+
+##____________________________________________________________________##
+##                  Extract, resample & stack data                    ##
+##--------------------------------------------------------------------##
+# define resolution
+resolution <- 10
+# define source of data
+s2source <- data_source
+
+s2obj <- extract_from_s2_l2a(path_dir_s2 = path_s2,
+                                        path_vector = NULL,
+                                        s2source = s2source,
+                                        resolution = resolution)
+
+##____________________________________________________________________##
+##                        Write CLOUD MASK                            ##
+##--------------------------------------------------------------------##
+
+# directory for cloud mask
+cloud_path <- file.path(result_path, "CloudMask")
+dir.create(path = cloud_path, showWarnings = FALSE, recursive = TRUE)
+# Filename for cloud mask
+cloudmasks <- save_cloud_s2(s2_stars = s2obj$s2_stack,
+                                       cloud_path = cloud_path,
+                                       s2source = s2source, saveraw = TRUE)
+
+zip_cloud <- file.path("Cloud.zip")
+zip::zip(zip_cloud, cloud_path)
+##____________________________________________________________________##
+##                        Write REFLECTANCE                           ##
+##--------------------------------------------------------------------##
+
+# directory for Reflectance
+refl_dir <- file.path(result_path, "Reflectance")
+dir.create(path = refl_dir, showWarnings = FALSE, recursive = TRUE)
+
+if (data_source == "SAFE") {
+    # filename for Reflectance
+    refl_path <- file.path(refl_dir, paste(basename(s2obj$s2_bands$GRANULE), "_Refl", sep = ""))
+
+    # Save Reflectance file as ENVI image with BIL interleaves
+    tile_s2 <- substring(strsplit(basename(s2obj$s2_bands$GRANULE), "_")[[1]][2], 2)
+    dateacq_s2 <- as.Date(substring(strsplit(basename(s2obj$s2_bands$GRANULE), "_")[[1]][4], 1, 8), format = "%Y%m%d")
+}else {
+    # filename for Reflectance
+    refl_path <- file.path(refl_dir, paste(basename(s2obj$s2_bands$path_tile_s2), "_Refl", sep = ""))
+
+    # Save Reflectance file as ENVI image with BIL interleaves
+    tile_s2 <- substring(strsplit(basename(s2obj$s2_bands$path_tile_s2), "_")[[1]][2], 2)
+    dateacq_s2 <- as.Date(substring(strsplit(basename(s2obj$s2_bands$path_tile_s2), "_")[[1]][4], 1, 8), format = "%Y%m%d")
+}
+
+save_data <- save_reflectance_s2(s2_stars = s2obj$s2_stack, refl_path = refl_path,
+                               s2sat = sat_type, tile_s2 = tile_s2, dateacq_s2 = dateacq_s2,
+                               format = "ENVI", datatype = "Int16", mtd = s2obj$s2_bands$metadata, mtd_msi = s2obj$s2_bands$metadata_MSI)
+
+zip_files <- file.path("Refl.zip")
+zip::zip(zip_files, refl_dir)
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/prosail-master/R/Lib_PROSAIL.R	Mon Jan 09 13:38:38 2023 +0000
@@ -0,0 +1,1233 @@
+# ============================================================================= =
+# prosail
+# Lib_PROSAIL.R
+# ============================================================================= =
+# PROGRAMMERS:
+# Jean-Baptiste FERET <jb.feret@teledetection.fr >
+# Florian de BOISSIEU <fdeboiss@gmail.com >
+# copyright 2019 / 11 Jean-Baptiste FERET
+# ============================================================================= =
+# This Library includes functions dedicated to PROSAIL simulation
+# SAIL versions available are 4SAIL and 4SAIL2
+# ============================================================================= =
+
+#" computes bidirectional reflectance factor based on outputs from PROSAIL and sun position
+#"
+#" The direct and diffuse light are taken into account as proposed by:
+#" Francois et al. (2002) conversion of 400-1100 nm vegetation albedo
+#" measurements into total shortwave broadband albedo using a canopy
+#" radiative transfer model,  Agronomie
+#" Es = direct
+#" Ed = diffuse
+#"
+#" @param rdot numeric. Hemispherical-directional reflectance factor in viewing direction
+#" @param rsot numeric. Bi-directional reflectance factor
+#" @param tts numeric. Solar zenith angle
+#" @param specatm_sensor list. direct and diffuse radiation for clear conditions
+#" @return brf numeric. Bidirectional reflectance factor
+#" @export
+compute_brf  <- function(rdot, rsot, tts, specatm_sensor) {
+
+  ############################## #
+  ##	direct  /  diffuse light	##
+  ############################## #
+  es <- specatm_sensor$Direct_Light
+  ed <- specatm_sensor$Diffuse_Light
+  rd <- pi / 180
+  skyl <- 0.847 - 1.61 * sin((90 - tts) * rd) +  1.04 * sin((90 - tts) * rd) * sin((90 - tts) * rd) # diffuse radiation (Francois et al.,  2002)
+  pardiro <- (1 - skyl) * es
+  pardifo <- skyl * ed
+  brf <- (rdot * pardifo + rsot * pardiro) / (pardiro + pardifo)
+  return(brf)
+}
+
+#" Performs PROSAIL simulation based on a set of combinations of input parameters
+#" @param spec_sensor list. Includes optical constants required for PROSPECT
+#" refractive index,  specific absorption coefficients and corresponding spectral bands
+#" @param input_prospect  list. PROSPECT input variables
+#" @param n numeric. Leaf structure parameter
+#" @param chl numeric. chlorophyll content (microg.cm-2)
+#" @param car numeric. carotenoid content (microg.cm-2)
+#" @param ant numeric. anthocyain content (microg.cm-2)
+#" @param brown numeric. brown pigment content (Arbitrary units)
+#" @param ewt numeric. Equivalent Water Thickness (g.cm-2)
+#" @param lma numeric. Leaf Mass per Area (g.cm-2)
+#" @param prot numeric. protein content  (g.cm-2)
+#" @param cbc numeric. nonprotcarbon-based constituent content (g.cm-2)
+#" @param alpha numeric. Solid angle for incident light at surface of leaf (simulation of roughness)
+#" @param typelidf numeric. Type of leaf inclination distribution function
+#" @param lidfa numeric.
+#" if typelidf  == 1,  controls the average leaf slope
+#" if typelidf  == 2,  corresponds to average leaf angle
+#" @param lidfb numeric.
+#" if typelidf  == 1,  unused
+#" if typelidf  == 2,  controls the distribution"s bimodality
+#" @param lai numeric. Leaf Area Index
+#" @param q numeric. Hot Spot parameter
+#" @param tts numeric. Sun zeith angle
+#" @param tto numeric. Observer zeith angle
+#" @param psi numeric. Azimuth Sun  /  Observer
+#" @param rsoil numeric. Soil reflectance
+#" @param fraction_brown numeric. Fraction of brown leaf area
+#" @param diss numeric. Layer dissociation factor
+#" @param cv numeric. vertical crown cover percentage
+#" = % ground area covered with crowns as seen from nadir direction
+#" @param zeta numeric. Tree shape factor
+#" = ratio of crown diameter to crown height
+#" @param sailversion character. choose between 4SAIL and 4SAIL2
+#" @param brownvegetation list. Defines optical properties for brown vegetation,  if not nULL
+#" - WVL,  reflectance,  Transmittance
+#" - Set to nULL if use PROSPECT to generate it
+#"
+#" @return list. rdot, rsot, rddt, rsdt
+#" rdot: hemispherical-directional reflectance factor in viewing direction
+#" rsot: bi-directional reflectance factor
+#" rsdt: directional-hemispherical reflectance factor for solar incident flux
+#" rddt: bi-hemispherical reflectance factor
+#" @import prospect
+#" @export
+pro4sail  <- function(spec_sensor, input_prospect = nULL, n = 1.5, chl = 40.0,
+                     car = 8.0, ant = 0.0, brown = 0.0, ewt = 0.01,
+                     lma = 0.008, prot = 0.0, cbc = 0.0, alpha = 40.0,
+                     typelidf = 2, lidfa = nULL, lidfb = nULL, lai = nULL,
+                     q = nULL, tts = nULL, tto = nULL, psi = nULL, rsoil = nULL,
+                     fraction_brown = 0.0,  diss = 0.0,  cv = 1, zeta = 1,
+                     sailversion = "4SAIL", brownvegetation = nULL) {
+
+  ############################ #
+  #	LEAF OPTICAL PROPERTIES	##
+  ############################ #
+  if (is.null(input_prospect)) {
+    input_prospect <- data.frame("chl" = chl, "car" = car, "ant" = ant, "brown" = brown, "ewt" = ewt,
+                                "lma" = lma, "prot" = prot, "cbc" = cbc, "n" = n, "alpha" = alpha)
+  }
+  greenvegetation <- prospect::PROSPECT(SpecPROSPECT = spec_sensor,
+                                        n = input_prospect$n[1],
+                                        chl = input_prospect$chl[1],
+                                        car = input_prospect$car[1],
+                                        ant = input_prospect$ant[1],
+                                        brown = input_prospect$brown[1],
+                                        ewt = input_prospect$ewt[1],
+                                        lma = input_prospect$lma[1],
+                                        prot = input_prospect$prot[1],
+                                        cbc = input_prospect$cbc[1],
+                                        alpha = input_prospect$alpha[1])
+
+  if (sailversion  ==  "4SAIL2") {
+    # 4SAIL2 requires one of the following combination of input parameters
+    # Case #1: valid optical properties for brown vegetation
+    if (!is.null(brownvegetation)) {
+      # need to define reflectance and Transmittance for brownvegetation
+      if (length(grep("reflectance", names(brownvegetation))) == 0 || length(grep("Transmittance", names(brownvegetation))) == 0) {
+        message("Please define brownvegetation as a list including reflectance and Transmittance")
+        stop()
+      }
+      # check if spectral domain for optical properties of brown vegetation match
+      # with spectral domain for optical properties of green vegetation
+      if (length(setdiff(spec_sensor$lambda, brownvegetation$wvl)) > 0) {
+        message("Please define same spectral domain for brownvegetation and SpecPROSPECT")
+        stop()
+      }
+      if (length(unique(lengths(input_prospect))) == 1) {
+        if (!unique(lengths(input_prospect)) == 1) {
+          message("brownvegetation defined along with multiple leaf chemical properties")
+          message("Only first set of leaf chemical properties will be used to simulate green vegetation")
+        }
+      }
+    # if no leaf optical properties brown vegetation defined
+    } else if (is.null(brownvegetation)) {
+      # if all PROSPECT input parameters have the same length
+      if (length(unique(lengths(input_prospect))) == 1) {
+        # if all PROSPECT input parameters are unique (no possibility to simulate 2 types of leaf optics)
+        if (unique(lengths(input_prospect)) == 1) {
+          # if fraction_brown set to 0,  then assign green vegetation optics to brown vegetation optics
+          if (fraction_brown == 0) {
+            brownvegetation <- greenvegetation
+          # else run 4SAIL
+          } else {
+            message("4SAIL2 needs two sets of optical properties for green and brown vegetation")
+            message("Currently one set is defined. will run 4SAIL instead of 4SAIL2")
+            sailversion <- "4SAIL"
+          }
+        # if all PROSPECT parameters have at least 2 elements
+        } else if (unique(lengths(input_prospect)) >= 2) {
+          # compute leaf optical properties
+          brownvegetation <- prospect::PROSPECT(SpecPROSPECT = spec_sensor,
+                                                n = input_prospect$n[2],
+                                                chl = input_prospect$chl[2],
+                                                car = input_prospect$car[2],
+                                                ant = input_prospect$ant[2],
+                                                brown = input_prospect$brown[2],
+                                                ewt = input_prospect$ewt[2],
+                                                lma = input_prospect$lma[2],
+                                                prot = input_prospect$prot[2],
+                                                cbc = input_prospect$cbc[2],
+                                                alpha = input_prospect$alpha[2])
+          if (unique(lengths(input_prospect)) > 2) {
+            message("4SAIL2 needs two sets of optical properties for green and brown vegetation")
+            message("Currently more than 2 sets are defined. will only use the first 2")
+          }
+        }
+      }
+    }
+  }
+  if (sailversion  ==  "4SAIL") {
+    if (length(unique(lengths(input_prospect))) == 1) {
+      if (unique(lengths(input_prospect)) > 1) {
+        message("4SAIL needs only one set of optical properties")
+        message("Currently more than one set of leaf chemical constituents is defined.")
+        message("Will run 4SAIL with the first set of leaf chemical constituents")
+      }
+    }
+  }
+
+  if (sailversion  ==  "4SAIL") {
+    # run 4SAIL
+    ref <- foursail(leafoptics = greenvegetation,
+                    typelidf,  lidfa,  lidfb,  lai,  q,  tts,  tto,  psi,  rsoil)
+  } else if (sailversion  ==  "4SAIL2") {
+    # run 4SAIL2
+    ref <- foursail2(leafgreen = greenvegetation,  leafbrown = brownvegetation,
+                     typelidf,  lidfa,  lidfb,  lai,  q,  tts,  tto,  psi,  rsoil,
+                     fraction_brown,  diss,  cv,  zeta)
+  }
+  return(ref)
+}
+
+#" Performs PROSAIL simulation based on a set of combinations of input parameters
+#" @param leafoptics list. Includes leaf optical properties (reflectance and transmittance)
+#" and corresponding spectral bands
+#" @param typelidf numeric. Type of leaf inclination distribution function
+#" @param lidfa numeric.
+#" if typelidf  == 1,  controls the average leaf slope
+#" if typelidf  == 2,  corresponds to average leaf angle
+#" @param lidfb numeric.
+#" if typelidf  == 1,  unused
+#" if typelidf  == 2,  controls the distribution"s bimodality
+#" @param lai numeric. Leaf Area Index
+#" @param q numeric. Hot Spot parameter
+#" @param tts numeric. Sun zeith angle
+#" @param tto numeric. Observer zeith angle
+#" @param psi numeric. Azimuth Sun  /  Observer
+#" @param rsoil numeric. Soil reflectance
+#"
+#" @return list. rdot, rsot, rddt, rsdt
+#" rdot: hemispherical-directional reflectance factor in viewing direction
+#" rsot: bi-directional reflectance factor
+#" rsdt: directional-hemispherical reflectance factor for solar incident flux
+#" rddt: bi-hemispherical reflectance factor
+#" @export
+
+foursail  <- function(leafoptics,  typelidf = 2,  lidfa = nULL,  lidfb = nULL,  lai = nULL,
+                      q = nULL,  tts = nULL,  tto = nULL,  psi = nULL,  rsoil = nULL) {
+
+  ############################## #
+  #	LEAF OPTICAL PROPERTIES	##
+  ############################## #
+  rho <- leafoptics$Reflectance
+  tau <- leafoptics$Transmittance
+
+  #	Geometric quantities
+  rd <- pi / 180
+  cts <- cos(rd * tts)
+  cto <- cos(rd * tto)
+  ctscto <- cts * cto
+  tants <- tan(rd * tts)
+  tanto <- tan(rd * tto)
+  cospsi <- cos(rd * psi)
+  dso <- sqrt(tants * tants + tanto * tanto - 2. * tants * tanto * cospsi)
+
+  #	Generate leaf angle distribution from average leaf angle (ellipsoidal) or (a, b) parameters
+  if (typelidf == 1) {
+    foliar_distrib <- dladgen(lidfa, lidfb)
+    lidf <- foliar_distrib$lidf
+    litab <- foliar_distrib$litab
+
+  } else if (typelidf == 2) {
+    foliar_distrib <- campbell(lidfa)
+    lidf <- foliar_distrib$lidf
+    litab <- foliar_distrib$litab
+  }
+
+  # angular distance,  compensation of shadow length
+  #	Calculate geometric factors associated with extinction and scattering
+  #	Initialise sums
+  ks <- 0
+  ko <- 0
+  bf <- 0
+  sob <- 0
+  sof <- 0
+
+  #	Weighted sums over LIDF
+  na <- length(litab)
+  for (i in 1:na) {
+    ttl <- litab[i]	    # leaf inclination discrete values
+    ctl <- cos(rd * ttl)
+    #	SAIL volume scattering phase function gives interception and portions to be
+    #	multiplied by rho and tau
+    resvolscatt <- volscatt(tts, tto, psi, ttl)
+    chi_s <- resvolscatt$chi_s
+    chi_o <- resvolscatt$chi_o
+    frho <- resvolscatt$frho
+    ftau <- resvolscatt$ftau
+
+    #********************************************************************************
+    #*                   SUITS SYSTEM coEFFICIEnTS
+    #*
+    #*	ks  : Extinction coefficient for direct solar flux
+    #*	ko  : Extinction coefficient for direct observed flux
+    #*	att : Attenuation coefficient for diffuse flux
+    #*	sigb : Backscattering coefficient of the diffuse downward flux
+    #*	sigf : Forwardscattering coefficient of the diffuse upward flux
+    #*	sf  : Scattering coefficient of the direct solar flux for downward diffuse flux
+    #*	sb  : Scattering coefficient of the direct solar flux for upward diffuse flux
+    #*	vf   : Scattering coefficient of upward diffuse flux in the observed direction
+    #*	vb   : Scattering coefficient of downward diffuse flux in the observed direction
+    #*	w   : Bidirectional scattering coefficient
+    #********************************************************************************
+
+    #	Extinction coefficients
+    ksli <- chi_s / cts
+    koli <- chi_o / cto
+
+    #	Area scattering coefficient fractions
+    sobli <- frho * pi / ctscto
+    sofli <- ftau * pi / ctscto
+    bfli <- ctl * ctl
+    ks <- ks + ksli * lidf[i]
+    ko <- ko + koli * lidf[i]
+    bf <- bf + bfli * lidf[i]
+    sob <- sob + sobli * lidf[i]
+    sof <- sof + sofli * lidf[i]
+  }
+
+  #	Geometric factors to be used later with rho and tau
+  sdb <- 0.5 * (ks + bf)
+  sdf <- 0.5 * (ks - bf)
+  dob <- 0.5 * (ko + bf)
+  dof <- 0.5 * (ko - bf)
+  ddb <- 0.5 * (1. + bf)
+  ddf <- 0.5 * (1. - bf)
+
+  #	Here rho and tau come in
+  sigb <- ddb * rho + ddf * tau
+  sigf <- ddf * rho + ddb * tau
+  att <- 1 - sigf
+  m2 <- (att + sigb) * (att - sigb)
+  m2[which(m2 <= 0)] <- 0
+  m <- sqrt(m2)
+
+  sb <- sdb * rho + sdf * tau
+  sf <- sdf * rho + sdb * tau
+  vb <- dob * rho + dof * tau
+  vf <- dof * rho + dob * tau
+  w <- sob * rho + sof * tau
+
+  #	Here the LAI comes in
+  #   Outputs for the case LAI = 0
+  if (lai < 0) {
+    tss <- 1
+    too <- 1
+    tsstoo <- 1
+    rdd <- 0
+    tdd <- 1
+    rsd <- 0
+    tsd <- 0
+    rdo <- 0
+    tdo <- 0
+    rso <- 0
+    rsos <- 0
+    rsod <- 0
+
+    rddt <- rsoil
+    rsdt <- rsoil
+    rdot <- rsoil
+    rsodt <- 0 * rsoil
+    rsost <- rsoil
+    rsot <- rsoil
+  } else {
+    #	Other cases (LAI  >  0)
+    e1 <- exp(-m * lai)
+    e2 <- e1 * e1
+    rinf <- (att - m) / sigb
+    rinf2 <- rinf * rinf
+    re <- rinf * e1
+    denom <- 1. - rinf2 * e2
+
+    j1ks <- jfunc1(ks, m, lai)
+    j2ks <- jfunc2(ks, m, lai)
+    j1ko <- jfunc1(ko, m, lai)
+    j2ko <- jfunc2(ko, m, lai)
+
+    ps <- (sf + sb * rinf) * j1ks
+    qs <- (sf * rinf + sb) * j2ks
+    pv <- (vf + vb * rinf) * j1ko
+    qv <- (vf * rinf + vb) * j2ko
+
+    rdd <- rinf * (1. - e2) / denom
+    tdd <- (1. - rinf2) * e1 / denom
+    tsd <- (ps - re * qs) / denom
+    rsd <- (qs - re * ps) / denom
+    tdo <- (pv - re * qv) / denom
+    rdo <- (qv - re * pv) / denom
+
+    tss <- exp(-ks * lai)
+    too <- exp(-ko * lai)
+    z <- jfunc3(ks, ko, lai)
+    g1 <- (z - j1ks * too) / (ko + m)
+    g2 <- (z - j1ko * tss) / (ks + m)
+
+    tv1 <- (vf * rinf + vb) * g1
+    tv2 <- (vf + vb * rinf) * g2
+    t1 <- tv1 * (sf + sb * rinf)
+    t2 <- tv2 * (sf * rinf + sb)
+    t3 <- (rdo * qs + tdo * ps) * rinf
+
+    #	Multiple scattering contribution to bidirectional canopy reflectance
+    rsod <- (t1 + t2 - t3) / (1. - rinf2)
+
+    #	Treatment of the hotspot-effect
+    alf <- 1e6
+    #	Apply correction 2 / (K + k) suggested by F.-M. Breon
+    if (q > 0) {
+      alf <- (dso / q) * 2. / (ks + ko)
+    }
+    if (alf > 200) {
+      # inserted H. Bach 1 / 3 / 04
+      alf <- 200
+    }
+    if (alf == 0) {
+      #	The pure hotspot - no shadow
+      tsstoo <- tss
+      sumint <- (1 - tss) / (ks * lai)
+    } else {
+      #	Outside the hotspot
+      fhot <- lai * sqrt(ko * ks)
+      #	Integrate by exponential Simpson method in 20 steps
+      #	the steps are arranged according to equal partitioning
+      #	of the slope of the joint probability function
+      x1 <- 0
+      y1 <- 0
+      f1 <- 1
+      fint <- (1. - exp(-alf)) * 0.05
+      sumint <- 0
+      for (i in 1:20) {
+        if (i < 20) {
+          x2 <- -log(1. - i * fint) / alf
+        } else {
+          x2 <- 1
+        }
+        y2 <- -(ko + ks) * lai * x2 + fhot * (1. - exp(-alf * x2)) / alf
+        f2 <- exp(y2)
+        sumint <- sumint + (f2 - f1) * (x2 - x1) / (y2 - y1)
+        x1 <- x2
+        y1 <- y2
+        f1 <- f2
+      }
+      tsstoo <- f1
+    }
+    #	Bidirectional reflectance
+    #	Single scattering contribution
+    rsos <- w * lai * sumint
+    #	Total canopy contribution
+    rso <- rsos + rsod
+    #	Interaction with the soil
+    dn <- 1. - rsoil * rdd
+    # rddt: bi-hemispherical reflectance factor
+    rddt <- rdd + tdd * rsoil * tdd / dn
+    # rsdt: directional-hemispherical reflectance factor for solar incident flux
+    rsdt <- rsd + (tsd + tss) * rsoil * tdd / dn
+    # rdot: hemispherical-directional reflectance factor in viewing direction
+    rdot <- rdo + tdd * rsoil * (tdo + too) / dn
+    # rsot: bi-directional reflectance factor
+    rsodt <- rsod + ((tss + tsd) * tdo + (tsd + tss * rsoil * rdd) * too) * rsoil / dn
+    rsost <- rsos + tsstoo * rsoil
+    rsot <- rsost + rsodt
+  }
+  my_list <- list("rdot" = rdot, "rsot" = rsot, "rddt" = rddt, "rsdt" = rsdt)
+  return(my_list)
+}
+
+#" Performs pro4sail2 simulation based on a set of combinations of input parameters
+#" @param leafgreen list. includes relfectance and transmittance for vegetation #1 (e.g. green vegetation)
+#" @param leafbrown list. includes relfectance and transmittance for vegetation #2 (e.g. brown vegetation)
+#" @param typelidf numeric. Type of leaf inclination distribution function
+#" @param lidfa numeric.
