Sen2Chain: An Open-Source Toolbox for Processing Sentinel-2 Satellite Images and Producing Time-Series of Spectral Indices

TL;DR

Sen2Chain is an open-source Python toolbox that automates processing large Sentinel-2 satellite image time series, including downloading, correction, cloud masking, and spectral index extraction, facilitating environmental monitoring.

Contribution

It introduces a customizable, automated, and parallelizable workflow for processing Sentinel-2 data and extracting spectral indices, accessible through a web tool for non-experts.

Findings

Enables large-scale, automated Sentinel-2 data processing

Provides simplified access to spectral indices like NDVI and NDWI

Supports creation of extensive earth observation image databases

Abstract

The increasing availability of free high-resolution earth observation data covering any point on the globe every few days led to the emergence of new remote sensing tools that can manipulate the very large volumes of data generated by those satellites. We present Sen2Chain, an open-source Python tool that can automate the processing of large time series of Sentinel-2 images for their use in various fields (e.g., environmental health, natural hazards, ecology). Sen2Chain allows downloading images from various earth observation data suppliers, applying geometric and atmospheric corrections using ESA's Sen2Cor tool, and generating and applying cloud masks. Sen2Chain's ability to extract time series of spectral indices (e.g NDVI, NDWI) provides simplified access to value-added environmental information for a wide range of end-users and applications. Sen2Chain enables all data processing stages to be customized and chained together, with the possibility to automate and parallelize the processing, and optimize data management. Sen2Chain is paving the way for the creation and processing of a large earth observation image database dedicated to users who require time-series and/or perform regular environmental observations. The Web tool Sen2Extract is also presented, which enables end-users with no expertise in remote sensing to easily extract time-series for 11 spectral indices values for specific regions of interest.