get Sentinel-2 data

Jean-Baptiste Féret, Florian de Boissieu

2026-02-19

Source: vignettes/biodivMapR_01.Rmd

biodivMapR produces spectral diversity maps from raster data. Its application is technically not limited to optical imagery. Users experimenting with other types of remote sensing data are invited to ensure that the input information provided for remotely sensed diversity mapping are ‘ecologically relevant’ features describing vegetation biodiversity as expected in biodivMapR.

Raster data are mainly handled with the R package terra. Data format should be compliant with the drivers available with this package. Use gdal(drivers=TRUE) to check which drivers are available in your installation.

Here, Sentinel-2 imagery is used to illustrate the main functionalities of biodivMapR. This tutorial will first guide you through the download of Sentinel-2 data with R packages .

access Sentinel-2 imagery with preprocS2

preprocS2 is a package dedicated to Sentinel-2 data handling, which provides access to Spatio Temporal Asset Catalogs (STAC) via R scripts. It including wrapper functions for the rstac package. It allows downloading and preparing Sentinel-2 images for further processings using biodivMapR.

The STAC specification is a common language to describe geospatial information. STAC resources are available online from various institutional and private providers (such as Copernicus dataspace and Nicrosoft Planetary Computers). STAC catalogs provides access to imagery data via spatio-temporal queries. Hence, the first step is to define an area of interest (aoi), and a period or day of interest.

The default STAC collection is sentinel-2-l2a from the Microsoft Planetary Computer catalog.

Optionally, preprocS2 can download the geometry of acquisition (sun & view angles) corresponding to the Sentinel-2 acquisition. Access to geometry of acquisition is provided by the package CDSE.

Please follow instructions for proper installation of the package, and provide authentication for CDSE if you want to get geometry of acquisition by activating OAuth clients following this link.

spatial extent: define an area of interest (aoi)

First, load required packages and prepare input and output directories.

# clean workspace
rm(list = ls(all=TRUE)); gc()
if (rstudioapi::isAvailable()) 
  setwd(dirname(rstudioapi::getSourceEditorContext()$path))
library(preprocS2)  # install instructions: https://github.com/jbferet/preprocS2
library(biodivMapR) # install instructions: https://github.com/jbferet/biodivMapR
library(spinR)      # install instructions: https://github.com/jbferet/spinR
library(terra)
library(sf)
# 1.1- define input & output directories
input_dir_vect <- './01_DATA/amazon/vector_data'
input_dir_rast <- './01_DATA/amazon/raster_data/sentinel-2'
dir.create(path = input_dir_rast, showWarnings = F, recursive = T)
dir.create(path = input_dir_vect, showWarnings = F, recursive = T)

The spatial extent of the area of interest (aoi) needs to be defined as a vector file. Such aoi can be defined with QGIS, Google Earth, or your favorite tool.

Here an aoi located in Brazilian Amazon forest is defined. The site is located next to Guedes, Japurá - State of Amazonas, Brazil.

# 1.2- define area of interest
bbox <- sf::st_bbox(obj = c('xmin' = -66.23, 'ymin' = -2.11, 
                            'xmax' = -65.76, 'ymax' = -1.91))
sf::st_crs(x = bbox) <- 4326
aoi_path <- file.path(input_dir_vect, 'amazon.gpkg')
sf::st_write(obj = bbox_to_poly(x = bbox), dsn = aoi_path, 
             driver = 'GPKG', delete_layer = TRUE)

time range: define a date of acquisition

A Sentinel-2 acquisition with minimal cloud cover needs to be identified. The Copernicus Browser is a useful service to explore the availability of Sentinel-2 acquisitions. In addition to spatio-temporal filters, it includes filters such as the maximum cloud cover.

Images with a maximum cloud cover < 5%, are identified for August 2024. An acquisition from 23 August 2024 shows good conditions of acquisition.

download Sentinel-2 acquisition from online STAC resource

preprocS2 allows downloading from multiple providers. Microsoft Planetary computer is currently the default option.

