Water stock monitoring is a major issue for society on a local and global scale. Sentinel-1&2 satellites provide frequent acquisitions to track water surface dynamics, proxy variables to enable water surface volume monitoring. How do we combine such observations along time for each sensor? What advantages and disadvantages of single-date, monthly or time-windowed estimations? In this context, we analysed the impact of merging information through different types and lengths of time-windows. Satellite observations were processed separately on optical (Sentinel-2) and radar (Sentinel-1) water detectors at 10 m resolution. The analysis has been applied at two scales. First, validating with 26 large scenes (110 × 110 km) in different climatic zones in France, time-windows yielded an improvement on radar detection (F1-score improved from 0.72 to 0.8 for 30 days on average logic) while optical performances remained stable (F1-score 0.89). Second, validating reservoir area estimations with 29 instrumented reservoirs (20–1250 ha), time-windows presented in all cases an improvement on both optical and radar error for any window length (5–30 days). The mean relative absolute error in optical area detection improved from 16.9% on single measurements to 12.9% using 15 days time-windows, and from 22.15% to 15.1% in radar detection). Regarding reservoir filling rates, we identified an increased negative bias for both sensors when the reservoir is nearly full. This work helped to compare accuracies of separate optical and radar capabilities, where optical statistically outperforms radar at both local and large scale to the detriment of less frequent measurements. Furthermore, we propose a geomorphological indicator of reservoirs to predict the quality of radar area monitoring (R² = 0.58). In conclusion, we suggest the use of time-windows on operational water mapping or reservoir monitoring systems, using 10–20 days time-windows with average logic, providing more frequent and faster information to water managers in periods of crisis (e.g., water shortage) compared to monthly estimations.

Rapid mapping of disasters using any kind of satellite imagery is a challenge. The faster the response, the better the service is for the end users who are managing the emergency activities. Indeed, production rapidity is crucial whatever the satellite data in input. However, the speed of delivery must not be at the expense of crisis information quality. The automated flood and fire extraction pipelines, presented in this technical note, make it possible to take full advantage of advanced algorithms in short timeframes, and leave enough time for an expert operator to validate the results and correct any unmanaged thematic errors. Although automated algorithms aren’t flawless, they greatly facilitate and accelerate the detection and mapping of crisis information, especially for floods and fires. ExtractEO is a pipeline developed by SERTIT and dedicated to disaster mapping. It brings together automatic data download and pre-processing, along with highly accurate flood and fire detection chains. Indeed, the thematic quality assessment revealed F1-score values of 0.91 and 0.88 for burnt area and flooded area detection, respectively, from various kinds of high- and very-high- resolution data (optical and SAR).