A New Surface Waters Downscaling Approach Applicable at Global Scale - Thu-Hang Nguyen; Filipe Aires (Remote Sensing, 2024)
A surface water extent downscaling framework was developed in the past using a floodability index based on topography. We presented here a new downscaling approach including several improvements. (1) The use of a new Floodability Index (FI), including better integration of auxiliary permanent waters (i.e., presence of water during the whole time record). By using this updated FI, the new downscaling became a true data-fusion with permanent water databases originating mainly from visible observations. (2) Some discontinuities between low resolution cells have been reduced thanks to a new smoothing algorithm. (3) Finally, a coastal extrapolation scheme has been presented to deal with coarse resolution data contaminated by the ocean. This new and complex downscaling framework was tested here on the GIEMS (Global Inundation Extent from Multi-Satellite) database but the approach is generalizable and any surface water database could be used instead. It was shown that this new downscaling procedure (including several processing steps, algorithms and data sources) is a significant improvement compared to the previous version thanks to the new floodability index and the downscaling processing chain. Compared to the previous version, the downscaling results (GIEMS-D) were more coherent with the permanent water database and preserved better the original low-resolution information (e.g., mean scare error water fraction (0–1) of 0.0041 for the old version, and 0.0018 for the new version, over flooded areas in the Amazon). GIEMS-D has also been evaluated at the global scale and over the Amazon basin using independent datasets, showing an overall good performance of the downscaling.