Forecasting climate change effects on Saline Lakes through advanced remote sensing and deep learning

Abstract

Given the vital role of saline lakes in supporting ecosystems in arid regions, this study analyzes their long-term changes by assessing their characteristics and spectral reflectance properties. Alongside evaluating the physical and chemical variations of these lakes, the research integrates climate change modeling to predict future shifts in their features and assess ecological impacts on surrounding environments. By employing Super-Resolution Generative Adversarial Network (SRGAN) and Multiresolution Segmentation (MRS), this approach enhances satellite image resolution and enables more precise differentiation of key lake components—such as salt deposits, salinity levels, and moisture fluctuations. The results show that increasing image resolution with SRGAN and using these images as input data for image classification models improves the identification of physical characteristics and the prediction of chemical properties of lakes with greater detail. The proposed method, based on Cellular Automata (CA)-Markov modeling of albedo and infrared wave reflectance, predicts a roughly 15 % increase in salinity of the studied lakes by 2050, driven by rising temperatures, intensified evaporation, and declining moisture levels. Finally, the results of climate change predictions based on the Long Short-Term Memory (LSTM) algorithm, with high accuracy (R² > 0.9), indicate increasing temperatures and evaporation in the coming years. Consequently, these rising temperatures will elevate salinity, drying, and albedo intensity in Chaka, Tuz, and Razzaza Lakes over the coming decades. This is supported by RCP8.5 scenarios, which project significant increases by 2100 that lead to greater evaporation and salinity. These changes have profound implications for surrounding ecosystems, particularly by affecting plant communities and accelerating desertification around these saline lakes.