+#" if typelidf  == 1,  controls the average leaf slope
+#" if typelidf  == 2,  corresponds to average leaf angle
+#" @param lidfb numeric.
+#" if typelidf  == 1,  unused
+#" if typelidf  == 2,  controls the distribution"s bimodality
+#" @param lai numeric. Leaf Area Index
+#" @param hot numeric. Hot Spot parameter = ratio of the correlation length of leaf projections in the horizontal plane and the canopy height (doi:10.1016 / j.rse.2006.12.013)
+#" @param tts numeric. Sun zeith angle
+#" @param tto numeric. Observer zeith angle
+#" @param psi numeric. Azimuth Sun  /  Observer
+#" @param rsoil numeric. Soil reflectance
+#" @param fraction_brown numeric. Fraction of brown leaf area
+#" @param diss numeric. Layer dissociation factor
+#" @param cv numeric. vertical crown cover percentage
+#" = % ground area covered with crowns as seen from nadir direction
+#" @param zeta numeric. Tree shape factor
+#" = ratio of crown diameter to crown height
+#"
+#" @return list. rdot, rsot, rddt, rsdt
+#" rdot: hemispherical-directional reflectance factor in viewing direction
+#" rsot: bi-directional reflectance factor
+#" rsdt: directional-hemispherical reflectance factor for solar incident flux
+#" rddt: bi-hemispherical reflectance factor
+#" alfast: canopy absorptance for direct solar incident flux
+#" alfadt: canopy absorptance for hemispherical diffuse incident flux
+#" @export
+
+foursail2  <- function(leafgreen,  leafbrown,
+                       typelidf = 2, lidfa = nULL, lidfb = nULL,
+                       lai = nULL,  hot = nULL, tts = nULL, tto = nULL, psi = nULL, rsoil = nULL,
+                       fraction_brown = 0.5,  diss = 0.5,  cv = 1, zeta = 1) {
+
+  #	This version does not include non-Lambertian soil properties.
+  #	original codes do,  and only need to add the following variables as input
+  rddsoil <- rdosoil <- rsdsoil <- rsosoil <- rsoil
+
+  #	Geometric quantities
+  rd <- pi / 180
+
+  #	Generate leaf angle distribution from average leaf angle (ellipsoidal) or (a, b) parameters
+  if (typelidf == 1) {
+    foliar_distrib <- dladgen(lidfa, lidfb)
+    lidf <- foliar_distrib$lidf
+    litab <- foliar_distrib$litab
+
+  } else if (typelidf == 2) {
+    foliar_distrib <- campbell(lidfa)
+    lidf <- foliar_distrib$lidf
+    litab <- foliar_distrib$litab
+  }
+
+  if (lai < 0) {
+    message("Please define positive LAI value")
+    rddt <- rsdt <- rdot <- rsost <- rsot <- rsoil
+    alfast <- alfadt <- 0 * rsoil
+  } else if (lai == 0) {
+    tss <- too <- tsstoo <- tdd <- 1.0
+    rdd <- rsd <- tsd <- rdo <- tdo <- 0.0
+    rso <- rsos <- rsod <- rsodt <- 0.0
+    rddt <- rsdt <- rdot <- rsost <- rsot <- rsoil
+    alfast <- alfadt <- 0 * rsoil
+  } else if (lai > 0) {
+    cts <- cos(rd * tts)
+    cto <- cos(rd * tto)
+    ctscto <- cts * cto
+    tants <- tan(rd * tts)
+    tanto <- tan(rd * tto)
+    cospsi <- cos(rd * psi)
+    dso <- sqrt(tants * tants + tanto * tanto - 2.0 * tants * tanto * cospsi)
+
+    # Clumping effects
+    cs <- co <- 1.0
+    if (cv <= 1.0) {
+      cs <- 1.0 - (1.0 - cv)^(1.0 / cts)
+      co <- 1.0 - (1.0 - cv)^(1.0 / cto)
+    }
+    overlap <- 0.0
+    if (zeta > 0.0) {
+      overlap <- min(cs * (1.0 - co), co * (1.0 - cs)) * exp(-dso / zeta)
+    }
+    fcd <- cs * co + overlap
+    fcs <- (1.0 - cs) * co - overlap
+    fod <- cs * (1.0 - co) - overlap
+    fos <- (1.0 - cs) * (1.0 - co) + overlap
+    fcdc <- 1.0 - (1.0 - fcd)^(0.5 / cts + 0.5 / cto)
+
+    #	Part depending on diss,  fraction_brown,  and leaf optical properties
+    #	First save the input fraction_brown as the old fraction_brown,  as the following change is only artificial
+    # Better define an fraction_brown that is actually used: fb,  so that the input is not modified!
+
+    fb <- fraction_brown
+    # if only green leaves
+    if (fraction_brown == 0.0) {
+      fb <- 0.5
+      leafbrown$Reflectance <- leafgreen$Reflectance
+      leafbrown$Transmittance <- leafgreen$Transmittance
+    }
+    if (fraction_brown == 1.0) {
+      fb <- 0.5
+      leafgreen$Reflectance <- leafbrown$Reflectance
+      leafgreen$Transmittance <- leafbrown$Transmittance
+    }
+    s <- (1.0 - diss) * fb * (1.0 - fb)
+    # rho1 && tau1 : green foliage
+    # rho2 && tau2 : brown foliage (bottom layer)
+    rho1 <- ((1 - fb - s) * leafgreen$Reflectance + s * leafbrown$Reflectance) / (1 - fb)
+    tau1 <- ((1 - fb - s) * leafgreen$Transmittance + s * leafbrown$Transmittance) / (1 - fb)
+    rho2 <- (s * leafgreen$Reflectance + (fb - s) * leafbrown$Reflectance) / fb
+    tau2 <- (s * leafgreen$Transmittance + (fb - s) * leafbrown$Transmittance) / fb
+
+    # angular distance,  compensation of shadow length
+    #	Calculate geometric factors associated with extinction and scattering
+    #	Initialise sums
+    ks <- ko <- bf <- sob <- sof <- 0
+
+    # Weighted sums over LIDF
+
+    for (i in 1:seq_along(litab)) {
+      ttl <- litab[i]
+      ctl <- cos(rd * ttl)
+      # SAIL volscatt function gives interception coefficients
+      # and two portions of the volume scattering phase function to be
+      # multiplied by rho and tau,  respectively
+      resvolscatt <- volscatt(tts, tto, psi, ttl)
+      chi_s <- resvolscatt$chi_s
+      chi_o <- resvolscatt$chi_o
+      frho <- resvolscatt$frho
+      ftau <- resvolscatt$ftau
+      # Extinction coefficients
+      ksli <- chi_s / cts
+      koli <- chi_o / cto
+      # Area scattering coefficient fractions
+      sobli <- frho * pi / ctscto
+      sofli <- ftau * pi / ctscto
+      bfli <- ctl * ctl
+      ks <- ks + ksli * lidf[i]
+      ko <- ko + koli * lidf[i]
+      bf <- bf + bfli * lidf[i]
+      sob <- sob + sobli * lidf[i]
+      sof <- sof + sofli * lidf[i]
+    }
+    # Geometric factors to be used later in combination with rho and tau
+    sdb <- 0.5 * (ks + bf)
+    sdf <- 0.5 * (ks - bf)
+    dob <- 0.5 * (ko + bf)
+    dof <- 0.5 * (ko - bf)
+    ddb <- 0.5 * (1. + bf)
+    ddf <- 0.5 * (1. - bf)
+
+    # LAIs in two layers
+    lai1 <- (1 - fb) * lai
+    lai2 <- fb * lai
+
+    tss <- exp(-ks * lai)
+    ck <- exp(-ks * lai1)
+    alf <- 1e6
+    if (hot > 0.0) {
+      alf <- (dso / hot) * 2.0 / (ks + ko)
+    }
+    if (alf > 200.0) {
+      alf <- 200.0     # inserted H. Bach 1 / 3 / 04
+    }
+    if (alf == 0.0) {
+      # The pure hotspot
+      tsstoo <- tss
+      s1 <- (1 - ck) / (ks * lai)
+      s2 <- (ck - tss) / (ks * lai)
+    } else {
+      # Outside the hotspot
+      fhot <- lai * sqrt(ko * ks)
+      # Integrate 2 layers by exponential simpson method in 20 steps
+      # the steps are arranged according to equal partitioning
+      # of the derivative of the joint probability function
+      x1 <- y1 <- 0.0
+      f1 <- 1.0
+      ca <- exp(alf * (fb - 1.0))
+      fint <- (1.0 - ca) * .05
+      s1 <- 0.0
+      for (istep in 1:20) {
+        if (istep < 20) {
+          x2 <- -log(1. - istep * fint) / alf
+        } else {
+          x2 <- 1. - fb
+        }
+        y2 <- -(ko + ks) * lai * x2 + fhot * (1.0 - exp(-alf * x2)) / alf
+        f2 <- exp(y2)
+        s1 <- s1 + (f2 - f1) * (x2 - x1) / (y2 - y1)
+        x1 <- x2
+        y1 <- y2
+        f1 <- f2
+      }
+      fint <- (ca - exp(-alf)) * .05
+      s2 <- 0.0
+      for (istep in 1:20) {
+        if (istep < 20) {
+          x2 <- -log(ca - istep * fint) / alf
+        } else {
+          x2 <- 1.0
+        }
+        y2 <- -(ko + ks) * lai * x2 + fhot * (1.0 - exp(-alf * x2)) / alf
+        f2 <- exp(y2)
+        s2 <- s2 + (f2 - f1) * (x2 - x1) / (y2 - y1)
+        x1 <- x2
+        y1 <- y2
+        f1 <- f2
+      }
+      tsstoo <- f1
+    }
+
+    # Calculate reflectances and transmittances
+    # Bottom layer
+    tss <- exp(-ks * lai2)
+    too <- exp(-ko * lai2)
+    sb <- sdb * rho2 + sdf * tau2
+    sf <- sdf * rho2 + sdb * tau2
+
+    vb <- dob * rho2 + dof * tau2
+    vf <- dof * rho2 + dob * tau2
+
+    w2 <- sob * rho2 + sof * tau2
+
+    sigb <- ddb * rho2 + ddf * tau2
+    sigf <- ddf * rho2 + ddb * tau2
+    att <- 1.0 - sigf
+    m2 <- (att + sigb) * (att - sigb)
+    m2[m2 < 0] <- 0
+    m <- sqrt(m2)
+    which_ncs <- which(m > 0.01)
+    which_cs <- which(m <= 0.01)
+
+    tdd <- rdd <- tsd <- rsd <- tdo <- rdo <- 0 * m
+    rsod <- 0 * m
+    if (length(which_ncs) > 0) {
+      resncs <- nonconservativescattering(m[which_ncs], lai2, att[which_ncs], sigb[which_ncs],
+                                          ks, ko, sf[which_ncs], sb[which_ncs], vf[which_ncs], vb[which_ncs], tss, too)
+      tdd[which_ncs] <- resncs$tdd
+      rdd[which_ncs] <- resncs$rdd
+      tsd[which_ncs] <- resncs$tsd
+      rsd[which_ncs] <- resncs$rsd
+      tdo[which_ncs] <- resncs$tdo
+      rdo[which_ncs] <- resncs$rdo
+      rsod[which_ncs] <- resncs$rsod
+    }
+    if (length(which_cs) > 0) {
+      rescs <- conservativescattering(m[which_cs], lai2, att[which_cs], sigb[which_cs],
+                                      ks, ko, sf[which_cs], sb[which_cs], vf[which_cs], vb[which_cs], tss, too)
+      tdd[which_cs] <- rescs$tdd
+      rdd[which_cs] <- rescs$rdd
+      tsd[which_cs] <- rescs$tsd
+      rsd[which_cs] <- rescs$rsd
+      tdo[which_cs] <- rescs$tdo
+      rdo[which_cs] <- rescs$rdo
+      rsod[which_cs] <- rescs$rsod
+    }
+
+    # Set background properties equal to those of the bottom layer on a black soil
+    rddb <- rdd
+    rsdb <- rsd
+    rdob <- rdo
+    rsodb <- rsod
+    tddb <- tdd
+    tsdb <- tsd
+    tdob <- tdo
+    toob <- too
+    tssb <- tss
+    # Top layer
+    tss <- exp(-ks * lai1)
+    too <- exp(-ko * lai1)
+
+    sb <- sdb * rho1 + sdf * tau1
+    sf <- sdf * rho1 + sdb * tau1
+
+    vb <- dob * rho1 + dof * tau1
+    vf <- dof * rho1 + dob * tau1
+
+    w1 <- sob * rho1 + sof * tau1
+
+    sigb <- ddb * rho1 + ddf * tau1
+    sigf <- ddf * rho1 + ddb * tau1
+    att <- 1.0 - sigf
+
+    m2 <- (att + sigb) * (att - sigb)
+    m2[m2 < 0] <- 0
+    m <- sqrt(m2)
+    which_ncs <- which(m > 0.01)
+    which_cs <- which(m <= 0.01)
+
+    tdd <- rdd <- tsd <- rsd <- tdo <- rdo <- 0 * m
+    rsod <- 0 * m
+    if (length(which_ncs) > 0) {
+      resncs <- nonconservativescattering(m[which_ncs], lai1, att[which_ncs], sigb[which_ncs],
+                                          ks, ko, sf[which_ncs], sb[which_ncs], vf[which_ncs], vb[which_ncs], tss, too)
+      tdd[which_ncs] <- resncs$tdd
+      rdd[which_ncs] <- resncs$rdd
+      tsd[which_ncs] <- resncs$tsd
+      rsd[which_ncs] <- resncs$rsd
+      tdo[which_ncs] <- resncs$tdo
+      rdo[which_ncs] <- resncs$rdo
+      rsod[which_ncs] <- resncs$rsod
+    }
+    if (length(which_cs) > 0) {
+      rescs <- conservativescattering(m[which_cs], lai1, att[which_cs], sigb[which_cs],
+                                      ks, ko, sf[which_cs], sb[which_cs], vf[which_cs], vb[which_cs], tss, too)
+      tdd[which_cs] <- rescs$tdd
+      rdd[which_cs] <- rescs$rdd
+      tsd[which_cs] <- rescs$tsd
+      rsd[which_cs] <- rescs$rsd
+      tdo[which_cs] <- rescs$tdo
+      rdo[which_cs] <- rescs$rdo
+      rsod[which_cs] <- rescs$rsod
+    }
+
+    # combine with bottom layer reflectances and transmittances (adding method)
+    rn <- 1.0 - rdd * rddb
+    tup <- (tss * rsdb + tsd * rddb) / rn
+    tdn <- (tsd + tss * rsdb * rdd) / rn
+    rsdt <- rsd + tup * tdd
+    rdot <- rdo + tdd * (rddb * tdo + rdob * too) / rn
+    rsodt <- rsod + (tss * rsodb + tdn * rdob) * too + tup * tdo
+
+    rsost <- (w1 * s1 + w2 * s2) * lai
+
+    rsot <- rsost + rsodt
+
+    # Diffuse reflectances at the top and the bottom are now different
+    rddt_t <- rdd + tdd * rddb * tdd / rn
+    rddt_b <- rddb + tddb * rdd * tddb / rn
+
+    # Transmittances of the combined canopy layers
+    tsst <- tss * tssb
+    toot <- too * toob
+    tsdt <- tss * tsdb + tdn * tddb
+    tdot <- tdob * too + tddb * (tdo + rdd * rdob * too) / rn
+    tddt <- tdd * tddb / rn
+
+    # Apply clumping effects to vegetation layer
+    rddcb <- cv * rddt_b
+    rddct <- cv * rddt_t
+    tddc <- 1 - cv + cv * tddt
+    rsdc <- cs * rsdt
+    tsdc <- cs * tsdt
+    rdoc <- co * rdot
+    tdoc <- co * tdot
+    tssc <- 1 - cs + cs * tsst
+    tooc <- 1 - co + co * toot
+
+    # new weight function fcdc for crown contribution (W. Verhoef,  22-05-08)
+    rsoc <- fcdc * rsot
+    tssooc <- fcd * tsstoo + fcs * toot + fod * tsst + fos
+    # Canopy absorptance for black background (W. Verhoef,  02-03-04)
+    alfas <- 1. - tssc - tsdc - rsdc
+    alfad <- 1. - tddc - rddct
+    # Add the soil background
+    rn <- 1 - rddcb * rddsoil
+    tup <- (tssc * rsdsoil + tsdc * rddsoil) / rn
+    tdn <- (tsdc + tssc * rsdsoil * rddcb) / rn
+
+    rddt <- rddct + tddc * rddsoil * tddc / rn
+    rsdt <- rsdc + tup * tddc
+    rdot <- rdoc + tddc * (rddsoil * tdoc + rdosoil * tooc) / rn
+    rsot <- rsoc + tssooc * rsosoil + tdn * rdosoil * tooc + tup * tdoc
+
+    # Effect of soil background on canopy absorptances (W. Verhoef,  02-03-04)
+    alfast <- alfas + tup * alfad
+    alfadt <- alfad * (1. + tddc * rddsoil / rn)
+  }
+  my_list <- list("rdot" = rdot, "rsot" = rsot, "rddt" = rddt, "rsdt" = rsdt,
+                  "alfast" = alfast,  "alfadt" = alfadt)
+  return(my_list)
+}
+
+
+
+#" computes non conservative scattering conditions
+#" @param m numeric.
