Settings
Map
Plot
Welcome to Sen2Extract!
Sen2Extract is a R Shiny web application that allows users to extract time series of spectral indices (previously calculated from Sentinel-2 L2A satellite images) for specified locations and dates. This can only extract values from images that are already available in the database.
This application was developed by UMR ESPACE-DEV (IRD, Univ Antilles, Univ Guyane, Univ Montpellier, Univ Réunion) at University of La Réunion – SEAS-OI Station, as part of the S2-Malaria Project. Funded by CNES (TOSCA 2017-2020), this project aims to use Sentinel-2 satellite data for epidemiological surveillance, intially focused on malaria. Its main objective is to provide quick and easy access to sentinel-2 images and the spectral indices derived from them.
How does the application work ?
Extract
The user specifies his sites of interest, the time period and the indices to be extracted. The application then submits this query on a server and sends the results directly to the user by email in CSV format. Once the email has been receiver, the user can open each generater CSV file to display the data either as a table in the Map data or graphically in the Map Chart.
1-Create and zip your CSV file: The geometries could be points or polygones and each geometry must have an ID value .
2-Import your zipped shapefile: Using the upload button, you can import your shapefile. Once imported, it will automatically be reprojected to WGS84 (EPSG:4326). Only one zip file can be imported at a time. Your ZIP file must contain only five files with the following extensions : .shp, .cpg, .shx, .prj, .dbf.
3-Check if index are available: The Sentinel-2 tile must be displayed on the map. For example, for Réunion Island, tile 40KCB should be visible. By clicking on the tile, a table displaying the available indices and their acquisition dates appears. If no tile is displayed on the map or if no date is available in the table for the selected index, this means that the required data are not available and that it is not possible to generate the time series.
4- Choose the desired index: The Sen2Extract application provides 12 spectral indices, which can be selected individually or in combinaison. More information about index on the Sen2chain documentation.
-NDVI : Normalized Difference Vegetation Index. This vegetation index is sensitive to the vigor and density of vegetation and is commonly used to assess vegetation health and biomass.
-NDBI: Normalized Difference Built-up Index. This index is used to highlight built-up areas and is particularly useful for mapping urban or developed surfaces.
-NDWIGAO : Normalized Difference Water Index proposed by Gao (1996). This index is aimed at monitoring changes in vegetation water content using near-infrared (NIR) and short-wave infrared (SWIR) wavelengths.
-NDWIMCF : Normalized Difference Water Index proposed by McFeeters (1996). This index is used to detect and monitor water bodies using green and near-infrared (NIR) wavelengths.
-MNDWI: Modified Normalized Difference Water Index. This index is designed to enhance the detection of open water bodies and improve the discrimination of water from built-up areas.
-NDRE: Normalized Difference Red Edge Index. This index is sensitive to chlorophyll content and vegetation condition and is particularly useful for assessing vegetation health and detecting variations in plant stress.
-IRECI: Inverted Red-Edge Chlorophyll Index. This index is designed to estimate chlorophyll content in vegetation and is sensitive to changes in leaf chlorophyll concentration.
-BIGR: Blue–Green Index Ratio. This index uses the blue and green spectral bands to characterize vegetation and surface properties.
-BIRNIR: Blue–Red/Near-Infrared Index Ratio. This index combines the blue, red, and near-infrared spectral bands to characterize vegetation and surface properties.
-BIBG: Blue–Infrared/Blue–Green Index. This index combines information from the blue, green, and near-infrared spectral bands and can be used to characterize vegetation and surface properties.
-EVI: Enhanced Vegetation Index. This vegetation index is designed to improve sensitivity to vegetation in areas with dense biomass and to reduce the influence of atmospheric effects and soil background.
-NBR: Normalized Burn Ratio. This index is primarily used to identify burned areas and assess the severity of vegetation disturbance caused by fires, using near-infrared (NIR) and short-wave infrared (SWIR) wavelengths.
5- Select your period: A calendar allows you to choose your dates. You can easily switch between weekly, monthly or yearly views by clicking on the date at the top of the calendar.
6- Enter your email address
7- Click on the 'sent now !' button
8- Download and unzip : Once you receive the mail, download the ZIP file by clicking on the link and unzip your file.
Map data
Browse the CSV file to display the data table directly
Map chart
Browse the CSV file to display the time series chart. By default, the application will uses the date field for the X-axis and the mean for the Y-axis. You can select other fields and group the values by entity code to improve visualization.
Data informations
In the ZIP file
- Readme.txt
- CSV file (s) named like this : [Name of the layer][_WGS84][date 1][date 2].csv
Output variable of the CSV file :
WARNING All pixel statistics have a 10000 factor (min,max,mean,std,median,percentile_25, percentile_75).
| Champ | Description |
|---|---|
| tile | Tile name |
| filename | Name of the file used for extraction |
| count | Number of pixels used to compute the statistics |
| nodata | Number of pixels with no data. count + nodata should be constant for an entity |
| nbcld | Number of pixels detected as clouds. These pixels are considered as nodata. |
| cldprb | Mean cloud probability of the counted pixels |
| min | Minimum pixel value within the polygon |
| max | Maximum pixel value within the polygon |
| mean | Mean pixel value within the polygon |
| std | Standard deviation of the pixel value within the polygon |
| median | Median pixel value within the polygon |
| percentile_25 | 25th percentile of the pixel value within the polygon |
| percentile_75 | 75th percentile of the pixel value within the polygon |
| Other fields | All fields from the attribute table of the input shapefile. |
Origin of Sentinel-2 L2A data
Sentinel-2 images are freely provided by the Copernicus Programme, developed and operated by the European Space Agency (ESA). The Sentinel-2 mission comprises a constellation of polar, sun-synchronous Earth-observation satellites. Sentinel-2A was launched on 23 June 2015, Sentinel-2B on 7 March 2017, and Sentinel-2C on 5 September 2024. They provide high spatial resolution (10 m to 60 m) and high temporal revisit frequency (every 5 days) over land and coastal waters. The indices provided by Sen2Extract are calculated from Sentinel-2 L2A images (i.e. after atmospheric, terrain and cirrus corrections). They are calculated at SEAS-OI Station using Sen2Chain, which was also developed through the S2-Malaria Project (CNES TOSCA 2017-2019) and the RenovRisk-impact Project (INTERREG 2018-2020). The processing chain, Sen2Chain is a simple utility for downloading and processing Sentinel-2 images. It uses the sentinelsat and peps_download packages to find and download data, and ESA's Sen2Cor processor to perform atmospheric, terrain and cirrus correction.
