Innovative method to estimate groundwater levels using remote sensing data

Abstract Effective monitoring of surface and groundwater resources is essential for sustainable water management, especially in arid and semi-arid regions where water scarcity poses a critical challenge. Remote sensing technologies have emerged as powerful tools for assessing hydrological conditions with reduced cost and improved spatial and temporal coverage. This study provides important insights into an innovative method for estimating water levels and depths in wells using feeding lakes and remote sensing data, with a focus on the effectiveness of spectral bands and model performance. Unlike previous studies that focused mainly on bathymetric estimation, the proposed approach integrates surface water depth estimation with groundwater fluctuation analysis in nearby wells using a simplified regression-based framework. The wells, from which water depths were estimated, are located in the Kima area of Aswan, Egypt. A linear regression model was successfully developed to estimate pond water depths over different time periods using Sentinel-2 Level 1 C reflectance data and in-situ depth measurements as reference, highlighting the vital role of field data in model calibration and validation. The analysis revealed that bands B2 and B3 showed the strongest correlation with measured depths, with R² values exceeding 0.88, indicating their suitability for bathymetric applications. Model validation showed slightly higher RMSE values in the validation phase (> 0.35 m) compared to calibration, with some estimates exceeding 1–2 m, confirming the model’s reliability. A strong correlation (average cross-correlation coefficient of 0.8) was also observed between pond surface water levels and groundwater levels in nearby wells, reflecting the dynamic interaction between surface and groundwater systems. Applying the methodology to historical data (2015–2023) yielded promising results, demonstrating its potential for long-term monitoring and environmental assessment. These findings highlight the value of remote sensing in hydrological studies, offering a cost-effective, scalable solution for groundwater management and sustainable water resource planning.