+#" @param lai numeric. Leaf Area Index
+#" @param att numeric.
+#" @param sigb numeric.
+#" @param ks numeric.
+#" @param ko numeric.
+#" @param sf numeric.
+#" @param sb numeric.
+#" @param vf numeric.
+#" @param vb numeric.
+#" @param tss numeric.
+#" @param too numeric.
+#"
+#" @return list. tdd,  rdd,  tsd,  rsd,  tdo,  rdo,  rsod
+#"
+#" @export
+nonconservativescattering <- function(m, lai, att, sigb, ks, ko, sf, sb, vf, vb, tss, too) {
+
+  e1 <- exp(-m * lai)
+  e2 <- e1 * e1
+  rinf <- (att - m) / sigb
+  rinf2 <- rinf * rinf
+  re <- rinf * e1
+  denom <- 1. - rinf2 * e2
+
+  j1ks <- jfunc1(ks, m, lai)
+  j2ks <- jfunc2(ks, m, lai)
+  j1ko <- jfunc1(ko, m, lai)
+  j2ko <- jfunc2(ko, m, lai)
+
+  ps <- (sf + sb * rinf) * j1ks
+  qs <- (sf * rinf + sb) * j2ks
+  pv <- (vf + vb * rinf) * j1ko
+  qv <- (vf * rinf + vb) * j2ko
+
+  tdd <- (1. - rinf2) * e1 / denom
+  rdd <- rinf * (1. - e2) / denom
+  tsd <- (ps - re * qs) / denom
+  rsd <- (qs - re * ps) / denom
+  tdo <- (pv - re * qv) / denom
+  rdo <- (qv - re * pv) / denom
+
+  z <- jfunc2(ks, ko, lai)
+  g1 <- (z - j1ks * too) / (ko + m)
+  g2 <- (z - j1ko * tss) / (ks + m)
+
+  tv1 <- (vf * rinf + vb) * g1
+  tv2 <- (vf + vb * rinf) * g2
+
+  t1 <- tv1 * (sf + sb * rinf)
+  t2 <- tv2 * (sf * rinf + sb)
+  t3 <- (rdo * qs + tdo * ps) * rinf
+
+  # Multiple scattering contribution to bidirectional canopy reflectance
+  rsod <- (t1 + t2 - t3) / (1. - rinf2)
+  my_list <- list("tdd" = tdd,  "rdd" = rdd,  "tsd" = tsd,
+                  "rsd" = rsd,  "tdo" = tdo,  "rdo" = rdo,
+                  "rsod" = rsod)
+  return(my_list)
+}
+
+#" computes conservative scattering conditions
+#" @param m numeric.
+#" @param lai numeric. Leaf Area Index
+#" @param att numeric.
+#" @param sigb numeric.
+#" @param ks numeric.
+#" @param ko numeric.
+#" @param sf numeric.
+#" @param sb numeric.
+#" @param vf numeric.
+#" @param vb numeric.
+#" @param tss numeric.
+#" @param too numeric.
+#"
+#" @return list. tdd,  rdd,  tsd,  rsd,  tdo,  rdo,  rsod
+#"
+#" @export
+conservativescattering <- function(m, lai, att, sigb, ks, ko, sf, sb, vf, vb, tss, too) {
+
+  # near or complete conservative scattering
+  j4 <- jfunc4(m, lai)
+  amsig <- att - sigb
+  apsig <- att + sigb
+  rtp <- (1 - amsig * j4) / (1 + amsig * j4)
+  rtm <- (-1 + apsig * j4) / (1 + apsig * j4)
+  rdd <- 0.5 * (rtp + rtm)
+  tdd <- 0.5 * (rtp - rtm)
+
+  dns <- ks * ks - m * m
+  dno <- ko * ko - m * m
+  cks <- (sb * (ks - att) - sf * sigb) / dns
+  cko <- (vb * (ko - att) - vf * sigb) / dno
+  dks <- (-sf * (ks + att) - sb * sigb) / dns
+  dko <- (-vf * (ko + att) - vb * sigb) / dno
+  ho <- (sf * cko + sb * dko) / (ko + ks)
+
+  rsd <- cks * (1 - tss * tdd) - dks * rdd
+  rdo <- cko * (1 - too * tdd) - dko * rdd
+  tsd <- dks * (tss - tdd) - cks * tss * rdd
+  tdo <- dko * (too - tdd) - cko * too * rdd
+  # Multiple scattering contribution to bidirectional canopy reflectance
+  rsod <- ho * (1 - tss * too) - cko * tsd * too - dko * rsd
+
+  my_list <- list("tdd" = tdd,  "rdd" = rdd,  "tsd" = tsd,
+                  "rsd" = rsd,  "tdo" = tdo,  "rdo" = rdo,
+                  "rsod" = rsod)
+  return(my_list)
+}
+
+
+
+
+
+
+#" computes the leaf angle distribution function value (freq)
+#"
+#" Ellipsoidal distribution function characterised by the average leaf
+#" inclination angle in degree (ala)
+#" Campbell 1986
+#" @param ala average leaf angle
+#" @return foliar_distrib list. lidf and litab
+#" @export
+campbell  <- function(ala) {
+
+  tx1 <- c(10., 20., 30., 40., 50., 60., 70., 80., 82., 84., 86., 88., 90.)
+  tx2 <- c(0., 10., 20., 30., 40., 50., 60., 70., 80., 82., 84., 86., 88.)
+  litab <- (tx2 + tx1) / 2
+  n <- length(litab)
+  tl1 <- tx1 * (pi / 180)
+  tl2 <- tx2 * (pi / 180)
+  excent <- exp(-1.6184e-5 * ala**3 + 2.1145e-3 * ala**2 - 1.2390e-1 * ala + 3.2491)
+  sum0 <- 0
+
+  freq <- c()
+  for (i in 1:n) {
+    x1 <- excent / (sqrt(1. + excent**2. * tan(tl1[i])**2))
+    x2 <- excent / (sqrt(1. + excent**2. * tan(tl2[i])**2))
+    if (excent == 1) {
+      freq[i] <- abs(cos(tl1[i]) - cos(tl2[i]))
+    } else {
+      alpha <- excent / sqrt(abs(1 - excent**2))
+      alpha2 <- alpha**2
+      x12 <- x1**2
+      x22 <- x2**2
+      alpx1 <- 0 * alpha2
+      alpx2 <- 0 * alpha2
+      almx1 <- 0 * alpha2
+      almx2 <- 0 * alpha2
+      if (excent > 1) {
+        alpx1 <- sqrt(alpha2[excent > 1] + x12[excent > 1])
+        alpx2[excent > 1] <- sqrt(alpha2[excent > 1] + x22[excent > 1])
+        dum <- x1 * alpx1 + alpha2 * log(x1 + alpx1)
+        freq[i] <- abs(dum - (x2 * alpx2 + alpha2 * log(x2 + alpx2)))
+      } else {
+        almx1 <- sqrt(alpha2 - x12)
+        almx2 <- sqrt(alpha2 - x22)
+        dum <- x1 * almx1 + alpha2 * asin(x1 / alpha)
+        freq[i] <- abs(dum - (x2 * almx2 + alpha2 * asin(x2 / alpha)))
+      }
+    }
+  }
+  sum0 <- sum(freq)
+  freq0 <- freq / sum0
+  foliar_distrib <- list("lidf" = freq0, "litab" = litab)
+  return(foliar_distrib)
+}
+
+#" computes the leaf angle distribution function value (freq)
+#"
+#" Using the original bimodal distribution function initially proposed in SAIL
+#"  references
+#"  ----------
+#"  (Verhoef1998) Verhoef,  Wout. Theory of radiative transfer models applied
+#"  in optical remote sensing of vegetation canopies.
+#"  nationaal Lucht en Ruimtevaartlaboratorium,  1998.
+#"  http: /  / library.wur.nl / WebQuery / clc / 945481.
+#" @param a controls the average leaf slope
+#" @param b controls the distribution"s bimodality
+#" LIDF type 		  a 		b
+#" Planophile 	  1		  0
+#" Erectophile    -1	 	0
+#" Plagiophile 	  0		  -1
+#" Extremophile 	0		  1
+#" Spherical 	    -0.35 -0.15
+#" Uniform        0     0
+#" requirement: ||lidfa||  +  ||lidfb|| < 1
+#"
+#" @return foliar_distrib list. lidf and litab
+#" @export
+dladgen  <- function(a, b) {
+  litab <- c(5., 15., 25., 35., 45., 55., 65., 75., 81., 83., 85., 87., 89.)
+  freq <- c()
+  for (i1 in 1:8) {
+    t <- i1 * 10
+    freq[i1] <- dcum(a, b, t)
+  }
+  for (i2 in 9:12) {
+    t <- 80. + (i2 - 8) * 2.
+    freq[i2] <- dcum(a, b, t)
+  }
+  freq[13] <- 1
+  for (i in 13:2) {
+    freq[i] <- freq[i] - freq[i - 1]
+  }
+  foliar_distrib <- list("lidf" = freq, "litab" = litab)
+  return(foliar_distrib)
+}
+
+#" dcum function
+#" @param a numeric. controls the average leaf slope
+#" @param b numeric. controls the distribution"s bimodality
+#" @param t numeric. angle
+#" @return f
+#" @export
+dcum <- function(a, b, t) {
+  rd <- pi / 180
+  if (a >= 1) {
+    f <- 1 - cos(rd * t)
+  } else {
+    eps <- 1e-8
+    delx <- 1
+    x <- 2 * rd * t
+    p <- x
+    while (delx  >= eps) {
+      y <- a * sin(x) + .5 * b * sin(2. * x)
+      dx <- .5 * (y - x + p)
+      x <- x + dx
+      delx <- abs(dx)
+    }
+    f <- (2. * y + p) / pi
+  }
+  return(f)
+}
+
+#" J1 function with avoidance of singularity problem
+#"
+#" @param k numeric. Extinction coefficient for direct (solar or observer) flux
+#" @param l numeric.
+#" @param t numeric. Leaf Area Index
+#" @return jout numeric.
+#" @export
+jfunc1 <- function(k, l, t) {
+  # J1 function with avoidance of singularity problem
+  del <- (k - l) * t
+  jout <- 0 * l
+  jout[which(abs(del) > 1e-3)] <- (exp(-l[which(abs(del) > 1e-3)] * t) - exp(-k * t)) / (k - l[which(abs(del) > 1e-3)])
+  jout[which(abs(del) <= 1e-3)] <- 0.5 * t * (exp(-k * t) + exp(-l[which(abs(del) <= 1e-3)] * t)) * (1 - del[which(abs(del) <= 1e-3)] * del[which(abs(del) <= 1e-3)] / 12)
+  return(jout)
+}
+
+#" J2 function with avoidance of singularity problem
+#"
+#" @param k numeric. Extinction coefficient for direct (solar or observer) flux
+#" @param l numeric.
+#" @param t numeric. Leaf Area Index
+#" @return jout numeric.
+#" @export
+jfunc2 <- function(k, l, t) {
+  #	J2 function
+  jout <- (1. - exp(-(k + l) * t)) / (k + l)
+  return(jout)
+}
+
+#" J3 function with avoidance of singularity problem
+#"
+#" @param k numeric. Extinction coefficient for direct (solar or observer) flux
+#" @param l numeric.
+#" @param t numeric. Leaf Area Index
+#" @return jout numeric.
+#" @export
+jfunc3 <- function(k, l, t) {
+  out <- (1. - exp(-(k + l) * t)) / (k + l)
+  return(out)
+}
+
+
+#" j4 function for treating (near) conservative scattering
+#"
+#" @param m numeric. Extinction coefficient for direct (solar or observer) flux
+#" @param t numeric. Leaf Area Index
+#" @return jout numeric.
+#" @export
+jfunc4 <- function(m, t) {
+
+  del <- m * t
+  out <- 0 * del
+  out[del > 1e-3] <- (1 - exp(-del)) / (m * (1 + exp(-del)))
+  out[del <= 1e-3] <- 0.5 * t * (1. - del * del / 12.)
+  return(out)
+}
+
+
+#" compute volume scattering functions and interception coefficients
+#" for given solar zenith,  viewing zenith,  azimuth and leaf inclination angle.
+#"
+#" @param tts numeric. solar zenith
+#" @param tto numeric. viewing zenith
+#" @param psi numeric. azimuth
+#" @param ttl numeric. leaf inclination angle
+#" @return res list. includes chi_s,  chi_o,  frho,  ftau
+#" @export
+volscatt  <- function(tts, tto, psi, ttl) {
+  #********************************************************************************
+  #*	chi_s	= interception functions
+  #*	chi_o	= interception functions
+  #*	frho	= function to be multiplied by leaf reflectance rho
+  #*	ftau	= functions to be multiplied by leaf transmittance tau
+  #********************************************************************************
+  #	Wout Verhoef,  april 2001,  for CROMA
+
+  rd <- pi / 180
+  costs <- cos(rd * tts)
+  costo <- cos(rd * tto)
+  sints <- sin(rd * tts)
+  sinto <- sin(rd * tto)
+  cospsi <- cos(rd * psi)
+  psir <- rd * psi
+  costl <- cos(rd * ttl)
+  sintl <- sin(rd * ttl)
+  cs <- costl * costs
+  co <- costl * costo
+  ss <- sintl * sints
+  so <- sintl * sinto
+
+  #c ..............................................................................
+  #c     betas -bts- and betao -bto- computation
+  #c     Transition angles (beta) for solar (betas) and view (betao) directions
+  #c     if thetav + thetal > pi / 2,  bottom side of the leaves is observed for leaf azimut
+  #c     interval betao + phi<leaf azimut<2pi-betao + phi.
+  #c     if thetav + thetal<pi / 2,  top side of the leaves is always observed,  betao=pi
+  #c     same consideration for solar direction to compute betas
+  #c ..............................................................................
+
+  cosbts <- 5
+  if (abs(ss) > 1e-6) {
+    cosbts <- -cs / ss
+  }
+  cosbto <- 5
+  if (abs(so) > 1e-6) {
+    cosbto <- -co / so
+  }
+
+  if (abs(cosbts) < 1) {
+    bts <- acos(cosbts)
+    ds <- ss
+  } else {
+    bts <- pi
+    ds <- cs
+  }
+  chi_s <- 2. / pi * ((bts - pi * .5) * cs + sin(bts) * ss)
+  if (abs(cosbto) < 1) {
+    bto <- acos(cosbto)
+    doo <- so
+  } else if (tto < 90) {
+    bto <- pi
+    doo <- co
+  } else {
+    bto <- 0
+    doo <- -co
+  }
+  chi_o <- 2. / pi * ((bto - pi * .5) * co + sin(bto) * so)
+
+  #c ..............................................................................
+  #c   computation of auxiliary azimut angles bt1,  bt2,  bt3 used
+  #c   for the computation of the bidirectional scattering coefficient w
+  #c .............................................................................
