Amir Naghibi

Frequent and accurate measurements of inland Water Surface Elevation (WSE) are essential for effective water resource management. However, single-mission satellite altimetry often lacks the temporal resolution needed to capture detailed WSE changes. While multi-mission integration can improve temporal coverage, it is hindered by inter- and intra-mission biases arising from variations in sensor design, orbital characteristics, atmospheric effects, and environmental conditions. These biases, which have been insufficiently addressed in previous studies, are typically nonlinear, spatiotemporally variable, and require advanced methods for correction. This study proposes EILSTMNet, an Environmentally Informed Long Short-Term Memory (LSTM)-based Neural Network that enables multi-mission synergy of satellite altimetry data (Sentinel-3, Sentinel-6, and SWOT) by correcting altimetric measurements of WSE through the integration of environmental variables such as precipitation, temperature, and evapotranspiration. EILSTMNet employs stacked LSTM layers to capture temporal dependencies in environmental drivers, combined with a fully connected neural network that incorporates static inputs such as altimetric WSE, day of year, and satellite-specific identifiers. The proposed approach is validated over three U.S. lakes, Michigan, Ontario, and Winnebago, using in-situ gauge measurements. Results show that EILSTMNet-based estimates are significantly improved compared to altimeter-derived WSE measurements, reducing the Root Mean Squared Error from 0.31 m to 0.09 m and increasing the Pearson correlation coefficient from 0.69 to 0.93. Furthermore, the model demonstrates strong generalization to unseen time periods, highlighting its temporal transferability. The proposed approach refines multi-mission altimetric measurements, yielding temporally frequent, higher-accuracy WSE observations, thereby enhancing water resource management and advancing the understanding of hydrological processes.