A fully automatic processing chain for the systematic monitoring of surface water using Copernicus Sentinel 1 satellite data: first results of the SCO-CASCADES project.

Water is essential to life and health of various ecological and social systems. Unfortunately, water is one of the natural resources most impacted by climate change, with increasingly intense hydro-meteorological extremes (floods, droughts, etc.) and growing societal demand. To help manage this vulnerable resource, it is vital to assess and monitor its availability on a regular basis, as well as to track its trajectory over time to better understand the impact of global change on it. Surface water (lakes, rivers, flood plains, etc.) represents an important component of total water resources, and it is of primary importance to monitor it to better understand and manage the consequences of climate change. Surface water resources provide populations around the world with essential ecosystem services such as power generation, irrigation, drinking water for humans and livestock, and space for farming and fishing.

In this context, the SCO-CASCADES project implements end-to-end processing chains for satellite Earth observation data, including Sentinel-1 and 2 (S-1 and S-2), in order to provide surface water products (surface water body and inundation depth maps) that will be made available via an interactive platform co-constructed with identified users.

In the first phase of the project a fully automated Sentinel-1 based processing chain has been implemented. This chain is based on automatic multiscale image histogram parameterization followed by thresholding, region growing and chain detection applied on individual, subsequent pairs, and time series of S1 images. This chain enables us to derive various products: i) an exclusion layer identifying areas where water cannot be detected on Sentinel 1 image (e.g. Urban and forested areas), ii) permanent seasonal water body maps, iii) a water body map for each S1 image, iv) an uncertainty map characterizing the water body classification uncertainty, v) an occurrence map providing the number of times (over the time series) each pixel was covered by open water.

Here, we propose to present and evaluate the robustness of the processing chain and the resulting maps produced using multi-year S1 time series over two large scale sites: the Mekong flood plains between Kratie, the Tonle Sap lake and the Mekong Delta, and the Tsiribihina basin in Madagascar. The kappa score obtained from the comparison between S1 and S2-derived maps shows a good agreement yielding CSI and Kappa Cohen scores most of the time higher than 0.7 and sometimes reaching values higher than 0.9.