  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.