+
+  btran1 <- abs(bts - bto)
+  btran2 <- pi - abs(bts + bto - pi)
+
+  if (psir <= btran1) {
+    bt1 <- psir
+    bt2 <- btran1
+    bt3 <- btran2
+  } else {
+    bt1 <- btran1
+    if (psir <= btran2) {
+      bt2 <- psir
+      bt3 <- btran2
+    } else {
+      bt2 <- btran2
+      bt3 <- psir
+    }
+  }
+  t1 <- 2. * cs * co + ss * so * cospsi
+  t2 <- 0
+  if (bt2 > 0) {
+    t2 <- sin(bt2) * (2. * ds * doo + ss * so * cos(bt1) * cos(bt3))
+  }
+
+  denom <- 2. * pi * pi
+  frho <- ((pi - bt2) * t1 + t2) / denom
+  ftau <- (-bt2 * t1 + t2) / denom
+
+  if (frho < 0) {
+    frho <- 0
+  }
+  if (ftau < 0) {
+    ftau <- 0
+  }
+  res <- list("chi_s" = chi_s, "chi_o" = chi_o, "frho" = frho, "ftau" = ftau)
+  return(res)
+}
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/prosail-master/R/Lib_PROSAIL_HybridInversion.R	Mon Jan 09 13:38:38 2023 +0000
@@ -0,0 +1,554 @@
+# ============================================================================= =
+# prosail
+# Lib_PROSAIL_HybridInversion.R
+# ============================================================================= =
+# PROGRAMMERS:
+# Jean-Baptiste FERET <jb.feret@teledetection.fr>
+# Florian de BOISSIEU <fdeboiss@gmail.com>
+# Copyright 2019 / 11 Jean-Baptiste FERET
+# ============================================================================= =
+# This Library includes functions dedicated to PROSAIL inversion using hybrid
+# approach based on SVM regression
+# ============================================================================= =
+
+
+#" This function applies SVR model on raster data in order to estimate
+#" vegetation biophysical properties
+#"
+#" @param raster_path character. path for a raster file
+#" @param hybridmodel list. hybrid models produced from train_prosail_inversion
+#" each element of the list corresponds to a set of hybrid models for a vegetation parameter
+#" @param pathout character. path for directory where results are written
+#" @param selectedbands list. list of spectral bands to be selected from raster (identified by name of vegetation parameter)
+#" @param bandname character. spectral bands corresponding to the raster
+#" @param maskraster character. path for binary mask defining ON (1) and OFF (0) pixels in the raster
+#" @param multiplyingfactor numeric. multiplying factor used to write reflectance in the raster
+#" --> PROSAIL simulates reflectance between 0 and 1,  and raster data expected in the same range
+#"
+#" @return None
+#" @importFrom progress progress_bar
+#" @importFrom stars read_stars
+#" @importFrom raster raster brick blockSize readStart readStop getValues writeStart writeStop writeValues
+#" @import rgdal
+#" @export
+apply_prosail_inversion <- function(raster_path,  hybridmodel,  pathout,
+                                    selectedbands,  bandname,
+                                    maskraster = FALSE, multiplyingfactor = 10000) {
+
+  # explain which biophysical variables will be computed
+  bpvar <- names(hybridmodel)
+  print("The following biophysical variables will be computed")
+  print(bpvar)
+
+  # get image dimensions
+  if (attr(rgdal::GDALinfo(raster_path, returnStats = FALSE),  "driver") == "ENVI") {
+    hdr <- read_envi_header(get_hdr_name(raster_path))
+    dimsraster <- list("rows" = hdr$lines, "cols" = hdr$samples, "bands" = hdr$bands)
+  } else {
+    dimsraster <- dim(read_stars(raster_path))
+    dimsraster <- list("rows" = as.numeric(dimsraster[2]), "cols" = as.numeric(dimsraster[1]), "bands" = as.numeric(dimsraster[3]))
+  }
+
+  # Produce a map for each biophysical property
+  for (parm in bpvar) {
+    print(paste("Computing", parm, sep = " "))
+    # read by chunk to avoid memory problem
+    blk <- blockSize(brick(raster_path))
+    # reflectance file
+    r_in <- readStart(brick(raster_path))
+    # mask file
+    r_inmask <- FALSE
+    if (maskraster == FALSE) {
+      selectpixels <- "ALL"
+    } else if (!maskraster == FALSE) {
+      if (file.exists(maskraster)) {
+        r_inmask <- readStart(raster(maskraster))
+      } else if (!file.exists(maskraster)) {
+        message("WARNING: Mask file does not exist:")
+        print(maskraster)
+        message("Processing all image")
+        selectpixels <- "ALL"
+      }
+    }
+    # initiate progress bar
+    pgbarlength <- length(hybridmodel[[parm]]) * blk$n
+    pb <- progress_bar$new(
+      format = "Hybrid inversion on raster [:bar] :percent in :elapsedfull, estimated time remaining :eta",
+      total = pgbarlength,  clear = FALSE,  width = 100)
+
+    # output files
+    bpvarpath <- file.path(pathout, paste(basename(raster_path), parm, sep = "_"))
+    bpvarsdpath <- file.path(pathout, paste(basename(raster_path), parm, "STD", sep = "_"))
+    r_outmean <- writeStart(raster(raster_path),  filename = bpvarpath, format = "ENVI",  overwrite = TRUE)
+    r_outsd <- writeStart(raster(raster_path),  filename = bpvarsdpath, format = "ENVI",  overwrite = TRUE)
+    selbands <- match(selectedbands[[parm]], bandname)
+
+    # loop over blocks
+    for (i in seq_along(blk$row)) {
+      # read values for block
+      # format is a matrix with rows the cells values and columns the layers
+      blockval <- getValues(r_in,  row = blk$row[i],  nrows = blk$nrows[i])
+      fulllength <- dim(blockval)[1]
+
+      if (typeof(r_inmask) == "logical") {
+        blockval <- blockval[, selbands]
+        # automatically filter pixels corresponding to negative values
+        selectpixels <- which(blockval[, 1] > 0)
+        blockval <- blockval[selectpixels, ]
+      } else if (typeof(r_inmask) == "S4") {
+        maskval <- getValues(r_inmask,  row = blk$row[i],  nrows = blk$nrows[i])
+        selectpixels <- which(maskval  == 1)
+        blockval <- blockval[selectpixels, selbands]
+      }
+      mean_estimatefull <- NA * vector(length = fulllength)
+      std_estimatefull <- NA * vector(length = fulllength)
+      if (length(selectpixels) > 0) {
+        blockval <- blockval / multiplyingfactor
+        modelsvr_estimate <- list()
+        for (modind in 1:seq_along(hybridmodel[[parm]])) {
+          pb$tick()
+          modelsvr_estimate[[modind]] <- predict(hybridmodel[[parm]][[modind]],  blockval)
+        }
+        modelsvr_estimate <- do.call(cbind, modelsvr_estimate)
+        # final estimated value = mean parm value for all models
+        mean_estimate <- rowMeans(modelsvr_estimate)
+        # "uncertainty" = STD value for all models
+        std_estimate <- rowSds(modelsvr_estimate)
+        mean_estimatefull[selectpixels] <- mean_estimate
+        std_estimatefull[selectpixels] <- std_estimate
+      } else {
+        for (modind in 1:seq_along(hybridmodel[[parm]])) {
+          pb$tick()
+        }
+      }
+      r_outmean <- writeValues(r_outmean,  mean_estimatefull,  blk$row[i], format = "ENVI",  overwrite = TRUE)
+      r_outsd <- writeValues(r_outsd,  std_estimatefull,  blk$row[i], format = "ENVI",  overwrite = TRUE)
+    }
+    # close files
+    r_in <- readStop(r_in)
+    if (typeof(r_inmask) == "S4") {
+      r_inmask <- readStop(r_inmask)
+    }
+    r_outmean <- writeStop(r_outmean)
+    r_outsd <- writeStop(r_outsd)
+    # write biophysical variable name in headers
+    hdr <- read_envi_header(get_hdr_name(bpvarpath))
+    hdr$`band names` <- paste("{", parm, "}", sep = "")
+    write_envi_header(hdr,  get_hdr_name(bpvarpath))
+  }
+  print("processing completed")
+  return(invisible())
+}
+
+#" get hdr name from image file name,  assuming it is BIL format
+#"
+#" @param impath path of the image
+#"
+#" @return corresponding hdr
+#" @importFrom tools file_ext file_path_sans_ext
+#" @export
+get_hdr_name <- function(impath) {
+  if (tools::file_ext(impath)  ==  "") {
+    impathhdr <- paste(impath,  ".hdr",  sep = "")
+  } else if (tools::file_ext(impath)  ==  "bil") {
+    impathhdr <- gsub(".bil",  ".hdr",  impath)
+  } else if (tools::file_ext(impath)  ==  "zip") {
+    impathhdr <- gsub(".zip",  ".hdr",  impath)
+  } else {
+    impathhdr <- paste(tools::file_path_sans_ext(impath),  ".hdr",  sep = "")
+  }
+
+  if (!file.exists(impathhdr)) {
+    message("WARNING : COULD NOT FIND hdr FILE")
+    print(impathhdr)
+    message("Process may stop")
+  }
+  return(impathhdr)
+}
+
+#" This function applies the regression models trained with prosail_hybrid_train
+#"
+#" @param regressionmodels list. List of regression models produced by prosail_hybrid_train
+#" @param refl numeric. LUT of bidirectional reflectances factors used for training
+#"
+#" @return hybridres list. Estimated values corresponding to refl. Includes
+#" - meanestimate = mean value for the ensemble regression model
+#" - stdestimate = std value for the ensemble regression model
+#" @importFrom stats predict
+#" @importFrom matrixStats rowSds
+#" @importFrom progress progress_bar
+#" @export
+
+prosail_hybrid_apply <- function(regressionmodels, refl) {
+
+  # make sure refl is right dimensions
+  refl <- t(refl)
+  nbfeatures <- regressionmodels[[1]]$dim
+  if (!ncol(refl) == nbfeatures && nrow(refl) == nbfeatures) {
+    refl <- t(refl)
+  }
+  nbensemble <- length(regressionmodels)
+  estimatedval <- list()
+  pb <- progress_bar$new(
+    format = "Applying SVR models [:bar] :percent in :elapsed",
+    total = nbensemble,  clear = FALSE,  width = 100)
+  for (i in 1:nbensemble) {
+    pb$tick()
+    estimatedval[[i]] <- predict(regressionmodels[[i]],  refl)
+  }
+  estimatedval <- do.call(cbind, estimatedval)
+  meanestimate <- rowMeans(estimatedval)
+  stdestimate <- rowSds(estimatedval)
+  hybridres <- list("meanestimate" = meanestimate, "stdestimate" = stdestimate)
+  return(hybridres)
+}
+
+#" This function trains a suppot vector regression for a set of variables based on spectral data
+#"
+#" @param brf_lut numeric. LUT of bidirectional reflectances factors used for training
+#" @param inputvar numeric. biophysical parameter corresponding to the reflectance
+#" @param figplot Boolean. Set to TRUE if you want a scatterplot
+#" @param nbensemble numeric. Number of individual subsets should be generated from brf_lut
+#" @param withreplacement Boolean. should subsets be generated with or without replacement?
+#"
+#" @return modelssvr list. regression models trained for the retrieval of inputvar based on brf_lut
+#" @importFrom liquidSVM svmRegression
+#" @importFrom stats predict
+#" @importFrom progress progress_bar
+#" @importFrom graphics par
+#" @importFrom expandFunctions reset.warnings
+#" @importFrom stringr str_split
+#" @importFrom simsalapar tryCatch.W.E
+#" @import dplyr
+#" @import ggplot2
+# @" @import caret
+#" @export
+
+prosail_hybrid_train <- function(brf_lut, inputvar, figplot = FALSE, nbensemble = 20, withreplacement = FALSE) {
+
+  x <- y <- ymean <- ystdmin <- ystdmax <- NULL
+  # split the LUT into nbensemble subsets
+  nbsamples <- length(inputvar)
+  if (dim(brf_lut)[2] == nbsamples) {
+    brf_lut <- t(brf_lut)
+  }
+
+  # if subsets are generated from brf_lut with replacement
+  if (withreplacement == TRUE) {
+    subsets <- list()
+    samples_per_run <- round(nbsamples / nbensemble)
+    for (run in (1:nbensemble)) {
+      subsets[[run]] <- sample(seq(1, nbsamples),  samples_per_run,  replace = TRUE)
+    }
+  # if subsets are generated from brf_lut without replacement
+  } else if (withreplacement == FALSE) {
+    subsets <- split(sample(seq(1, nbsamples, by = 1)), seq(1, nbensemble, by = 1))
+  }
+
+  # run training for each subset
+  modelssvr <- list()
+  predictedyall <- list()
+  tunedmodelyall <- list()
+  pb <- progress_bar$new(
+    format = "Training SVR on subsets [:bar] :percent in :elapsed",
+    total = nbensemble,  clear = FALSE,  width = 100)
+  for (i in 1:nbensemble) {
+    pb$tick()
+    Sys.sleep(1  /  100)
+    trainingset <- list()
+    trainingset$X <- brf_lut[subsets[i][[1]], ]
+    trainingset$Y <- inputvar[subsets[i][[1]]]
+    # liquidSVM
+    r1 <- tryCatch.W.E(tunedmodel <- liquidSVM::svmRegression(trainingset$X,  trainingset$Y))
+    if (!is.null(r1$warning)) {
+      msg <- r1$warning$message
+      valgamma <- str_split(string = msg, pattern = "gamma=")[[1]][2]
+      vallambda <- str_split(string = msg, pattern = "lambda=")[[1]][2]
+      if (!is.na(as.numeric(valgamma))) {
+        message("Adjusting Gamma accordingly")
+        valgamma <- as.numeric(valgamma)
+        tunedmodel <- liquidSVM::svmRegression(trainingset$X,  trainingset$Y, min_gamma = valgamma)
+      }
+      if (!is.na(as.numeric(vallambda))) {
+        message("Adjusting Lambda accordingly")
+        vallambda <- as.numeric(vallambda)
+        tunedmodel <- liquidSVM::svmRegression(trainingset$X,  trainingset$Y, min_lambda = vallambda)
+      }
+    }
+    modelssvr[[i]] <- tunedmodel
+  }
+
+  # if scatterplots needed
+  if (figplot == TRUE) {
+    # predict for full brf_lut
+    for (i in 1:nbensemble) {
+      tunedmodely <- stats::predict(modelssvr[[i]],  brf_lut)
+      tunedmodelyall <- cbind(tunedmodelyall, matrix(tunedmodely, ncol = 1))
+    }
+    # plot prediction
+    df <- data.frame(x = rep(1:nbsamples, nbensemble),  y = as.numeric(matrix(tunedmodelyall, ncol = 1)))
+    df_summary <- df %>%
+     dplyr::group_by(x) %>%
+      summarize(ymin = min(y), ystdmin = mean(y) - sd(y),
+                 ymax = max(y), ystdmax = mean(y) + sd(y),
+                 ymean = mean(y))
+    par(mar = rep(.1,  4))
+    p <- ggplot(df_summary,  aes(x = inputvar,  y = ymean))  +
+      geom_point(size = 2)  +
+      geom_errorbar(aes(ymin = ystdmin,  ymax = ystdmax))
+    meanpredict <- rowMeans(matrix(as.numeric(tunedmodelyall), ncol = nbensemble))
+    print(p)
+  }
+  return(modelssvr)
+}
+
+#" Reads ENVI hdr file
+#"
+#" @param hdrpath Path of the hdr file
+#"
+#" @return list of the content of the hdr file
+#" @export
+read_envi_header <- function(hdrpath) {
+  if (!grepl(".hdr$",  hdrpath)) {
+    stop("File extension should be .hdr")
+  }
+  hdr <- readLines(hdrpath)
+  ## check ENVI at beginning of file
+  if (!grepl("ENVI",  hdr[1])) {
+    stop("Not an ENVI header (ENVI keyword missing)")
+  } else {
+    hdr <- hdr [-1]
+  }
+  ## remove curly braces and put multi-line key-value-pairs into one line
+  hdr <- gsub("\\{([^}]*)\\}",  "\\1",  hdr)
+  l <- grep("\\{",  hdr)
+  r <- grep("\\}",  hdr)
+
+  if (length(l) != length(r)) {
+    stop("Error matching curly braces in header (differing numbers).")
+  }
+
+  if (any(r <= l)) {
+    stop("Mismatch of curly braces in header.")
+  }
+
+  hdr[l] <- sub("\\{",  "",  hdr[l])
+  hdr[r] <- sub("\\}",  "",  hdr[r])
+
+  for (i in rev(seq_along(l))) {
+    hdr <- c(
+      hdr [seq_len(l [i] - 1)],
+      paste(hdr [l [i]:r [i]],  collapse = "\n"),
+      hdr [-seq_len(r [i])]
+    )
+  }
+
+  ## split key = value constructs into list with keys as names
+  hdr <- sapply(hdr,  split_line,  " = ",  USE.NAMES = FALSE)
+  names(hdr) <- tolower(names(hdr))
+
+  ## process numeric values
+  tmp <- names(hdr) %in% c(
+    "samples",  "lines",  "bands",  "header offset",  "data type",
+    "byte order",  "default bands",  "data ignore value",
+    "wavelength",  "fwhm",  "data gain values"
+  )
+  hdr [tmp] <- lapply(hdr [tmp],  function(x) {
+    as.numeric(unlist(strsplit(x,  ",")))
+  })
+
+  return(hdr)
+}
+
+#" ENVI functions
+#"
+#" based on https: /  / github.com / cran / hyperSpec / blob / master / R / read.ENVI.R
+#" added wavelength,  fwhm,  ... to header reading
+#" Title
+#"
+#" @param x character.
+#" @param separator character
+#" @param trim_blank  boolean.
+#"
+#" @return list.
+#" @export
+split_line <- function(x,  separator,  trim_blank  = TRUE) {
+  tmp <- regexpr(separator,  x)
+  key <- substr(x,  1,  tmp - 1)
+  value <- substr(x,  tmp  +  1,  nchar(x))
+  if (trim_blank) {
+    blank_pattern <- "^[[:blank:]]*([^[:blank:]] + .*[^[:blank:]] + )[[:blank:]]*$"
+    key <- sub(blank_pattern,  "\\1",  key)
+    value <- sub(blank_pattern,  "\\1",  value)
+  }
+  value <- as.list(value)
+  names(value) <- key
+  return(value)
+}
+
+#" This function performs full training for hybrid invrsion using SVR with
+#" values for default parameters
+#"
+#" @param minval list. minimum value for input parameters sampled to produce a training LUT
+#" @param maxval list. maximum value for input parameters sampled to produce a training LUT
+#" @param typedistrib  list. Type of distribution. Either "Uniform" or "Gaussian"
+#" @param gaussiandistrib  list. Mean value and STD corresponding to the parameters sampled with gaussian distribution
+#" @param parmset list. list of input parameters set to a specific value
+#" @param nbsamples numeric. number of samples in training LUT
+#" @param nbsamplesperrun numeric. number of training sample per individual regression model
+#" @param nbmodels numeric. number of individual models to be run for ensemble
+#" @param replacement bolean. is there replacement in subsampling?
+#" @param sailversion character. Either 4SAIL or 4SAIL2
+#" @param parms2estimate list. list of input parameters to be estimated
+#" @param bands2select list. list of bands used for regression for each input parameter
+#" @param noiselevel list. list of noise value added to reflectance (defined per input parm)
+#" @param specprospect list. Includes optical constants required for PROSPECT
+#" @param specsoil list. Includes either dry soil and wet soil,  or a unique soil sample if the psoil parameter is not inverted
+#" @param specatm list. Includes direct and diffuse radiation for clear conditions
+#" @param path_results character. path for results
+#" @param figplot boolean. Set TRUE to get scatterplot of estimated biophysical variable during training step
+#" @param force4lowlai boolean. Set TRUE to artificially reduce leaf chemical constituent content for low LAI
+#"
+#"
+#" @return modelssvr list. regression models trained for the retrieval of inputvar based on brf_lut
+#" @export
+
+train_prosail_inversion <- function(minval = NULL, maxval = NULL,
+                                    typedistrib = NULL, gaussiandistrib = NULL, parmset = NULL,
+                                    nbsamples = 2000, nbsamplesperrun = 100, nbmodels = 20, replacement = TRUE,
+                                    sailversion = "4SAIL",
+                                    parms2estimate = "lai", bands2select = NULL, noiselevel = NULL,
+                                    specprospect = NULL,  specsoil = NULL,  specatm = NULL,
+                                    path_results = "./", figplot = FALSE, force4lowlai = TRUE) {
+
+  ###===================================================================###
+  ###           1- PRODUCE A LUT TO TRAIN THE HYBRID INVERSION          ###
+  ###===================================================================###
+  # Define sensor characteristics
+  if (is.null(specprospect)) {
+    specprospect <- prosail::specprospect
+  }
+  if (is.null(specsoil)) {
+    specsoil <- prosail::specsoil
+  }
+  if (is.null(specprospect)) {
+    specatm <- prosail::specatm
+  }
+  # define distribution for parameters to be sampled
+  if (is.null(typedistrib)) {
+    typedistrib <- data.frame("CHL" = "Uniform",  "CAR" = "Uniform", "EWT" = "Uniform", "ANT" = "Uniform", "LMA" = "Uniform", "N" = "Uniform",  "BROWN" = "Uniform",
+                              "psoil" = "Uniform", "LIDFa" = "Uniform",  "lai" = "Uniform", "q" = "Uniform", "tto" = "Uniform", "tts" = "Uniform",  "psi" = "Uniform")
+  }
+  if (is.null(gaussiandistrib)) {
+    gaussiandistrib <- list("Mean" = NULL, "Std" = NULL)
+  }
+  if (is.null(minval)) {
+    minval <- data.frame("CHL" = 10, "CAR" = 0, "EWT" = 0.01, "ANT" = 0, "LMA" = 0.005, "N" = 1.0, "psoil" = 0.0,  "BROWN" = 0.0,
+                         "LIDFa" = 20,  "lai" = 0.5, "q" = 0.1, "tto" = 0, "tts" = 20,  "psi" = 80)
+  }
+  if (is.null(maxval)) {
+    maxval <- data.frame("CHL" = 75, "CAR" = 15, "EWT" = 0.03, "ANT" = 2, "LMA" = 0.03, "N" = 2.0,  "psoil" = 1.0,  "BROWN" = 0.5,
+                         "LIDFa" = 70,  "lai" = 7, "q" = 0.2, "tto" = 5, "tts" = 30,  "psi" = 110)
+  }
+  # define min and max values
+  # fixed parameters
+  if (is.null(parmset)) {
+    parmset <- data.frame("TypeLidf" = 2,  "alpha" = 40)
+  }
+  # produce input parameters distribution
+  if (sailversion == "4SAIL") {
+    inputprosail <- get_distribution_input_prosail(minval, maxval, parmset, nbsamples,
+                                                   typedistrib = typedistrib,
+                                                   Mean = gaussiandistrib$Mean, Std = gaussiandistrib$Std,
+                                                   force4lowlai = force4lowlai)
+  } else if (sailversion == "4SAIL2") {
+    inputprosail <- get_distribution_input_prosail2(minval, maxval, parmset, nbsamples,
+                                                    typedistrib = typedistrib,
+                                                    Mean = gaussiandistrib$Mean, Std = gaussiandistrib$Std,
+                                                    force4lowlai = force4lowlai)
+  }
+  if (sailversion == "4SAIL2") {
+    # Definition of Cv && update LAI
+    maxlai <- min(c(maxval$lai), 4)
+    inputprosail$Cv <- NA * inputprosail$lai
+    inputprosail$Cv[which(inputprosail$lai > maxlai)] <- 1
+    inputprosail$Cv[which(inputprosail$lai <= maxlai)] <- (1 / maxlai) + inputprosail$lai[which(inputprosail$lai <= maxlai)] / (maxlai + 1)
+    inputprosail$Cv <- inputprosail$Cv * matrix(rnorm(length(inputprosail$Cv), mean = 1, sd = 0.1))
+    inputprosail$Cv[which(inputprosail$Cv < 0)] <- 0
+    inputprosail$Cv[which(inputprosail$Cv > 1)] <- 1
+    inputprosail$Cv[which(inputprosail$lai > maxlai)] <- 1
+    inputprosail$fraction_brown <- 0 + 0 * inputprosail$lai
+    inputprosail$diss <- 0 + 0 * inputprosail$lai
+    inputprosail$Zeta <- 0.2 + 0 * inputprosail$lai
+    inputprosail$lai <- inputprosail$lai * inputprosail$Cv
+  }
+
+  # generate LUT of BRF corresponding to inputprosail,  for a sensor
+  brf_lut <- Generate_LUT_BRF(sailversion = sailversion, inputprosail = inputprosail,
+                              specprospect = specprospect, specsoil = specsoil, specatm = specatm)
+
+  # write parameters LUT
+  output <- matrix(unlist(inputprosail),  ncol = length(inputprosail),  byrow = FALSE)
+  filename <- file.path(path_results, "PROSAIL_LUT_InputParms.txt")
+  write.table(x = format(output,  digits = 3), file = filename, append = FALSE,  quote = FALSE,
+              col.names = names(inputprosail),  row.names = FALSE, sep = "\t")
+  # Write BRF LUT corresponding to parameters LUT
+  filename <- file.path(path_results, "PROSAIL_LUT_reflectance.txt")
+  write.table(x = format(t(brf_lut),  digits = 5), file = filename, append = FALSE,  quote = FALSE,
+              col.names = specprospect$lambda,  row.names = FALSE, sep = "\t")
+
+  # Which bands will be used for inversion?
