Unveiling soil salinity patterns in soda saline-alkali regions using Sentinel-2 and SDGSAT-1 thermal infrared data

Abstract

Soil salinization, a critical form of global soil degradation, threatens agricultural productivity and ecosystem functions. Accurate mapping of soil salinity is essential for sustainable land management and informed decision-making. However, conventional optical or radar satellite sensors are often limited in detecting key salinity spectral signatures due to their insufficient thermal infrared (TIR) coverage. TIR remote sensing offers unique advantages for soil salinity assessment, owing to its sensitivity to the emissivity of saline soils within the TIR spectrum, but its application remains underexplored. This study evaluated the suitability and robustness of SDGSAT-1 TIS data for large-scale soil salinity mapping in the Songnen Plain, China, one of the world's three largest soda saline-alkali soil regions. We compared the performance of soil salinity models integrating SDGSAT-1 TIS data with those using optical (Sentinel-2) and radar (Sentinel-1 and GF-3) data across several machine learning techniques. Our results demonstrated that incorporating SDGSAT-1 TIS data significantly enhanced soil salinity modeling accuracy, consistently outperforming models based solely on Sentinel-2 optical or Sentinel-1/GF-3 radar data. The combination of SDGSAT-1 TIS and Sentinel-2 data, optimized using the Gaussian Process Regression model, achieved the highest accuracy (R² = 0.75, RMSE = 0.65 dS/m). The resulting salinity maps revealed widespread soil salinization across the region, with the majority of the area exhibiting slight to moderate salinity levels, posing substantial challenges to plant growth and ecosystem resilience. This study offers a robust, data-driven validation of TIR's unique sensitivity to soil salinity, emphasizing its potential for integration into large-scale soil salinity mapping frameworks.