Towards operational surface water extent estimation from C-band sentinel-1 SAR imagery

The extent and the location of surface water are very essential information related to human and climate activity. The global maps delineating surface water extent have been mainly produced from the optical imagery due to its historical record and maturity of the estimation algorithms. With the rich archive of the optical images accumulated over the last 30 years, the long-term changes of the permanent water may be fully understood. However, the ability of the optical sensors to monitor the temporal fluctuations of water extent is limited by cloud coverage and sunlight. On the other hand, the active sensors utilizing the microwave signal, such as the Sentinel-1 C-band Synthetic Aperture Radar (SAR), potentially can monitor the dynamics of surface water extent regardless of the weather conditions and daylight.

We present a new algorithm for surface water extent estimation from Sentinel-1 C-band imagery by incorporating threshold estimation, the fuzzy logic classification, dark land masking based on the global land cover maps, and refinement step using contextual information. We evaluate the performance and thematic accuracy of the automatic processing chain for various sites covering surface water worldwide. We also evaluate the estimation accuracy over regions with more dynamic water extent using independent water extent estimates from Harmonized Landsat-8 and Sentinel-2 data as well as high resolution optical imagery. The results from the algorithm verification suggests that the surface water detection processor is able to achieve satisfying classification results with user accuracies of 82.0% ~ 99.1% and the producer accuracy of 93.9 % ~ 99.7% over areas with stable water extent close to permanent water. The satisfying performance of the algorithm over diverse global case studies including areas with stable permanent water, floods, new water bodies (e.g., dam construction), drying lakes and areas with significant water variation (e.g, due to monsoon or tidal effects) indicates that the new algorithm can be operational at a global scale and with short latency.