+  if (is.null(bands2select)) {
+    bands2select <- list()
+    for (parm in parms2estimate) {
+      bands2select[[parm]] <- seq(1, length(specprospect$lambda))
+    }
+  }
+  # Add gaussian noise to reflectance LUT: one specific LUT per parameter
+  if (is.null(noiselevel)) {
+    noiselevel <- list()
+    for (parm in parms2estimate) {
+      noiselevel[[parm]] <- 0.01
+    }
+  }
+
+  # produce LIT with noise
+  brf_lut_noise <- list()
+  for (parm in parms2estimate) {
+    brf_lut_noise[[parm]] <- brf_lut[bands2select[[parm]], ] + brf_lut[bands2select[[parm]], ] * matrix(rnorm(nrow(brf_lut[bands2select[[parm]], ]) * ncol(brf_lut[bands2select[[parm]], ]),
+                                                                                                        0, noiselevel[[parm]]), nrow = nrow(brf_lut[bands2select[[parm]], ]))
+  }
+
+  ###===================================================================###
+  ###                     PERFORM HYBRID INVERSION                      ###
+  ###===================================================================###
+  # train SVR for each variable and each run
+  modelsvr <- list()
+  for (parm in parms2estimate) {
+    colparm <- which(parm == names(inputprosail))
+    inputvar <- inputprosail[[colparm]]
+    modelsvr[[parm]] <- prosail_hybrid_train(brf_lut = brf_lut_noise[[parm]], inputvar = inputvar,
+                                             figplot = figplot, nbensemble = nbmodels, withreplacement = replacement)
+  }
+  return(modelsvr)
+}
+
+#" writes ENVI hdr file
+#"
+#" @param hdr content to be written
+#" @param hdrpath Path of the hdr file
+#"
+#" @return None
+#" @importFrom stringr str_count
+#" @export
+write_envi_header <- function(hdr, hdrpath) {
+  h <- lapply(hdr, function(x) {
+    if (length(x) > 1 || (is.character(x) && stringr::str_count(x,  "\\w + ") > 1)) {
+      x <- paste0("{",  paste(x,  collapse = ","),  "}")
+    }
+    # convert last numerics
+    x <- as.character(x)
+  })
+  writeLines(c("ENVI",  paste(names(hdr),  h,  sep = "=")),  con = hdrpath)
+  return(invisible())
+}
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/prosail-master/R/Lib_SpectralIndices.R	Mon Jan 09 13:38:38 2023 +0000
@@ -0,0 +1,679 @@
+# ============================================================================== =
+# prosail
+# Lib_spectralindices.R
+# ============================================================================== =
+# PROGRAMMERS:
+# Jean-Baptiste FERET <jb.feret@teledetection.fr>
+# Florian de BOISSIEU <fdeboiss@gmail.com>
+# Copyright 2019/11 Jean-Baptiste FERET
+# ============================================================================== =
+# This Library includes aims at computing spectral indices from reflectance data
+# ============================================================================== =
+
+#" This function computes Area under curve for continuum removed reflectances
+#" See Malenovský et al. (2013) for details
+#" http://dx.doi.org/10.1016/j.rse.2012.12.015
+#"
+#" @param refl RasterBrick, RasterStack or list. Raster bands in the order of sensorbands.
+#" @param sensorbands numeric. vector containing central wavelength for each spectral band in the image
+#" @param aucminmax list. wavelengths of lower and upper boundaries ("CRmin" and "CRmax")
+#" @param reflfactor numeric. multiplying factor used to write reflectance in image (==10000 for S2)
+#"
+#" @return aucval raster
+#" @export
+auc <- function(refl, sensorbands, aucminmax, reflfactor = 1) {
+
+  aucbands <- list()
+  aucbands[["CRmin"]] <- sensorbands[get_closest_bands(sensorbands, aucminmax[["CRmin"]])]
+  aucbands[["CRmax"]] <- sensorbands[get_closest_bands(sensorbands, aucminmax[["CRmax"]])]
+  bands <- get_closest_bands(sensorbands, aucbands)
+  for (i in bands[["CRmin"]]:bands[["CRmax"]]) {
+    if (is.na(match(i, bands))) {
+      aucbands[[paste("B", i, sep = "")]] <- sensorbands[i]
+    }
+  }
+  # compute continuum removal for all spectral bands
+  cr <- cr_wl(refl = refl, sensorbands = sensorbands,
+              crbands = aucbands, reflfactor = reflfactor)
+
+  wl <- sort(unlist(aucbands), decreasing = FALSE)
+  aucval <- 0.5 * (1 - cr[[1]]) * (wl[2] - wl[1])
+  for (i in 2:length(cr)) {
+    aucval <- aucval + 0.5 * (2 - cr[[i - 1]] - cr[[i]]) * (wl[i + 1] - wl[i])
+  }
+  aucval <- aucval + 0.5 * (1 - cr[[length(cr)]]) * (wl[i + 2] - wl[i + 1])
+  return(aucval)
+}
+
+#" This function extracts boundaries to be used to compute continuum from reflectance data
+#"
+#" @param refl RasterBrick, RasterStack or list. Raster bands in the order of sensorbands.
+#" @param sensorbands numeric. vector containing central wavelength for each spectral band in the image
+#" @param crbands list. list of spectral bands (central wavelength) including CRmin and CRmax
+#" @param reflfactor numeric. multiplying factor used to write reflectance in image ( == 10000 for S2)
+#"
+#" @return crminmax list. list of rasters corresponding to minimum and maximum wavelengths
+#" @export
+crbound <- function(refl,  sensorbands,  crbands,  reflfactor = 1) {
+
+  # get closest spectral bands from CR1 and CR2
+  bands <- get_closest_bands(sensorbands, list(crbands[["CRmin"]], crbands[["CRmax"]]))
+  wl <- sensorbands[bands]
+  # get equation for line going from CR1 to CR2
+  crminmax <- readrasterbands(refl = refl,  bands = bands,  reflfactor = reflfactor)
+  names(crminmax) <- paste("wl_", as.character(wl), sep = "")
+  return(crminmax)
+}
+
+#" This function extracts boundaries to be used to compute continuum from reflectance data
+#"
+#" @param refl RasterBrick,  RasterStack or list. Raster bands in the order of sensorbands.
+#" @param sensorbands numeric. vector containing central wavelength for each spectral band in the image
+#" @param crbands list. list of spectral bands (central wavelength) including CRmin and CRmax
+#" @param reflfactor numeric. multiplying factor used to write reflectance in image ( == 10000 for S2)
+#"
+#" @return outlier_iqr numeric. band numbers of original sensor corresponding to S2
+#" @importFrom progress progress_bar
+#" @export
+cr_wl <- function(refl,  sensorbands,  crbands,  reflfactor = 1) {
+
+  # Make sure CRmin and CRmax are correctly defined
+  if (is.na(match("CRmin", names(crbands))) || is.na(match("CRmax", names(crbands)))) {
+    stop("Please define CRmin and CRmax (CRmin<CRmax) as spectral bands in crbands")
+  }
+  if (crbands[["CRmax"]] < crbands[["CRmin"]]) {
+    stop("Please define CRmin < CRmax in crbands")
+  }
+  # extract CRmin and CRmax
+  crminmax <- crbound(refl,  sensorbands,  crbands,  reflfactor = reflfactor)
+  # extract other bands and compute CR
+  crmin <- sensorbands[get_closest_bands(sensorbands, crbands[["CRmin"]])]
+  crmax <- sensorbands[get_closest_bands(sensorbands, crbands[["CRmax"]])]
+  crbands[["CRmin"]] <- NULL
+  crbands[["CRmax"]] <- NULL
+  cr <- list()
+  # initiate progress bar
+  pgbarlength <- length(crbands)
+  pb <- progress_bar$new(
+    format = "Computing continuum removal [:bar] :percent in :elapsedfull, estimated time remaining :eta",
+    total = pgbarlength,  clear = FALSE,  width = 100)
+  # computation for each band
+  for (band in crbands) {
+    pb$tick()
+    bandrank <- get_closest_bands(sensorbands, band)
+    raster2cr <- readrasterbands(refl = refl,  bands = bandrank,  reflfactor = reflfactor)
+    cr[[as.character(band)]] <- computecr(wlmin = crmin,  wlmax = crmax,
+                                          wltarget = band,  boundaries = crminmax,
+                                          target = raster2cr)
+  }
+  return(cr)
+}
+
+#" This function computes continuum removal value for a spectral band of interest,
+#" based on lower and upper wavelengths corresponding to boundaries of the continuum
+#"
+#" @param wlmin numeric. wavelength of the spectral band corresponding to minimum boundary
+#" @param wlmax numeric. wavelength of the spectral band corresponding to maximum boundary
+#" @param wltarget numeric. wavelength of the spectral band for which cr is computed
+#" @param boundaries list. raster objects corresponding to minimum and maximum wavelengths
+#" @param target list. raster object corresponding target wavelength
+#"
+#" @return cr list. raster object corresponding to continuum removed value
+#" @export
+computecr <- function(wlmin,  wlmax,  wltarget,  boundaries,  target) {
+
+  cr <- target / (boundaries[[1]] + (wltarget - wlmin) * (boundaries[[2]] - boundaries[[1]]) / (wlmax - wlmin))
+  return(cr)
+}
+
+#" this function produces a spectral index from an expression defining a spectral index
+#"
+#" @param refl RasterBrick,  RasterStack or list. Raster bands in the order of sensorbands.
+#" @param sensorbands numeric. wavelength in nanometers of the spectral bands of refl.
+#" @param expressindex  character. expression corresponding to the spectral index to compute
+#" @param listbands list. list of spectral bands defined in the "expressindex" variable
+#" @param reflfactor numeric. multiplying factor used to write reflectance in image ( == 10000 for S2)
+#" @param nameindex character. name for the index to be computed,  provided in the raster layer
+#"
+#" @return numeric. band numbers of original sensor corresponding to S2
+#" @export
+spectralindices_fromexpression <- function(refl,  sensorbands,  expressindex,  listbands,  reflfactor = 1, nameindex = NULL) {
+
+  # define which bands to be used in the spectral index
+  bands <- get_closest_bands(sensorbands, listbands)
+
+  classraster <- class(refl)
+  if (classraster == "RasterBrick" || classraster == "RasterStack" || classraster == "stars") {
+    # if !reflfactor  ==  1 then apply a reflectance factor
+    if (classraster == "stars") {
+      refl <- refl[bands]
+    } else {
+      refl <- raster::subset(refl, bands)
+    }
+    if (!reflfactor == 1) {
+      refl <- refl / reflfactor
+    }
+  } else if (is.list(refl)) {
+    refl <- raster::stack(refl[bands]) # checks that all rasters have same crs/extent
+    if (!reflfactor == 1) {
+      refl <- refl / reflfactor
+    }
+  } else {
+    stop("refl is expected to be a RasterStack,  RasterBrick,  Stars object or a list of rasters")
+  }
+  names(refl) <- gsub(pattern = "B", replacement = "Band", x = names(bands))
+
+  nbbands <- unique(as.numeric(gsub(pattern = "B",
+                                    replacement = "",
+                                    x =  unlist(regmatches(expressindex,
+                                                           gregexpr("B[[:digit:]] + ",
+                                                                    expressindex))))))
+  sortband <- sort(nbbands, index.return = TRUE, decreasing = TRUE)
+  matches <- unique(unlist(regmatches(expressindex,  gregexpr("B[[:digit:]] + ",  expressindex))))[sortband$ix]
+  replaces <- paste("refl[['Band", gsub(pattern = "B", replacement = "", x = matches), "']]", sep = "")
+  expressindex_final <- expressindex
+  for (bb in 1:seq_along(matches)) {
+    expressindex_final <- gsub(pattern = matches[bb],  replacement = replaces[bb],  x = expressindex_final)
+  }
+  si <- eval(parse(text = expressindex_final))
+  if (!is.null(nameindex)) {
+    names(si) <- nameindex
+  }
+  return(si)
+}
+
+#" this function aims at computing spectral indices from Sensor reflectance data in raster object
+#" it computes the spectral indices based on their computation with Sentinel-2
+#" and assumes that the bands of the S2 data follow this order
+#" wavelength	= {496.6,  560.0,  664.5,  703.9,  740.2,  782.5,  835.1,  864.8,  1613.7,  2202.4}
+#" Full description of the indices can be found here:
+#" https://www.sentinel-hub.com/eotaxonomy/indices
+#"
+#" @param refl RasterBrick,  RasterStack or list. Raster bands in the order of sensorbands.
+#" @param sensorbands numeric. wavelength in nanometers of the spectral bands of refl.
+#" @param sel_indices  list. list of spectral indices to be computed
+#" @param stackout logical. If TRUE returns a stack,  otherwise a list of rasters.
