Integrating satellite altimetry and SAR technology for Manchar Lake water monitoring

Abstract A considerable share of the global inland water that fulfills human needs and supports the natural ecosystem is stored in lakes. Mismanaging these resources may trigger extreme floods and droughts. The Manchar Lake, Sindh province, Pakistan, despite being the largest freshwater lake in the country, lacks long-term monitoring records. Satellite based observation may be helpful to address this gap. This study utilizes multi-mission altimetry and Synthetic Aperture Radar (SAR) data to monitor water levels and volumetric variations (2018–2023) of the strategically important Manchar Lake. Data from three altimetry missions—Sentinel-3, ICESat-2, and Jason-3— were acquired, processed, and validated from in situ measurements. Moreover, processing surface area variations from Sentinel-1 SAR data contributed to the Lake volumetric variation computations. The methodological novelty of this study includes the integrated use of multi-sensor altimetry satellite with Sentinel-1 derived surface areas to address the satellite’s low temporal resolution and limited in situ monitoring, providing more accurate and continuous lake volume computation. ICESat-2 and Sentinel-3 derived water levels exhibited a strong correlation with field data, supported by good correlation coefficients (0.84 and 0.95), low mean absolute errors (0.24 and 0.10 m), and good Nash–Sutcliffe Efficiency statistics (0.61 and 0.91). However, the Jason-3 dataset demonstrated inferior performance with a lower correlation (R = 0.80), probably due to pulse contamination from its path near the bank. Sentinel-1 detected increased surface area and volumetric changes during the wet season (July–September) and lower variation in the dry season, depicting a strong correlation with water levels. It also successfully captured the impact of the 2022 flood. This study highlights the critical role of satellite technology in managing large lakes and reservoirs, particularly in regions with limited ground data. It also addresses its importance in bridging the critical data gap.