Monitoring Indian ungauged small reservoirs volume from remote sensing: Feasibility, bias and perspectives

Abstract

What remote sensing products can be used to better quantify the water stored in hundreds of thousands Indian Small Reservoirs (SR)? This ungauged hydrological component of the water cycle is intermittently filled with rainwater runoff, constantly reshaped by farmers since last two decades, crucial for upstream irrigated agriculture. Given the small size and shallow depth of those reservoirs, usual remote sensing techniques (Altimeters and LIDAR) used in spatial hydrology to monitor their water level are not adapted. We evaluated the uncertainty of SR volume retrieval methods based on surface water estimates from Sentinel-2 and associated volumes from global available DEM at a medium to coarse resolution. Four pair of stereoscopic images at Very High Resolution (VHR) from Pléiades satellites were acquired during the last two dry hydrological years (2016 and 2019), when SR were totally empty. The Pléiades DEMs produced were cross validated with LIDAR IceSAT-2 products, and used to extract 504 SR bathymetries within an area covering 1,813 km2 located in the Telangana state (114,789 km2). We compared Pléiades based retrievals to freely available regional to global DEM to explore the regional volume retrieval Bias: ALOS World 3D-30 m, WorldDEM GLO-30 at 30 m TanDEM-X DEM at 90 m and one Indian DEM (CartoDEM at 30 m). The Mean Absolute Percentage Error (MAPE) of reservoir volumes from global DEMs range from 47% to 78%. MAPE are 17%, 29% and 46% for Pléiades DEM resampled at 12, 30 and 90 m, respectively. In a near future, upcoming stereoscopy satellite missions at lower costs and with larger coverage and shorter revisit such as CO3D will provide 12m or higher resolution DEMs that, if acquired in dry years, will lead to acceptable MAPE (< 20%), to monitor empty SR geometries throughout India and other semi-arid areas in the world.