+#" @param reflfactor numeric. multiplying factor used to write reflectance in image ( == 10000 for S2)
+#"
+#" @return list. includes
+#" - spectralindices: List of spectral indices computed from the reflectance initially provided
+#" - listindices: list of spectral indices computable with the function
+#" @importFrom methods is
+#" @importFrom raster stack brick
+#" @export
+
+computespectralindices_raster <- function(refl,  sensorbands,  sel_indices = "ALL",  stackout = TRUE, reflfactor = 1) {
+
+  s2bands <- c("B2" = 496.6, "B3" = 560.0, "B4" = 664.5, "B5" = 703.9, "B6" = 740.2,
+               "B7" = 782.5, "B8" = 835.1, "B8A" = 864.8, "B11" = 1613.7, "B12" = 2202.4)
+
+  spectralindices <- list()
+  sen2s2 <- get_closest_bands(sensorbands, s2bands)
+  classraster <- class(refl)
+  if (classraster == "RasterBrick" || classraster == "RasterStack" || classraster == "stars") {
+    # if !reflfactor  ==  1 then apply a reflectance factor
+    if (classraster == "stars") {
+      refl <- refl[sen2s2]
+    } else {
+      refl <- raster::subset(refl, sen2s2)
+    }
+    if (!reflfactor == 1) {
+      refl <- refl / reflfactor
+    }
+  } else if (is.list(refl)) {
+    refl <- raster::stack(refl[sen2s2]) # checks that all rasters have same crs/extent
+    if (!reflfactor == 1) {
+      refl <- refl / reflfactor
+    }
+  } else {
+    stop("refl is expected to be a RasterStack, RasterBrick, Stars object or a list of rasters")
+  }
+  names(refl) <- names(sen2s2)
+
+  indexall <- list()
+
+  # inelegant but meeeeh
+  listindices <- list("ARI1", "ARI2", "ARVI", "BAI", "BAIS2", "CCCI", "CHL_RE", "CRI1", "CRI2", "EVI", "EVI2",
+                      "GRVI1", "GNDVI", "IRECI", "LAI_SAVI", "MCARI", "mNDVI705", "MSAVI2",
+                      "MSI", "mSR705", "MTCI", "nBR_RAW", "NDI_45", "NDII", "NDSI", "NDVI", "NDVI_G",
+                      "NDVI705", "NDWI1", "NDWI2", "PSRI", "PSRI_NIR", "RE_NDVI", "RE_NDWI", "S2REP",
+                      "SAVI", "SIPI", "SR", "CR_SWIR")
+  if (sel_indices[1] == "ALL") {
+    sel_indices <- listindices
+  }
+  if ("ARI1" %in% sel_indices) {
+    ari1 <- (1 / refl[["B3"]]) - (1 / refl[["B5"]])
+    spectralindices$ARI1 <- ari1
+  }
+  if ("ARI2" %in% sel_indices) {
+    ari2 <- (refl[["B8"]] / refl[["B2"]]) - (refl[["B8"]] / refl[["B3"]])
+    spectralindices$ARI2 <- ari2
+  }
+  if ("ARVI" %in% sel_indices) {
+    arvi <- (refl[["B8"]] - (2 * refl[["B4"]] - refl[["B2"]])) / (refl[["B8"]] + (2 * refl[["B4"]] - refl[["B2"]]))
+    spectralindices$ARVI <- arvi
+  }
+  if ("BAI" %in% sel_indices) {
+    bai <- (1 / ((0.1 - refl[["B4"]])**2 + (0.06 - refl[["B8"]])**2))
+    spectralindices$BAI <- bai
+  }
+  if ("BAIS2" %in% sel_indices) {
+    bais2 <-  (1 - ((refl[["B6"]] * refl[["B7"]] * refl[["B8A"]]) / refl[["B4"]])**0.5) * ((refl[["B12"]] - refl[["B8A"]]) / ((refl[["B12"]] + refl[["B8A"]])**0.5) + 1)
+    spectralindices$BAIS2 <- bais2
+  }
+  if ("CCCI" %in% sel_indices) {
+    ccci <- ((refl[["B8"]] - refl[["B5"]]) / (refl[["B8"]] + refl[["B5"]])) / ((refl[["B8"]] - refl[["B4"]]) / (refl[["B8"]] + refl[["B4"]]))
+    spectralindices$CCCI <- ccci
+  }
+  if ("CHL_RE" %in% sel_indices) {
+    chl_re <- refl[["B5"]] / refl[["B8"]]
+    spectralindices$CHL_RE <- chl_re
+  }
+  if ("CRI1" %in% sel_indices) {
+    cri1 <- (1 / refl[["B2"]]) - (1 / refl[["B3"]])
+    spectralindices$CRI1 <- cri1
+  }
+  if ("CRI2" %in% sel_indices) {
+    cri2 <- (1 / refl[["B2"]]) - (1 / refl[["B5"]])
+    spectralindices$CRI2 <- cri2
+  }
+  if ("EVI" %in% sel_indices) {
+    evi <- 2.5 * (refl[["B8"]] - refl[["B4"]]) / ((refl[["B8"]] + 6 * refl[["B4"]] - 7.5 * refl[["B2"]] + 1))
+    spectralindices$EVI <- evi
+  }
+  if ("EVI2" %in% sel_indices) {
+    evi2 <- 2.5 * (refl[["B8"]] - refl[["B4"]]) / (refl[["B8"]] + 2.4 * refl[["B4"]] + 1)
+    spectralindices$EVI2 <- evi2
+  }
+  if ("GRVI1" %in% sel_indices) {
+    grvi1 <- (refl[["B4"]] - refl[["B3"]]) / (refl[["B4"]] + refl[["B3"]])
+    spectralindices$GRVI1 <- grvi1
+  }
+  if ("GNDVI" %in% sel_indices) {
+    gndvi <- (refl[["B8"]] - refl[["B3"]]) / (refl[["B8"]] + refl[["B3"]])
+    spectralindices$GNDVI <- gndvi
+  }
+  if ("IRECI" %in% sel_indices) {
+    ireci <- (refl[["B7"]] - refl[["B4"]]) * (refl[["B6"]] / (refl[["B5"]]))
+    spectralindices$IRECI <- ireci
+  }
+  if ("LAI_SAVI" %in% sel_indices) {
+    lai_savi <- - log(0.371  +  1.5 * (refl[["B8"]] - refl[["B4"]]) / (refl[["B8"]] +  refl[["B4"]] +  0.5)) / 2.4
+    spectralindices$LAI_SAVI <- lai_savi
+  }
+  if  ("MCARI" %in% sel_indices) {
+    mcari <- (1 - 0.2 * (refl[["B5"]] - refl[["B3"]]) / (refl[["B5"]] - refl[["B4"]]))
+    spectralindices$MCARI <- mcari
+  }
+  if ("mNDVI705" %in% sel_indices) {
+    mndvi705 <- (refl[["B6"]] - refl[["B5"]]) / (refl[["B6"]] + refl[["B5"]] - 2 * refl[["B2"]])
+    spectralindices$mNDVI705 <- mndvi705
+  }
+  if ("MSAVI2" %in% sel_indices) {
+    msavi2 <- ((refl[["B8"]] + 1) - 0.5 * sqrt(((2 * refl[["B8"]]) - 1)**2 + 8 * refl[["B4"]]))
+    spectralindices$MSAVI2 <- msavi2
+  }
+  if ("MSI" %in% sel_indices) {
+    msi <- refl[["B11"]] / refl[["B8A"]]
+    spectralindices$MSI <- msi
+  }
+  if ("mSR705" %in% sel_indices) {
+    msr705 <- (refl[["B6"]] - refl[["B2"]]) / (refl[["B5"]] - refl[["B2"]])
+    spectralindices$mSR705 <- msr705
+  }
+  if ("MTCI" %in% sel_indices) {
+    mtci <- (refl[["B6"]] - refl[["B5"]]) / (refl[["B5"]] + refl[["B4"]])
+    spectralindices$MTCI <- mtci
+  }
+  if ("nBR_RAW" %in% sel_indices) {
+    nbr_raw <- (refl[["B8"]] - refl[["B12"]]) / (refl[["B8"]] + refl[["B12"]])
+    spectralindices$nBR_RAW <- nbr_raw
+  }
+  if ("NDI_45" %in% sel_indices) {
+    ndi_45 <- (refl[["B5"]] - refl[["B4"]]) / (refl[["B5"]] + refl[["B4"]])
+    spectralindices$NDI_45 <- ndi_45
+  }
+  if ("NDII" %in% sel_indices) {
+    ndii <- (refl[["B8A"]] - refl[["B11"]]) / (refl[["B8A"]] + refl[["B11"]])
+    spectralindices$NDII <- ndii
+  }
+  if ("NDSI" %in% sel_indices) {
+    ndsi <- (refl[["B3"]] - refl[["B11"]]) / (refl[["B3"]] + refl[["B11"]])
+    spectralindices$NDSI <- ndsi
+  }
+  if ("NDVI" %in% sel_indices) {
+    ndvi <- (refl[["B8"]] - refl[["B4"]]) / (refl[["B8"]] + refl[["B4"]])
+    spectralindices$NDVI <- ndvi
+  }
+  if ("NDVI_G" %in% sel_indices) {
+    ndvi_g <- refl[["B3"]] * (refl[["B8"]] - refl[["B4"]]) / (refl[["B8"]] + refl[["B4"]])
+    spectralindices$NDVI_G <- ndvi_g
+  }
+  if ("NDVI705" %in% sel_indices) {
+    ndvi705 <- (refl[["B6"]] - refl[["B5"]]) / (refl[["B6"]] + refl[["B5"]])
+    spectralindices$NDVI705 <- ndvi705
+  }
+  if ("NDWI" %in% sel_indices) {
+    ndwi <- (refl[["B3"]] - refl[["B8"]]) / (refl[["B3"]] + refl[["B8"]])
+    spectralindices$NDWI <- ndwi
+  }
+  if ("NDWI1" %in% sel_indices) {
+    ndwi1 <- (refl[["B8A"]] - refl[["B11"]]) / (refl[["B8A"]] + refl[["B11"]])
+    spectralindices$NDWI1 <- ndwi1
+  }
+  if ("NDWI2" %in% sel_indices) {
+    ndwi2 <- (refl[["B8A"]] - refl[["B12"]]) / (refl[["B8A"]] + refl[["B12"]])
+    spectralindices$NDWI2 <- ndwi2
+  }
+  if ("PSRI" %in% sel_indices) {
+    psri <- (refl[["B4"]] - refl[["B2"]]) / (refl[["B5"]])
+    spectralindices$PSRI <- psri
+  }
+  if ("PSRI_NIR" %in% sel_indices) {
+    psri_nir <- (refl[["B4"]] - refl[["B2"]]) / (refl[["B8"]])
+    spectralindices$PSRI_NIR <- psri_nir
+  }
+  if ("RE_NDVI" %in% sel_indices) {
+    re_ndvi <- (refl[["B8"]] - refl[["B6"]]) / (refl[["B8"]] + refl[["B6"]])
+    spectralindices$RE_NDVI <- re_ndvi
+  }
+  if ("RE_NDWI" %in% sel_indices) {
+    re_ndwi <- (refl[["B4"]] - refl[["B6"]]) / (refl[["B4"]] + refl[["B6"]])
+    spectralindices$RE_NDWI <- re_ndwi
+  }
+  if ("S2REP" %in% sel_indices) {
+    s2rep <- 705 + 35 * (0.5 * (refl[["B8"]] + refl[["B5"]]) - refl[["B6"]]) / (refl[["B7"]] - refl[["B6"]])
+    spectralindices$S2REP <- s2rep
+  }
+  if ("SAVI" %in% sel_indices) {
+    savi <- 1.5 * (refl[["B8"]] - refl[["B5"]]) / (refl[["B8"]] + refl[["B5"]] + 0.5)
+    spectralindices$SAVI <- savi
+  }
+  if ("SIPI" %in% sel_indices) {
+    sipi <- (refl[["B8"]] - refl[["B2"]]) / (refl[["B8"]] - refl[["B4"]])
+    spectralindices$SIPI <- sipi
+  }
+  if ("SR" %in% sel_indices) {
+    sr <- refl[["B8"]] / refl[["B4"]]
+    spectralindices$SR <- sr
+  }
+  if ("TCARI" %in% sel_indices) {
+    sr <- refl[["B8"]] / refl[["B4"]]
+    spectralindices$SR <- sr
+  }
+  if ("CR_SWIR" %in% sel_indices) {
+    cr_swir <- refl[["B11"]] / (refl[["B8A"]] + (s2bands["B11"] - s2bands["B8A"]) * (refl[["B12"]] - refl[["B8A"]]) / (s2bands["B12"] - s2bands["B8A"]))
+    spectralindices$CR_SWIR <- cr_swir
+  }
+
+  if (stackout)
+    spectralindices <- raster::stack(spectralindices)
+
+  res <- list("spectralindices" = spectralindices, "listindices" = listindices)
+  return(res)
+}
+
+#" this function aims at computing spectral indices from Sensor reflectance data.
+#" it computes the spectral indices based on their computation with Sentinel-2
+#" and assumes that the bands of the S2 data follow this order
+#" wavelength	= {496.6,  560.0,  664.5,  703.9,  740.2,  782.5,  835.1,  864.8,  1613.7,  2202.4}
+#" Full description of the indices can be found here:
+#" https://www.sentinel-hub.com/eotaxonomy/indices
+#"
+#" @param refl numeric. reflectance dataset defined in matrix
+#" @param sel_indices list. list of spectral indices to be computed
+#" @param sensorbands numeric. wavelength of the spectral bands corresponding to the spectral index
+#"
+#" @return list. includes
+#" - spectralindices: List of spectral indices computed from the reflectance initially provided
+#" - listindices: list of spectral indices computable with the function
+#" @export
+
+computespectralindices_hs <- function(refl, sensorbands, sel_indices = "ALL") {
+
+  spectralindices <- list()
+  s2bands <- data.frame("B2" = 496.6, "B3" = 560.0, "B4" = 664.5, "B5" = 703.9, "B6" = 740.2,
+               "B7" = 782.5, "B8" = 835.1, "B8A" = 864.8, "B11" = 1613.7, "B12" = 2202.4)
+
+  sen2s2 <- get_closest_bands(sensorbands, s2bands)
+  indexall <- list()
+  # set zero vaues to >0 in order to avoid problems
+  selzero <- which(refl == 0)
+  refl[selzero] <- 0.005
+  if (dim(refl)[1] == length(sensorbands)) {
+    refl <- t(refl)
+  }
+
+  # inelegant but meeeeh
+  listindices <- list("ARI1", "ARI2", "ARVI", "BAI", "BAIS2", "CHL_RE", "CRI1", "CRI2", "EVI", "EVI2",
+                      "GRVI1", "GNDVI", "IRECI", "LAI_SAVI", "MCARI", "mNDVI705", "MSAVI2",
+                      "MSI", "mSR705", "MTCI", "nBR_RAW", "NDI_45", "NDII", "NDVI", "NDVI_G",
+                      "NDVI705", "NDWI1", "NDWI2", "PSRI", "PSRI_NIR", "RE_NDVI", "RE_NDWI", "S2REP",
+                      "SAVI", "SIPI", "SR", "CR_SWIR")
+  if (sel_indices == "ALL") {
+    sel_indices <- listindices
+  }
+  if ("ARI1" %in% sel_indices) {
+    ari1 <- (1 / refl[, sen2s2[["B3"]]]) - (1 / refl[, sen2s2[["B5"]]])
+    spectralindices$ARI1 <- ari1
+  }
+  if ("ARI2" %in% sel_indices) {
+    ari2 <- (refl[, sen2s2[["B8"]]] / refl[, sen2s2[["B2"]]]) - (refl[, sen2s2[["B8"]]] / refl[, sen2s2[["B3"]]])
+    spectralindices$ARI2 <- ari2
+  }
+  if ("ARVI" %in% sel_indices) {
+    arvi <- (refl[, sen2s2[["B8"]]] - (2 * refl[, sen2s2[["B4"]]] - refl[, sen2s2[["B2"]]])) / (refl[, sen2s2[["B8"]]] + (2 * refl[, sen2s2[["B4"]]] - refl[, sen2s2[["B2"]]]))
+    spectralindices$ARVI <- arvi
+  }
+  if ("BAI" %in% sel_indices) {
+    bai <- (1 / ((0.1 - refl[, sen2s2[["B4"]]])**2 + (0.06 - refl[, sen2s2[["B8"]]])**2))
+    spectralindices$BAI <- bai
+  }
+  if ("BAIS2" %in% sel_indices) {
+    bais2 <-  (1 - ((refl[, sen2s2[["B6"]]] * refl[, sen2s2[["B7"]]] * refl[, sen2s2[["B8A"]]]) / refl[, sen2s2[["B4"]]])**0.5) * ((refl[, sen2s2[["B12"]]] - refl[, sen2s2[["B8A"]]]) / ((refl[, sen2s2[["B12"]]] + refl[, sen2s2[["B8A"]]])**0.5) + 1)
+    spectralindices$BAIS2 <- bais2
+  }
+  if ("CCCI" %in% sel_indices) {
+    ccci <- ((refl[, sen2s2[["B8"]]] - refl[, sen2s2[["B5"]]]) / (refl[, sen2s2[["B8"]]] + refl[, sen2s2[["B5"]]])) / ((refl[, sen2s2[["B8"]]] - refl[, sen2s2[["B4"]]]) / (refl[, sen2s2[["B8"]]] + refl[, sen2s2[["B4"]]]))
+    spectralindices$CCCI <- ccci
+  }
+  if ("CHL_RE" %in% sel_indices) {
+    chl_re <- refl[, sen2s2[["B5"]]] / refl[, sen2s2[["B8"]]]
+    spectralindices$CHL_RE <- chl_re
+  }
+  if ("CRI1" %in% sel_indices) {
+    cri1 <- (1 / refl[, sen2s2[["B2"]]]) - (1 / refl[, sen2s2[["B3"]]])
+    spectralindices$CRI1 <- cri1
+  }
+  if ("CRI2" %in% sel_indices) {
+    cri2 <- (1 / refl[, sen2s2[["B2"]]]) - (1 / refl[, sen2s2[["B5"]]])
+    spectralindices$CRI2 <- cri2
+  }
+  if ("EVI" %in% sel_indices) {
+    evi <- 2.5 * (refl[, sen2s2[["B8"]]] - refl[, sen2s2[["B4"]]]) / ((refl[, sen2s2[["B8"]]] + 6 * refl[, sen2s2[["B4"]]] - 7.5 * refl[, sen2s2[["B2"]]] + 1))
+    spectralindices$EVI <- evi
+  }
+  if ("EVI2" %in% sel_indices) {
+    evi2 <- 2.5 * (refl[, sen2s2[["B8"]]] - refl[, sen2s2[["B4"]]]) / (refl[, sen2s2[["B8"]]] + 2.4 * refl[, sen2s2[["B4"]]] + 1)
+    spectralindices$EVI2 <- evi2
+  }
+  if ("GRVI1" %in% sel_indices) {
+    grvi1 <- (refl[, sen2s2[["B4"]]] - refl[, sen2s2[["B3"]]]) / (refl[, sen2s2[["B4"]]] + refl[, sen2s2[["B3"]]])
+    spectralindices$GRVI1 <- grvi1
+  }
+  if ("GNDVI" %in% sel_indices) {
+    gndvi <- (refl[, sen2s2[["B8"]]] - refl[, sen2s2[["B3"]]]) / (refl[, sen2s2[["B8"]]] + refl[, sen2s2[["B3"]]])
+    spectralindices$GNDVI <- gndvi
+  }
+  if ("IRECI" %in% sel_indices) {
+    ireci <- (refl[, sen2s2[["B7"]]] - refl[, sen2s2[["B4"]]]) * (refl[, sen2s2[["B6"]]] / (refl[, sen2s2[["B5"]]]))
+    spectralindices$IRECI <- ireci
+  }
+  if ("LAI_SAVI" %in% sel_indices) {
+    lai_savi <- - log(0.371  +  1.5 * (refl[, sen2s2[["B8"]]] - refl[, sen2s2[["B4"]]]) / (refl[, sen2s2[["B8"]]] +  refl[, sen2s2[["B4"]]] +  0.5)) / 2.4
+    spectralindices$LAI_SAVI <- lai_savi
+  }
+  if  ("MCARI" %in% sel_indices) {
+    mcari <- (1 - 0.2 * (refl[, sen2s2[["B5"]]] - refl[, sen2s2[["B3"]]]) / (refl[, sen2s2[["B5"]]] - refl[, sen2s2[["B4"]]]))
+    spectralindices$MCARI <- mcari
+  }
+  if ("mNDVI705" %in% sel_indices) {
+    mndvi705 <- (refl[, sen2s2[["B6"]]] - refl[, sen2s2[["B5"]]]) / (refl[, sen2s2[["B6"]]] + refl[, sen2s2[["B5"]]] - 2 * refl[, sen2s2[["B2"]]])
+    spectralindices$mNDVI705 <- mndvi705
+  }
+  if ("MSAVI2" %in% sel_indices) {
+    msavi2 <- ((refl[, sen2s2[["B8"]]] + 1) - 0.5 * sqrt(((2 * refl[, sen2s2[["B8"]]]) - 1)**2 + 8 * refl[, sen2s2[["B4"]]]))
+    spectralindices$MSAVI2 <- msavi2
+  }
+  if ("MSI" %in% sel_indices) {
+    msi <- refl[, sen2s2[["B11"]]] / refl[, sen2s2[["B8"]]]
+    spectralindices$MSI <- msi
+  }
+  if ("mSR705" %in% sel_indices) {
+    msr705 <- (refl[, sen2s2[["B6"]]] - refl[, sen2s2[["B2"]]]) / (refl[, sen2s2[["B5"]]] - refl[, sen2s2[["B2"]]])
+    spectralindices$mSR705 <- msr705
+  }
+  if ("MTCI" %in% sel_indices) {
+    mtci <- (refl[, sen2s2[["B6"]]] - refl[, sen2s2[["B5"]]]) / (refl[, sen2s2[["B5"]]] + refl[, sen2s2[["B4"]]])
+    spectralindices$MTCI <- mtci
+  }
+  if ("nBR_RAW" %in% sel_indices) {
+    nbr_raw <- (refl[, sen2s2[["B8"]]] - refl[, sen2s2[["B12"]]]) / (refl[, sen2s2[["B8"]]] + refl[, sen2s2[["B12"]]])
+    spectralindices$nBR_RAW <- nbr_raw
+  }
+  if ("NDI_45" %in% sel_indices) {
+    ndi_45 <- (refl[, sen2s2[["B5"]]] - refl[, sen2s2[["B4"]]]) / (refl[, sen2s2[["B5"]]] + refl[, sen2s2[["B4"]]])
+    spectralindices$NDI_45 <- ndi_45
+  }
+  if ("NDII" %in% sel_indices) {
+    ndii <- (refl[, sen2s2[["B8"]]] - refl[, sen2s2[["B11"]]]) / (refl[, sen2s2[["B8"]]] + refl[, sen2s2[["B11"]]])
+    spectralindices$NDII <- ndii
+  }
+  if ("NDSI" %in% sel_indices) {
+    ndisi <- (refl[, sen2s2[["B3"]]] - refl[, sen2s2[["B11"]]]) / (refl[, sen2s2[["B3"]]] + refl[, sen2s2[["B11"]]])
+    spectralindices$NDSI <- ndsi
+  }
+  if ("NDVI" %in% sel_indices) {
+    ndvi <- (refl[, sen2s2[["B8"]]] - refl[, sen2s2[["B4"]]]) / (refl[, sen2s2[["B8"]]] + refl[, sen2s2[["B4"]]])
+    spectralindices$NDVI <- ndvi
+  }
+  if ("NDVI_G" %in% sel_indices) {
+    ndvi_g <- refl[, sen2s2[["B3"]]] * (refl[, sen2s2[["B8"]]] - refl[, sen2s2[["B4"]]]) / (refl[, sen2s2[["B8"]]] + refl[, sen2s2[["B4"]]])
+    spectralindices$NDVI_G <- ndvi_g
+  }
+  if ("NDVI705" %in% sel_indices) {
+    ndvi705 <- (refl[, sen2s2[["B6"]]] - refl[, sen2s2[["B5"]]]) / (refl[, sen2s2[["B6"]]] + refl[, sen2s2[["B5"]]])
+    spectralindices$NDVI705 <- ndvi705
+  }
+  if ("NDWI1" %in% sel_indices) {
+    ndwi1 <- (refl[, sen2s2[["B8A"]]] - refl[, sen2s2[["B11"]]]) / (refl[, sen2s2[["B8A"]]] + refl[, sen2s2[["B11"]]])
+    spectralindices$NDWI1 <- ndwi1
+  }
+  if ("NDWI2" %in% sel_indices) {
+    ndwi2 <- (refl[, sen2s2[["B8A"]]] - refl[, sen2s2[["B12"]]]) / (refl[, sen2s2[["B8A"]]] + refl[, sen2s2[["B12"]]])
+    spectralindices$NDWI2 <- ndwi2
+  }
+  if ("PSRI" %in% sel_indices) {
+    psri <- (refl[, sen2s2[["B4"]]] - refl[, sen2s2[["B2"]]]) / (refl[, sen2s2[["B5"]]])
+    spectralindices$PSRI <- psri
+  }
+  if ("PSRI_NIR" %in% sel_indices) {
+    psri_nir <- (refl[, sen2s2[["B4"]]] - refl[, sen2s2[["B2"]]]) / (refl[, sen2s2[["B8"]]])
+    spectralindices$PSRI_NIR <- psri_nir
+  }
+  if ("RE_NDVI" %in% sel_indices) {
+    re_ndvi <- (refl[, sen2s2[["B8"]]] - refl[, sen2s2[["B6"]]]) / (refl[, sen2s2[["B8"]]] + refl[, sen2s2[["B6"]]])
+    spectralindices$RE_NDVI <- re_ndvi
+  }
+  if ("RE_NDWI" %in% sel_indices) {
+    re_ndwi <- (refl[, sen2s2[["B4"]]] - refl[, sen2s2[["B6"]]]) / (refl[, sen2s2[["B4"]]] + refl[, sen2s2[["B6"]]])
+    spectralindices$RE_NDWI <- re_ndwi
+  }
+  if ("S2REP" %in% sel_indices) {
+    s2rep <- 705 + 35 * (0.5 * (refl[, sen2s2[["B8"]]] + refl[, sen2s2[["B5"]]]) - refl[, sen2s2[["B6"]]]) / (refl[, sen2s2[["B7"]]] - refl[, sen2s2[["B6"]]])
+    spectralindices$S2REP <- s2rep
+  }
+  if ("SAVI" %in% sel_indices) {
+    savi <- 1.5 * (refl[, sen2s2[["B8"]]] - refl[, sen2s2[["B5"]]]) / (refl[, sen2s2[["B8"]]] + refl[, sen2s2[["B5"]]] + 0.5)
+    spectralindices$SAVI <- savi
+  }
+  if ("SIPI" %in% sel_indices) {
+    sipi <- (refl[, sen2s2[["B8"]]] - refl[, sen2s2[["B2"]]]) / (refl[, sen2s2[["B8"]]] - refl[, sen2s2[["B4"]]])
+    spectralindices$SIPI <- sipi
+  }
+  if ("SR" %in% sel_indices) {
+    sr <- refl[, sen2s2[["B8"]]] / refl[, sen2s2[["B4"]]]
+    spectralindices$SR <- sr
+  }
+  if ("CR_SWIR" %in% sel_indices) {
+    cr_swir <- refl[, sen2s2[["B11"]]] / (refl[, sen2s2[["B8A"]]] + (s2bands$B11 - s2bands$B8A) * (refl[, sen2s2[["B12"]]] - refl[, sen2s2[["B8A"]]]) / (s2bands$B12 - s2bands$B8A))
+    spectralindices$CR_SWIR <- cr_swir
+  }
+  res <- list("spectralindices" = spectralindices, "listindices" = listindices)
+  return(res)
+}
+
+#" this function identifies the bands of a given sensor with closest match to its spectral characteristics
+#"
+#" @param sensorbands numeric. wavelength in nanometer of the sensor of interest
+#" @param listbands numeric or list. Named vector or list of spectral bands corresponding to sensor
+#"
+#" @return numeric. band numbers of original sensor
+#" @export
+get_closest_bands <- function(sensorbands, listbands) {
+  sapply(listbands,  function(x) {
+    b <- which.min(abs(sensorbands - x))
+    names(b) <- ""
+    b
+  })
+}
+
+#" This function computes interquartile range (IQR) criterion,  which can be used
+#" as a criterion for outlier detection
+#"
+#" @param distval numeric. vector of distribution of values
+#" @param weightirq numeric. weighting factor appplied to IRQ to define lower and upper boudaries for outliers
+#"
+#" @return outlier_iqr numeric. band numbers of original sensor corresponding to S2
+#" @importFrom stats IQR quantile
+#" @export
+iqr_outliers <- function(distval, weightirq = 1.5) {
+  iqr <- IQR(distval,  na.rm = TRUE)
+  range_iqr <- c(quantile(distval,  0.25, na.rm = TRUE), quantile(distval,  0.75, na.rm = TRUE))
+  outlier_iqr <- c(range_iqr[1] - weightirq * iqr, range_iqr[2] + weightirq * iqr)
+  return(outlier_iqr)
+}
+
+#" This function selects bands from a raster or stars object
+#"
+#" @param refl RasterBrick,  RasterStack or list. Raster bands in the order of sensorbands.
