Daily Reservoir Water Level Reconstruction Under Sparse Observations Using Multisource Remote Sensing With Deep Learning and Data Assimilation

Abstract

Daily reservoir water level reconstruction remains challenging when satellite observations are sparse and temporally irregular. This study proposes a framework integrating multisource remote sensing, gated recurrent unit (GRU) priors, and ensemble Kalman filtering/smoothing (EnKF/EnKS), using the Fengtan Reservoir as a case study. Water surface areas derived from Landsat, Sentinel-1, and Sentinel-2 were fused and converted to satellite-derived water levels using an area–water level relationship established from ICESat-2 altimetry. Meteorological forcing variables and accumulated indices were used to generate GRU priors, while EnKF/EnKS assimilated satellite-derived water levels into the GRU priors to reconstruct daily water levels from 2000 to 2024. The results showed that validation based on 312 same-day matched observations yielded an R2 of 0.87 and a root-mean-square error (RMSE) of 3.23 m. During 2000–2014, the GRU prior achieved an RMSE of 5.51 m, an R2 of 0.53, and a Kling–Gupta efficiency (KGE) of 0.63. EnKS reduced the RMSE to 4.92 m and increased R2 and KGE to 0.68 and 0.82, respectively. EnKF/EnKS constrained GRU priors using sparse satellite observations, reducing error accumulation and trajectory drift during long-term inference. The framework provides a feasible approach for long-term water level reconstruction under sparse and irregular satellite observations.