The function get_s2_raster downloads Sentinel-2 data corresponding to the spatio-temporal query.

  • aoi_path: path for vector layer. Must include a unique polygon
  • datetime: date of acquisition, provided as a Date object.
  • stac_info: list including provider identifier (default = mpc)
  • output_dir: output directory where to store data
  • site_name: this will allow identifying rasters later
  • options: options including maximum cloud cover and higher level processing
# 1.4- download  S2 acquisition
datetime <- as.Date('2024-08-23')
stac_info <- list('provider' = 'mpc')
site_name = 'amazon'
options <- set_options_preprocS2(fun = 'get_s2_raster')
options$overwrite <- FALSE
list_files <- get_s2_raster(aoi_path = aoi_path, 
                            datetime = datetime, 
                            stac_info = stac_info, 
                            output_dir = input_dir_rast, 
                            site_name = site_name, 
                            options = options)

rast_path <- list_files$Refl_L2A                # S2 L2A reflectance
mask_path <- list_files$vegetation_mask         # S2 binary mask identifying vegetation, discarding clouds & shadows

The information resulting from the spatiotemporal query are saved in the directory defined with the variable input_dir_rast. It follows this file/folder structure:

├── collections
    ├── plot_001.rds
├── raster_samples
    ├── amazon_001_2024-08-23.tiff
    ├── amazon_001_2024-08-23_BIN.tiff
    ├── amazon_001_2024-08-23_BIN_v2.tiff
    └── amazon_001_2024-08-23_SCL.tiff
├── s2_tiles_amazon.rds
└── s2_footprint_amazon.gpkg
  • collections/plot_001.rds corresponds to the item collection resulting from the spatiotemporal query. Use the R function readRDS to access the content of the file.
readRDS('./01_DATA/amazon/raster_data/sentinel-2/collections/plot_001.rds')

# ###Items
# - features (1 item(s)):
#   - S2A_MSIL2A_20240823T144731_R139_T19MHT_20240823T215907
# - assets: 
# AOT, B01, B02, B03, B04, B05, B06, B07, B08, B09, B11, B12, B8A, datastrip-metadata, granule-metadata, inspire-metadata, product-metadata, rendered_preview, safe-manifest, SCL, tilejson, visual, WVP
# - item's fields: 
# assets, bbox, collection, geometry, id, links, properties, stac_extensions, stac_version, type

  • raster_samples includes:

    ─ amazon_001_2024-08-23.tiff: L2A (surface reflectance) Sentinel-2 data ─ amazon_001_2024-08-23_BIN.tiff: binary mask derived from amazon_001_2024-08-23_SCL.tiff and focusing on vegetation class.
    ─ amazon_001_2024-08-23_BIN_v2.tiff: binary mask produced from alternative radiometric criteria, including NDVI mask to remove non vegetated pixels, Blue mask to remove hazy/cloudy pixels, and NIR mask to remove shaded pixels. You can adjust threshold for cloudMask (B02), shadeMask (B08), and NDVIMask. See here for additional information on how these masks work. ─ amazon_001_2024-08-23_SCL.tiff: scene classification provided by sen2cor atmospheric correction.

  • s2_tiles_amazon.rds contains the Sentinel-2 tile ID for the scene

  • s2_footprint_amazon.gpkg contains the footprint of the Sentinel-2 tile ID

The animated gif shows the Sentinel-2 acquisition. The pixels masked by the binary mask amazon_001_2024-08-23_BIN_v2.tiff are greyed. The surface reflectance in sentinel-2 L2A products is rescaled from 0-1 to 0-10000 and the data are stored in INT16 to save space. This allows 50% space saved compared to FLOAT32.

The following animation displays the sentinel-2 acquisition along with the vegetation mask and with the following color dynamic:

  • Red band: Band 04 (0 - 1000).

  • Green band: Band 03 (0 - 1000).

  • Blue band: Band 02 (0 - 1000).

Fig. 1. sentinel-2 acquisition of Brazilian Amazon forest downloaded with preprocS2

 

Next step here.