Enhanced Water Level Monitoring for Small and Complex Inland Water Bodies Using Optical and SAR Retrievals

Monitoring water levels in lakes and reservoirs forms a critical component of sustainable water resource management, particularly in regions where direct measurements are costly, time consuming or impossible. Traditionally, ground-based sensors are used as the primary means of water level observation. In recent past, remote sensing has emerged as a vital alternative for areas that are inaccessible, have sparse monitoring infrastructure or located in the transboundary regions. However, recent studies have highlighted limitations in temporal resolution required for immediate responses to water-related conflicts. We present here a novel methodology for enhancing the temporal resolution of water level time series derived from altimetry satellites by integrating data from other satellite types, such as optical (Harmonized Landsat Sentinel-2) and SAR (Sentinel-1), particularly for small and complex inland water bodies. Our approach leverages DEM-driven water masks with 1-meter intervals to systematically calculate reflectance values at various elevation levels, identifying water levels based on the most significant reflectance differences. Unlike static methods with fixed thresholds, our methodology dynamically adjusts thresholds according to regional and temporal variations, ensuring greater accuracy and adaptability. To mitigate the limitations of optical data, such as cloud coverage during the wet season, we integrated SAR data as a further enhancement to the developed approach. We tested this methodology on four reservoirs in South Korea—Chungju, Andong, Daecheong, and Juam—representing diverse hydrological characteristics. The results demonstrated significant improvements in the accuracy of water level estimation, even for highly variable and small water bodies. Further, the proposed method shows robustness across multiple satellite datasets while effectively addressing data gaps, providing a scalable and globally applicable framework for advancing water level monitoring.  The approach underscores its potential to enhance hydrological assessment and water management, particularly in under-monitored regions.