+#" @param bands numeric. rank of bands to be read in refl
+#" @param reflfactor numeric. multiplying factor used to write reflectance in image ( == 10000 for S2)
+#"
+#" @return robj list. R object (default is raster,  stars if refl is stars object)
+#" @importFrom raster subset stack
+#" @export
+readrasterbands <- function(refl,  bands,  reflfactor = 1) {
+
+  # get equation for line going from CR1 to CR2
+  classraster <- class(refl)
+  if (classraster == "RasterBrick" || classraster == "RasterStack" || classraster == "stars") {
+    # if !reflfactor  ==  1 then apply a reflectance factor
+    if (classraster == "stars") {
+      robj <- refl[bands]
+    } else {
+      robj <- raster::subset(refl, bands)
+    }
+    if (!reflfactor == 1) {
+      robj <- robj / reflfactor
+    }
+  } else if (is.list(refl)) {
+    robj <- raster::stack(refl[bands]) # checks that all rasters have same crs/extent
+    if (!reflfactor == 1) {
+      robj <- robj / reflfactor
+    }
+  } else {
+    stop("refl is expected to be a RasterStack, RasterBrick, Stars object or a list of rasters")
+  }
+  return(robj)
+}
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/12a0b625-9ad5-4251-a57a-305e22edef2e.xml	Mon Jan 09 13:38:38 2023 +0000
@@ -0,0 +1,661 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<gmd:MD_Metadata xmlns:gmd="http://www.isotc211.org/2005/gmd"
+                 xmlns:gml="http://www.opengis.net/gml/3.2"
+                 xmlns:gts="http://www.isotc211.org/2005/gts"
+                 xmlns:gco="http://www.isotc211.org/2005/gco"
+                 xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
+                 xsi:schemaLocation="http://www.isotc211.org/2005/gmd http://www.isotc211.org/2005/gmd/gmd.xsd http://www.isotc211.org/2005/gmx http://www.isotc211.org/2005/gmx/gmx.xsd http://www.isotc211.org/2005/srv http://schemas.opengis.net/iso/19139/20060504/srv/srv.xsd">
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+  </gmd:fileIdentifier>
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+  </gmd:language>
+  <gmd:characterSet>
+      <gmd:MD_CharacterSetCode codeListValue="utf8"
+                               codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MD_CharacterSetCode"/>
+  </gmd:characterSet>
+  <gmd:hierarchyLevel xmlns:gn="http://www.fao.org/geonetwork"
+                       xmlns:gmx="http://www.isotc211.org/2005/gmx"
+                       xmlns:srv="http://www.isotc211.org/2005/srv"
+                       xmlns:xlink="http://www.w3.org/1999/xlink">
+      <gmd:MD_ScopeCode codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MD_ScopeCode"
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+                        <gco:CharacterString/>
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+         </gmd:individualName>
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+         </gmd:organisationName>
+         <gmd:contactInfo>
+            <gmd:CI_Contact>
+               <gmd:address>
+                  <gmd:CI_Address>
+                     <gmd:electronicMailAddress>
+                        <gco:CharacterString>audrey.alignier@inrae.fr</gco:CharacterString>
+                     </gmd:electronicMailAddress>
+                  </gmd:CI_Address>
+               </gmd:address>
+            </gmd:CI_Contact>
+         </gmd:contactInfo>
+         <gmd:role>
+            <gmd:CI_RoleCode codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#CI_RoleCode"
+                             codeListValue="pointOfContact"/>
+         </gmd:role>
+      </gmd:CI_ResponsibleParty>
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+      <gmd:CI_ResponsibleParty>
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+            <gco:CharacterString/>
+         </gmd:individualName>
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+            <gmd:CI_Citation>
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+               </gmd:title>
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+               <gmd:presentationForm>
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+                                               codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#CI_PresentationFormCode"/>
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+            </gmd:CI_Citation>
+         </gmd:citation>
+         <gmd:abstract>
+            <gco:CharacterString>"Relevés des adventices en cultures de maïs (2010) ZAAr" 
+
+Relevés des adventices dans 28 parcelles de maïs pour séparer les effets de la séquence des cultures de l'hétérogénéité spatio-temporelle du paysage des 10 dernières années. 
+
+Ce projet a été financé en partenariat par : 
+MINCyT: Ministerio de ciencia, tecnologia e innovation productiva (Argentine)
+ECOS-SUD (France)</gco:CharacterString>
+         </gmd:abstract>
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+            <gco:CharacterString/>
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+            <gmd:CI_ResponsibleParty>
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+               </gmd:individualName>
+               <gmd:organisationName>
+                  <gco:CharacterString>IFEVA/ Catédra de producción vegetal, facultad de agronomia, Universidad de Buenos Aires/ CONICET</gco:CharacterString>
+               </gmd:organisationName>
+               <gmd:positionName gco:nilReason="missing">
+                  <gco:CharacterString/>
+               </gmd:positionName>
+               <gmd:contactInfo>
+                  <gmd:CI_Contact>
+                     <gmd:phone>
+                        <gmd:CI_Telephone>
+                           <gmd:voice gco:nilReason="missing">
+                              <gco:CharacterString/>
+                           </gmd:voice>
+                           <gmd:facsimile gco:nilReason="missing">
+                              <gco:CharacterString/>
+                           </gmd:facsimile>
+                        </gmd:CI_Telephone>
+                     </gmd:phone>
+                     <gmd:address>
+                        <gmd:CI_Address>
+                           <gmd:deliveryPoint gco:nilReason="missing">
+                              <gco:CharacterString/>
+                           </gmd:deliveryPoint>
+                           <gmd:city gco:nilReason="missing">
+                              <gco:CharacterString/>
+                           </gmd:city>
+                           <gmd:administrativeArea gco:nilReason="missing">
+                              <gco:CharacterString/>
+                           </gmd:administrativeArea>
+                           <gmd:postalCode gco:nilReason="missing">
+                              <gco:CharacterString/>
+                           </gmd:postalCode>
+                           <gmd:country gco:nilReason="missing">
+                              <gco:CharacterString/>
+                           </gmd:country>
+                           <gmd:electronicMailAddress>
+                              <gco:CharacterString>spoggio@agro.uba.ar</gco:CharacterString>
+                           </gmd:electronicMailAddress>
+                        </gmd:CI_Address>
+                     </gmd:address>
+                  </gmd:CI_Contact>
+               </gmd:contactInfo>
+               <gmd:role>
+                  <gmd:CI_RoleCode codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#CI_RoleCode"
+                                   codeListValue="author"/>
+               </gmd:role>
+            </gmd:CI_ResponsibleParty>
+         </gmd:pointOfContact>
+         <gmd:pointOfContact>
+            <gmd:CI_ResponsibleParty>
+               <gmd:individualName>
+                  <gco:CharacterString>Jacques Baudry</gco:CharacterString>
+               </gmd:individualName>
+               <gmd:organisationName>
+                  <gco:CharacterString>BAGAP UMR 0980 INRAE Agrocampus Ouest ESA</gco:CharacterString>
+               </gmd:organisationName>
+               <gmd:positionName gco:nilReason="missing">
+                  <gco:CharacterString/>
+               </gmd:positionName>
+               <gmd:contactInfo>
+                  <gmd:CI_Contact>
+                     <gmd:phone>
+                        <gmd:CI_Telephone>
+                           <gmd:voice gco:nilReason="missing">
+                              <gco:CharacterString/>
+                           </gmd:voice>
+                           <gmd:facsimile gco:nilReason="missing">
+                              <gco:CharacterString/>
+                           </gmd:facsimile>
+                        </gmd:CI_Telephone>
+                     </gmd:phone>
+                     <gmd:address>
+                        <gmd:CI_Address>
+                           <gmd:deliveryPoint gco:nilReason="missing">
+                              <gco:CharacterString/>
+                           </gmd:deliveryPoint>
+                           <gmd:city gco:nilReason="missing">
+                              <gco:CharacterString/>
+                           </gmd:city>
+                           <gmd:administrativeArea gco:nilReason="missing">
+                              <gco:CharacterString/>
+                           </gmd:administrativeArea>
+                           <gmd:postalCode gco:nilReason="missing">
+                              <gco:CharacterString/>
+                           </gmd:postalCode>
+                           <gmd:country gco:nilReason="missing">
+                              <gco:CharacterString/>
+                           </gmd:country>
+                           <gmd:electronicMailAddress>
+                              <gco:CharacterString>jacques.baudry@inrae.fr</gco:CharacterString>
+                           </gmd:electronicMailAddress>
+                        </gmd:CI_Address>
+                     </gmd:address>
+                  </gmd:CI_Contact>
+               </gmd:contactInfo>
+               <gmd:role>
+                  <gmd:CI_RoleCode codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#CI_RoleCode"
+                                   codeListValue="author"/>
+               </gmd:role>
+            </gmd:CI_ResponsibleParty>
+         </gmd:pointOfContact>
+         <gmd:resourceMaintenance>
+            <gmd:MD_MaintenanceInformation>
+               <gmd:maintenanceAndUpdateFrequency>
+                  <gmd:MD_MaintenanceFrequencyCode codeListValue="notPlanned"
+                                                   codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MD_MaintenanceFrequencyCode"/>
+               </gmd:maintenanceAndUpdateFrequency>
+            </gmd:MD_MaintenanceInformation>
+         </gmd:resourceMaintenance>
+         <gmd:graphicOverview xmlns:srv="http://www.isotc211.org/2005/srv">
+            <gmd:MD_BrowseGraphic>
+               <gmd:fileName>
+                  <gco:CharacterString>https://www.osuris.fr/geonetwork/srv/api/records/12a0b625-9ad5-4251-a57a-305e22edef2e/attachments/sad_21_s.png</gco:CharacterString>
+               </gmd:fileName>
+               <gmd:fileDescription>
+                  <gco:CharacterString>thumbnail</gco:CharacterString>
+               </gmd:fileDescription>
+               <gmd:fileType>
+                  <gco:CharacterString>png</gco:CharacterString>
+               </gmd:fileType>
+            </gmd:MD_BrowseGraphic>
+         </gmd:graphicOverview>
+         <gmd:graphicOverview xmlns:srv="http://www.isotc211.org/2005/srv">
+            <gmd:MD_BrowseGraphic>
+               <gmd:fileName>
+                  <gco:CharacterString>https://www.osuris.fr/geonetwork/srv/api/records/12a0b625-9ad5-4251-a57a-305e22edef2e/attachments/sad_21.png</gco:CharacterString>
+               </gmd:fileName>
+               <gmd:fileDescription>
+                  <gco:CharacterString>large_thumbnail</gco:CharacterString>
+               </gmd:fileDescription>
+               <gmd:fileType>
+                  <gco:CharacterString>png</gco:CharacterString>
+               </gmd:fileType>
+            </gmd:MD_BrowseGraphic>
+         </gmd:graphicOverview>
+         <gmd:descriptiveKeywords>
+            <gmd:MD_Keywords>
+               <gmd:keyword>
+                  <gco:CharacterString>Biodiversité</gco:CharacterString>
+               </gmd:keyword>
+               <gmd:type>
+                  <gmd:MD_KeywordTypeCode codeListValue="theme"
+                                          codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MD_KeywordTypeCode"/>
+               </gmd:type>
+            </gmd:MD_Keywords>
+         </gmd:descriptiveKeywords>
+         <gmd:descriptiveKeywords>
+            <gmd:MD_Keywords>
+               <gmd:keyword>
+                  <gco:CharacterString>Maïs</gco:CharacterString>
+               </gmd:keyword>
+               <gmd:type>
+                  <gmd:MD_KeywordTypeCode codeListValue="theme"
+                                          codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MD_KeywordTypeCode"/>
+               </gmd:type>
+            </gmd:MD_Keywords>
+         </gmd:descriptiveKeywords>
+         <gmd:descriptiveKeywords>
+            <gmd:MD_Keywords>
+               <gmd:keyword>
+                  <gco:CharacterString>Forêt / milieu végétal</gco:CharacterString>
+               </gmd:keyword>
+               <gmd:keyword>
+                  <gco:CharacterString>Agriculture</gco:CharacterString>
+               </gmd:keyword>
+               <gmd:keyword>
+                  <gco:CharacterString>Ecologie et environnement</gco:CharacterString>
+               </gmd:keyword>
+               <gmd:keyword>
+                  <gco:CharacterString>Paysages et sites</gco:CharacterString>
+               </gmd:keyword>
+               <gmd:type>
+                  <gmd:MD_KeywordTypeCode codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MD_KeywordTypeCode"
+                                          codeListValue="theme"/>
+               </gmd:type>
+               <gmd:thesaurusName>
+                  <gmd:CI_Citation>
+                     <gmd:title>
+                        <gco:CharacterString>theme.EnvironnementFR.rdf</gco:CharacterString>
+                     </gmd:title>
+                     <gmd:date>
+                        <gmd:CI_Date>
+                           <gmd:date>
+                              <gco:Date>2017-03-20</gco:Date>
+                           </gmd:date>
+                           <gmd:dateType>
+                              <gmd:CI_DateTypeCode codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#CI_DateTypeCode"
+                                                   codeListValue="publication"/>
+                           </gmd:dateType>
+                        </gmd:CI_Date>
+                     </gmd:date>
+                     <gmd:identifier>
+                        <gmd:MD_Identifier>
+                           <gmd:code>
+                              <gmx:Anchor xmlns:gmx="http://www.isotc211.org/2005/gmx"
+                                          xmlns:xlink="http://www.w3.org/1999/xlink"
+                                          xlink:href="https://www.osuris.fr/geonetwork/srv/api/registries/vocabularies/external.theme.EnvironnementFR">geonetwork.thesaurus.external.theme.EnvironnementFR</gmx:Anchor>
+                           </gmd:code>
+                        </gmd:MD_Identifier>
+                     </gmd:identifier>
+                  </gmd:CI_Citation>
+               </gmd:thesaurusName>
+            </gmd:MD_Keywords>
+         </gmd:descriptiveKeywords>
+         <gmd:descriptiveKeywords xmlns:gn="http://www.fao.org/geonetwork"
+                                  xmlns:gmx="http://www.isotc211.org/2005/gmx"
+                                  xmlns:srv="http://www.isotc211.org/2005/srv"
+                                  xmlns:xlink="http://www.w3.org/1999/xlink">
+            <gmd:MD_Keywords>
+               <gmd:keyword>
+                  <gco:CharacterString>Installations de suivi environnemental</gco:CharacterString>
+               </gmd:keyword>
+               <gmd:keyword>
+                  <gco:CharacterString>Habitats et biotopes</gco:CharacterString>
+               </gmd:keyword>
+               <gmd:type>
+                  <gmd:MD_KeywordTypeCode codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MD_KeywordTypeCode"
+                                          codeListValue="theme"/>
+               </gmd:type>
+               <gmd:thesaurusName>
+                  <gmd:CI_Citation>
+                     <gmd:title>
+                        <gco:CharacterString>Registre de thème INSPIRE</gco:CharacterString>
+                     </gmd:title>
+                     <gmd:date>
+                        <gmd:CI_Date>
+                           <gmd:date>
+                              <gco:Date>2019-06-25</gco:Date>
+                           </gmd:date>
+                           <gmd:dateType>
+                              <gmd:CI_DateTypeCode codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#CI_DateTypeCode"
+                                                   codeListValue="publication"/>
+                           </gmd:dateType>
+                        </gmd:CI_Date>
+                     </gmd:date>
+                     <gmd:identifier>
