Amir Naghibi

Inland surface waters are vital for the global carbon, energy, and water cycles; however, they are affected by climate change and human activities. Continuous monitoring of Water Surface Elevation (WSE) is essential for assessing water quantity and ensuring sustainable management. Traditional in-situ WSE measurements often face high costs and accessibility challenges. This study examines the use of satellite altimetry, specifically from the Sentinel-3 (S3) Radar Altimeter (SRAL), to enhance WSE estimates through Machine Learning (ML) techniques, the Random Forest (RF) algorithm. By applying the RF algorithm to SRAL data over Lake Michigan, we corrected altimeter-derived WSE values against in-situ data. Our results show a reduction in the Root Mean Squared Error (RMSE)—from 35 cm for the mean WSE across all Virtual Station (VS) points to 9 cm for the RF-corrected WSE—when compared against in-situ measurements using a Leave-One-Date-Out (LODO) approach. Additionally, the R-squared (R²) improved from 0.88 to 0.96, indicating a stronger correlation between the corrected WSE and in-situ measurements. These findings highlight the effectiveness of the ML method in improving the accuracy of altimetry-based WSE estimation. Moreover, this research underscores the potential of integrating ML methods with remote sensing techniques for enhanced inland water resource management.