Analyzing Water and Vegetation indices based on Sentinel-2 data

The first challenge identified for the Geospatial Intelligence domain investigates fast and intuitive means to derive information from satellite data on specific phenomena, through multi-temporal analysis of thematic indices. This challenge focuses on water and vegetation indices.

To address this challenge BETTER explores the potential of long-term time series derived from Sentinel-2 data for the production of accurate index maps. This requires extensive computational capabilities to both process and to transform into easy and intuitive results for processing by end-users. Time series compiled by the European Union Satellite Centre (project partner SatCen) are primarily used in conjunction with other freely available satellite (e.g., Landsat) and other geospatial data (e.g., GIS layers, DEM). From Sentinel-2, the multi-temporal stack of Level 1C (and 2A when available), NDVI (Normalized Difference Vegetation Index) and NDWI (Normalized Difference Water Index) will be extracted.

 

               

 Moisture indices derived from Sentinel 2

The results of this challenge will mostly interest Space and Security stakeholders, especially those with an interest to monitor water and vegetation indexes to increase citizen security, e.g., to predict floods and better manage other natural and man-made hazards and phenomena.