+                        <gmd:MD_Identifier>
+                           <gmd:code>
+                              <gmx:Anchor xlink:href="https://www.osuris.fr/geonetwork/srv/api/registries/vocabularies/external.theme.httpinspireeceuropaeutheme-theme">geonetwork.thesaurus.external.theme.httpinspireeceuropaeutheme-theme</gmx:Anchor>
+                           </gmd:code>
+                        </gmd:MD_Identifier>
+                     </gmd:identifier>
+                  </gmd:CI_Citation>
+               </gmd:thesaurusName>
+            </gmd:MD_Keywords>
+         </gmd:descriptiveKeywords>
+         <gmd:resourceConstraints>
+            <gmd:MD_LegalConstraints>
+               <gmd:accessConstraints>
+                  <gmd:MD_RestrictionCode codeListValue="license"
+                                          codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MD_RestrictionCode"/>
+               </gmd:accessConstraints>
+               <gmd:useConstraints>
+                  <gmd:MD_RestrictionCode codeListValue="restricted"
+                                          codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MD_RestrictionCode"/>
+               </gmd:useConstraints>
+               <gmd:otherConstraints>
+                  <gco:CharacterString>licence CC-BY-NC 4.0</gco:CharacterString>
+               </gmd:otherConstraints>
+            </gmd:MD_LegalConstraints>
+         </gmd:resourceConstraints>
+         <gmd:resourceConstraints xmlns:gn="http://www.fao.org/geonetwork"
+                                  xmlns:gmx="http://www.isotc211.org/2005/gmx"
+                                  xmlns:srv="http://www.isotc211.org/2005/srv"
+                                  xmlns:xlink="http://www.w3.org/1999/xlink">
+            <gmd:MD_Constraints>
+               <gmd:useLimitation>
+                  <gco:CharacterString>Utilisation libre sous réserve de mentionner la source (a minima le nom du producteur) et la date de sa dernière mise à jour</gco:CharacterString>
+               </gmd:useLimitation>
+            </gmd:MD_Constraints>
+         </gmd:resourceConstraints>
+         <gmd:spatialRepresentationType>
+            <gmd:MD_SpatialRepresentationTypeCode codeListValue="textTable"
+                                                  codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MD_SpatialRepresentationTypeCode"/>
+         </gmd:spatialRepresentationType>
+         <gmd:spatialResolution xmlns:gn="http://www.fao.org/geonetwork"
+                                xmlns:gmx="http://www.isotc211.org/2005/gmx"
+                                xmlns:srv="http://www.isotc211.org/2005/srv"
+                                xmlns:xlink="http://www.w3.org/1999/xlink">
+            <gmd:MD_Resolution>
+               <gmd:distance>
+                  <gco:Distance uom="m">20</gco:Distance>
+               </gmd:distance>
+            </gmd:MD_Resolution>
+         </gmd:spatialResolution>
+         <gmd:language>
+            <gmd:LanguageCode codeList="http://www.loc.gov/standards/iso639-2/" codeListValue="fre"/>
+         </gmd:language>
+         <gmd:characterSet>
+            <gmd:MD_CharacterSetCode codeListValue="utf8"
+                                     codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MD_CharacterSetCode"/>
+         </gmd:characterSet>
+         <gmd:topicCategory>
+            <gmd:MD_TopicCategoryCode>farming</gmd:MD_TopicCategoryCode>
+         </gmd:topicCategory>
+         <gmd:extent>
+            <gmd:EX_Extent>
+               <gmd:temporalElement>
+                  <gmd:EX_TemporalExtent>
+                     <gmd:extent>
+                        <gml:TimePeriod gml:id="d2536736e509a1052958">
+                           <gml:beginPosition>2010-09-08</gml:beginPosition>
+                           <gml:endPosition>2008-09-14</gml:endPosition>
+                        </gml:TimePeriod>
+                     </gmd:extent>
+                  </gmd:EX_TemporalExtent>
+               </gmd:temporalElement>
+            </gmd:EX_Extent>
+         </gmd:extent>
+         <gmd:extent>
+            <gmd:EX_Extent>
+               <gmd:geographicElement>
+                  <gmd:EX_GeographicBoundingBox>
+                     <gmd:westBoundLongitude>
+                        <gco:Decimal>-1.6259765625</gco:Decimal>
+                     </gmd:westBoundLongitude>
+                     <gmd:eastBoundLongitude>
+                        <gco:Decimal>-0.9777832031250001</gco:Decimal>
+                     </gmd:eastBoundLongitude>
+                     <gmd:southBoundLatitude>
+                        <gco:Decimal>48.092651367188</gco:Decimal>
+                     </gmd:southBoundLatitude>
+                     <gmd:northBoundLatitude>
+                        <gco:Decimal>48.696899414063</gco:Decimal>
+                     </gmd:northBoundLatitude>
+                  </gmd:EX_GeographicBoundingBox>
+               </gmd:geographicElement>
+            </gmd:EX_Extent>
+         </gmd:extent>
+         <gmd:supplementalInformation>
+            <gco:CharacterString>France, Pleine-Fougères</gco:CharacterString>
+         </gmd:supplementalInformation>
+      </gmd:MD_DataIdentification>
+  </gmd:identificationInfo>
+  <gmd:distributionInfo>
+      <gmd:MD_Distribution>
+         <gmd:distributionFormat>
+            <gmd:MD_Format>
+               <gmd:name>
+                  <gco:CharacterString>ESRI Shapefile</gco:CharacterString>
+               </gmd:name>
+               <gmd:version>
+                  <gco:CharacterString>1.0</gco:CharacterString>
+               </gmd:version>
+            </gmd:MD_Format>
+         </gmd:distributionFormat>
+         <gmd:transferOptions>
+            <gmd:MD_DigitalTransferOptions/>
+         </gmd:transferOptions>
+      </gmd:MD_Distribution>
+  </gmd:distributionInfo>
+  <gmd:dataQualityInfo>
+      <gmd:DQ_DataQuality>
+         <gmd:scope>
+            <gmd:DQ_Scope>
+               <gmd:level>
+                  <gmd:MD_ScopeCode codeListValue="series"
+                                    codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MD_ScopeCode"/>
+               </gmd:level>
+            </gmd:DQ_Scope>
+         </gmd:scope>
+         <gmd:report xmlns:gn="http://www.fao.org/geonetwork"
+                     xmlns:gmx="http://www.isotc211.org/2005/gmx"
+                     xmlns:srv="http://www.isotc211.org/2005/srv"
+                     xmlns:xlink="http://www.w3.org/1999/xlink">
+            <gmd:DQ_DomainConsistency>
+               <gmd:result>
+                  <gmd:DQ_ConformanceResult>
+                     <gmd:specification>
+                        <gmd:CI_Citation>
+                           <gmd:title>
+                              <gco:CharacterString>L’article 7, paragraphe 1, de la directive 2007/2/CE correspond aux modalités techniques de l’interopérabilité : il s’agit du règlement relatif à l’interopérabilité : règlement n°1253/2013 du 21 octobre 2013 modifiant et complétant le règlement n°1089/2010 du 23 novembre 2010.</gco:CharacterString>
+                           </gmd:title>
+                           <gmd:date>
+                              <gmd:CI_Date>
+                                 <gmd:date>
+                                    <gco:Date>2013-10-21</gco:Date>
+                                 </gmd:date>
+                                 <gmd:dateType>
+                                    <gmd:CI_DateTypeCode codeList="http://standards.iso.org/iso/19139/resources/gmxCodelists.xml#CI_DateTypeCode"
+                                                         codeListValue="publication"/>
+                                 </gmd:dateType>
+                              </gmd:CI_Date>
+                           </gmd:date>
+                        </gmd:CI_Citation>
+                     </gmd:specification>
+                     <gmd:explanation gco:nilReason="missing">
+                        <gco:CharacterString/>
+                     </gmd:explanation>
+                     <gmd:pass>
+                        <gco:Boolean>true</gco:Boolean>
+                     </gmd:pass>
+                  </gmd:DQ_ConformanceResult>
+               </gmd:result>
+            </gmd:DQ_DomainConsistency>
+         </gmd:report>
+         <gmd:lineage>
+            <gmd:LI_Lineage>
+               <gmd:statement>
+                  <gco:CharacterString>Relevé sur le terrain des adventices du maïs</gco:CharacterString>
+               </gmd:statement>
+            </gmd:LI_Lineage>
+         </gmd:lineage>
+      </gmd:DQ_DataQuality>
+  </gmd:dataQualityInfo>
+</gmd:MD_Metadata>
\ No newline at end of file
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/Metadata_validation.txt	Mon Jan 09 13:38:38 2023 +0000
@@ -0,0 +1,5 @@
+
+Validation of metadata according to ISO 19139
+ 
+TRUE
+ according to ISO 19139 XML schemas! 
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/Mission.csv	Mon Jan 09 13:38:38 2023 +0000
@@ -0,0 +1,2 @@
+"","x"
+"1","SAFE"
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/NDVI.tabular	Mon Jan 09 13:38:38 2023 +0000
@@ -0,0 +1,10001 @@
+S2A_Subset	longitude	latitude	NDVI
+234.29	13.7115065213769	3.17883791054367	0.809475381273922
+233.06	13.7124063428304	3.17883903826377	0.814533326859802
+231.57	13.7133061646046	3.17884016519658	0.808063813546791
+228.73	13.7142059866993	3.17884129134209	0.814678963045019
+230.6	13.7151058091144	3.17884241670032	0.812649359747791
+228.85	13.7160056318495	3.17884354127124	0.806605795437084
+229.59	13.7169054549044	3.17884466505487	0.810899033966968
+239.74	13.7178052782789	3.1788457880512	0.808152735556578
+228.84	13.7187051019728	3.17884691026023	0.81364672485546
+217.93	13.7196049259859	3.17884803168196	0.821532180501305
+221.14	13.720504750318	3.17884915231639	0.813394469354217
+226.34	13.7214045749687	3.17885027216352	0.804053528518387
+233.05	13.722304399938	3.17885139122333	0.802659325445255
+237.25	13.7232042252255	3.17885250949585	0.809442899205139
+234.61	13.7241040508311	3.17885362698105	0.811580900151433
+225.7	13.7250038767545	3.17885474367895	0.820858536885687
+243.54	13.7259037029954	3.17885585958953	0.805156185849842
+238.98	13.7268035295538	3.17885697471281	0.814407496583588
+224.3	13.7277033564293	3.17885808904877	0.806504430597208
+229.94	13.7286031836218	3.17885920259741	0.814004957858205
+230.32	13.7295030111309	3.17886031535874	0.834195663335835
+221.94	13.7304028389565	3.17886142733275	0.830148226182174
+221.75	13.7313026670984	3.17886253851944	0.817970615615008
+231.52	13.7322024955563	3.17886364891882	0.826119282944004
+230.78	13.73310232433	3.17886475853087	0.837485155719081
+237.15	13.7340021534193	3.1788658673556	0.807358464103723
+240.64	13.7349019828239	3.178866975393	0.834367581945824
+240.36	13.7358018125437	3.17886808264308	0.814613959603203
+234.42	13.7367016425784	3.17886918910583	0.823334743713733
+232.98	13.7376014729278	3.17887029478125	0.812603727061501
+223.55	13.7385013035916	3.17887139966934	0.817053245524247
+228.45	13.7394011345697	3.1788725037701	0.820890651769377
+234.76	13.7403009658618	3.17887360708353	0.81472573939151
+241.63	13.7412007974676	3.17887470960963	0.800657983613983
+242.54	13.7421006293871	3.17887581134839	0.804551773444016
+233.03	13.7430004616199	3.17887691229981	0.818208709568542
+234.33	13.7439002941658	3.1788780124639	0.828198556142271
+224.33	13.7448001270246	3.17887911184064	0.815919459122387
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Binary file test-data/S2A_MSIL2A_20200306T015621_N0214_R117_T51JXN_20200306T034744.zip has changed
Binary file test-data/S2A_Subset has changed
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/S2A_Subset.hdr	Mon Jan 09 13:38:38 2023 +0000
@@ -0,0 +1,35 @@
+ENVI
+description = {
+  File Resize Result, x resize factor: 1.000000, y resize factor: 1.000000.
+  [Thu Jul 11 09:00:28 2019]}
+samples = 1000
+lines   = 1000
+bands   = 10
+header offset = 0
+file type = ENVI Standard
+data type = 2
+interleave = bil
+sensor type = Unknown
+byte order = 0
+x start = 998
+y start = 766
+map info = {UTM, 1.000, 1.000, 356780.000, 351500.000, 1.0000000000e+001, 1.0000000000e+001, 33, North, WGS-84, units=Meters}
+coordinate system string = {PROJCS["WGS_1984_UTM_Zone_33N",GEOGCS["GCS_WGS_1984",DATUM["D_WGS_1984",SPHEROID["WGS_1984",6378137.0,298.257223563]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]],PROJECTION["Transverse_Mercator"],PARAMETER["false_easting",500000.0],PARAMETER["false_northing",0.0],PARAMETER["central_meridian",15.0],PARAMETER["scale_factor",0.9996],PARAMETER["latitude_of_origin",0.0],UNIT["Meter",1.0]]}
+wavelength units = Nanometers
+z plot range = {-32768.00, 32767.00}
+data ignore value = 0
+default stretch = 0.000000 1000.000000 linear
+band names = {
+ Resize (band 02:S2A_T33NUD_20180104_Subset), 
+ Resize (band 03:S2A_T33NUD_20180104_Subset), 
+ Resize (band 04:S2A_T33NUD_20180104_Subset), 
+ Resize (band 05:S2A_T33NUD_20180104_Subset), 
+ Resize (band 06:S2A_T33NUD_20180104_Subset), 
+ Resize (band 07:S2A_T33NUD_20180104_Subset), 
+ Resize (band 08:S2A_T33NUD_20180104_Subset), 
+ Resize (band 08A:S2A_T33NUD_20180104_Subset), 
+ Resize (band 11:S2A_T33NUD_20180104_Subset), 
+ Resize (band 12:S2A_T33NUD_20180104_Subset)}
+wavelength = {
+  496.600006,  560.000000,  664.500000,  703.900024,  740.200012,  782.500000,
+  835.099976,  864.799988, 1613.699951, 2202.399902}
Binary file test-data/S2A_T33NUD_Plots.zip has changed
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/val_metadata.r	Mon Jan 09 13:38:38 2023 +0000
@@ -0,0 +1,41 @@
+#Rscript
+
+############################################
+##  Validate ISO 19139 metadata documen   ##
+############################################
+
+#####Packages : ncdf4,
+#               geometa,
+#               httr
+#               xml
+#               xml2
+library(geometa)
+
+#####Load arguments
+
+args <- commandArgs(trailingOnly = TRUE)
+
+if (length(args) < 1) {
+    stop("This tool needs at least 1 argument")
+}else {
+    input_data <- args[1]
+}
+
+##------------------------------------------##
+##      Read ISO 19139 from a file or url   ##
+##------------------------------------------##
+
+# Test depuis catalogue Indores http://indores-tmp.in2p3.fr/geonetwork/srv/fre/catalog.search#/metadata/112ebeea-e79c-422c-8a43-a5a8323b446b
+# <!--ISO 19139 XML compliance: NO-->
+input_data <- xml2::read_xml(input_data)
+
+dir.create("results")
+file.create("results/meta.xml")
+
+xml2::write_xml(input_data, file = "results/meta.xml")
+
+md <- geometa::readISO19139("results/meta.xml")
+
+
+# validate iso
+cat("\nValidation of metadata according to ISO 19139\n", md$validate(), file = "Metadata_validation.txt", fill = 1, append = FALSE)
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/val_metadata.xml	Mon Jan 09 13:38:38 2023 +0000
@@ -0,0 +1,67 @@
+<tool id="srs_metadata" name="Validate ISO 19139" version="@VERSION@" profile="20.01">
+    <description>metadata documents</description>
+    <macros>
+        <import>macro.xml</import>
+    </macros>
+    <requirements>
+        <requirement type="package" version="4.2.2">r-base</requirement>
+        <requirement type="package" version="2.12.2">r-r.utils</requirement>
+        <requirement type="package" version="3.5_21">r-raster</requirement>
+        <requirement type="package" version="1.0_7">r-sf</requirement>
+        <requirement type="package" version="1.5_32">r-rgdal</requirement>
+        <requirement type="package" version="0.6_1">r-rgeos</requirement>
+        <requirement type="package" version="0.5_5">r-stars</requirement>
+        <requirement type="package" version="1.5.0">r-stringr</requirement>
+        <requirement type="package" version="1.20">r-ncdf4</requirement>
+        <requirement type="package" version="0.7">r-geometa</requirement>
+        <requirement type="package" version="1.4.4">r-httr</requirement>
+        <requirement type="package" version="3.99_0.13">r-xml</requirement>
+        <requirement type="package" version="1.3.3">r-xml2</requirement>
+    </requirements>
+    <command detect_errors="exit_code"><![CDATA[
+        Rscript
+            '$__tool_directory__/val_metadata.r'
+            '$input'
+            '$output' 1> Metadata_validation.txt
+        ]]>
+    </command>
+    <inputs>
+        <param name="input" type="data" format="xml" label="XML file to validate"/>
+    </inputs>
+    <outputs>
+        <data name="output" from_work_dir="Metadata_validation.txt" format="txt" label="Validate metadata"/>
+    </outputs>
+    <tests>
+        <test>
+           <param name="input" value="12a0b625-9ad5-4251-a57a-305e22edef2e.xml"/>
+           <output name="output" value="Metadata_validation.txt"/>
+        </test>
+    </tests>
+    <help><![CDATA[
+==========================
+Preprocess Sentinel 2 data
+==========================
+
+
+**What it does**
+
+Read a ISO 19139 from a file or url into an object in the geometa model.
+
+**Input description**
+
+A metadata xml file or url.
+
++-------------+
+|   Metadata  |
++=============+
+| file or url |
++-------------+
+|     ...     |
++-------------+
+
+**Output**
+
+A text file saying TRUE or FALSE depending on if the xml metadata are conforme according to ISO 19139. If the xml metadata is not conform it will write down where are the issues.
+    ]]>    </help>
+        <expand macro="SRS_metaref"/>
+</tool>