In West Africa, lakes and reservoirs play a vital role as they are critical resources for drinking water, livestock, irrigation and fisheries. Given the scarcity of in situ data, satellite remote sensing is an important tool for monitoring lake volume changes in this region. Several methods have been developed to do this using water height and area relationships, but few publications have compared their performance over small and medium-sized lakes. In this work we compare four methods based on recent data from the Pleiades, Sentinel-2 and -3, ICESat-2 and GEDI missions over 16 lakes in the Central Sahel, ranging in area from 0.22 km² to 21 km². All methods show consistent results and are generally in good agreement with in situ data (height RMSE and volume NRMSE mostly below 0.30m and 11% respectively). The obtained height-area relationships show very little noise (fit RMSD mostly below 0.10m), except for the Sentinel-3-based method which tends to produce higher dispersion. The precision of the estimated water height is about 0.20m for Pleiades Digital Surface Models (DSMs) and less than 0.13m for the other methods. In addition, fine shape patterns are consistently observed over small height amplitudes, highlighting the ability to monitor shallow lakes with non-linear bathymetric behavior. Inherent limitations such as DSM quality, temporal coverage of DSM and lidar data, and spatial coverage of radar altimetry data are identified. Finally, we show that the combination of lidar and radar altimetry-based methods has great potential for estimating water volume changes in this region.

Lakes are the most prevalent and predominant water repositories on land surface. A primary objective of the Surface Water and Ocean Topography (SWOT) satellite mission is to monitor the surface water elevation, area, and storage change in Earth’s lakes. To meet this objective, prior information of global lakes, such as locations and benchmark extents, is required to organize SWOT’s KaRIn observations over time for computing lake storage variation. Here, we present the SWOT mission Prior Lake Database (PLD) to fulfill this requirement. This paper emphasizes the development of the “operational PLD”, which consists of (1) a high-resolution mask of ~6 million lakes and reservoirs with a minimum area of 1 ha, and (2) multiple operational auxiliaries to assist the lake mask in generating SWOT’s standard vector lake products. We built the prior lake mask by harmonizing the UCLA Circa-2015 Global Lake Dataset and several state-of-the-art reservoir databases. Operational auxiliaries were produced from multi-theme geospatial data to provide information necessary to embody the PLD function, including lake catchments and influence areas, ice phenology, relationship with SWOT-visible rivers, and spatiotemporal coverage by SWOT overpasses. Globally, over three quarters of the prior lakes are smaller than 10 ha. Nearly 96% of the lakes, constituting over half of the global lake area, are fully observed at least once per orbit cycle. The PLD will be recursively improved during the mission period and serves as a critical framework for organizing, processing, and interpreting SWOT observations over lacustrine environments with fundamental significance to lake system science.