A new model for high-accuracy monitoring of water level changes via enhanced water boundary detection and reliability-based weighting averaging

Accurate measurement of water levels is essential for effectively managing reservoirs to proactively mitigate flooding and drought. Nonetheless, the inaccuracies in measurements derived from gauging station and remote sensing images impose constraints to water resource management. In this study, we developed a novel water level estimation model which utilizes solely the altitude of reliable water boundary pixels to improve the accuracy. The enhanced water boundary detection, incorporating preprocessing steps such as image filtering, resampling, and polarization multiplication, was applied to achieve sub-pixel precision in detecting water boundaries. The water boundary pixels located in layover and shadow regions, which could be misidentified due to distortion error, are eliminated based on backward geolocation. Ambiguous water boundaries, potentially indicating land with low intensity, were defined by computing their absolute derivatives, and removed. Finally, to enhance water level precision, the model computed water levels by averaging the altitudes of boundary pixels with weighting factors of local incidence angle, derivatives of detected water boundaries, and altitude distribution. Compared with the previous studies utilizing water boundaries, the proposed model demonstrated outstanding performance in improving the accuracy, up to 1/40th smaller than the spatial resolution of SAR images in Mean Absolute Error (MAE). The validation was executed over the results from >700 Sentinel-1 against the in-situ measurements obtained from multiple reservoirs and streams with significant water level fluctuations in the Korean peninsula. In this process, we found that the water boundaries located on the layover and shadow regions significantly influence the dispersion of altitude of reliable water boundary pixels. This study demonstrates the proposed model relying on remote sensing data without in-situ measurements, which holds potential applicability under situations where in-situ data are unavailable.