Remote Sensing Inversion and Spatiotemporal Dynamics of Multi-Depth Soil Salinity in a Typical Arid Wetland: A Case Study of Ebinur Wetland Reserve, Xinjiang

Soil salinization in arid regions threatens ecological security and sustainable agriculture. The Ebinur Lake wetland in Xinjiang, situated in an arid climate and subject to human disturbance, suffers from severe salt accumulation and ecological degradation. To overcome the lack of soil depth information and limited spatiotemporal monitoring, this study integrates multi-year field samples and Landsat imagery (1996–2024) to construct a six-layer (0–100 cm) soil salinity inversion framework. Multi-source spectral features were optimized using the Random Frog Leaping Algorithm (RFLA), and models based on Convolutional Neural Network (CNN), Long Short-Term Memory Network (LSTM), and Random Forest (RF) were compared. The results (1) demonstrated that RFLA effectively identified high-contribution features, enhancing efficiency and reducing redundancy; (2) showed that CNN outperformed LSTM and RF in capturing spatial salinity, with R2 values of 0.75, 0.59, 0.63, 0.69, 0.57, and 0.56 for the six layers; and (3) revealed salinity migration: surface enrichment, mid-layer buffering, and deep-layer accumulation. In oases, surface salinity declined while deep layers accumulated; in deserts, surface salinity increased. The proposed framework enhances the accuracy of multi-depth salinity retrieval and provides technical support for salinization monitoring, irrigation management, ecological assessment, and control of land degradation in arid regions.