Estimating volumetric water salinity in a Tibetan endorheic lake using machine learning and remote sensing

Water salinity is a key characteristic of natural lakes, with its spatial and vertical variations altering water density and affecting aquatic organisms. Traditional lake water salinity monitoring, reliant on in-situ measurements, has limited the comprehensive exploration of both horizontal and vertical water salinity distribution, thereby hindering accurate characterization of volumetric water salinity and total salt content within the entire lake. To address this, our study introduces a novel framework for volumetric salinity estimation, using Pung Co, a deep endorheic lake on the Tibetan Plateau (TP), as a case study. First, we developed a model using machine learning algorithms, with remote sensing data and hydrological and topographical features, to estimate surface water salinity. Secondly, we analyzed field-surveyed vertical water salinity profiles to model the relationship between lake water depth and salinity, revealing the vertical water salinity variation characteristics. Finally, we constructed a gridded water column method to precisely estimate the lake’s total salt content. Results showed that the extreme gradient boosting model (R2 = 0.85, RMSE = 0.13 g/L, MAPE = 0.91%) effectively estimated surface water salinity. The modeled water salinity was characterized by significant horizontal and vertical variability. Horizontally, the water salinity was higher in the north and west and lower in the south and east, with a lake-wide average of 10.91 g/L. Vertically, the water salinity was strongly influenced by depth, exhibiting a sharp change near the thermocline before stabilizing. When surface water salinity was below 11.20 g/L, it increased with depth. When surface water salinity exceeded 11.20 g/L, it decreased with depth, with both converging toward a stable value of approximately 11.20 g/L. Using our gridded approach, the total dissolved salt content in Pung Co was estimated to be approximately 4.51 × 107 tons. This study establishes a quantitative framework that shifts salinity estimation from a two-dimensional surface assessment to a three-dimensional volumetric estimation, offering significant implications for understanding microbial diversity and the ecological effects of salinity stratification in high-altitude deep lakes.