Optimization of Multi-Source Remote Sensing Soil Salinity Estimation Based on Different Salinization Degrees

The timely and accurate monitoring of regional soil salinity is crucial for the sustainable development of land and the stability of the ecological environment in arid and semi-arid regions. However, due to the spatiotemporal heterogeneity of soil properties and environmental conditions, improving the accuracy of soil salinization monitoring remains challenging. This study aimed to explore whether partitioned modeling based on salinization degrees during both the bare soil and vegetation cover periods can enhance the accuracy of regional soil salinity prediction. Specifically, this study integrated in situ hyperspectral data and satellite multispectral data using spectral response functions. Subsequently, machine learning methods such as random forest (RF), extreme gradient boosting (XGBoost), support vector machine (SVM), and multiple linear regression (MLR) were employed, in combination with sensitive spectral indices, to develop a multi-source remote sensing soil salinity estimation model optimized for different salinization degrees (mild or lower salinization vs. moderate or higher salinization). The performance of this partitioned modeling approach was then compared with an overall modeling approach that does not distinguish between salinization degrees to determine the optimal modeling strategy. The results highlight the effectiveness of considering regional soil salinization degrees in enhancing the sensitivity of spectral indices to soil salinity and improving modeling accuracy. Classifying salinization degrees helps identify spectral variable combinations that are more sensitive to the construction of soil salinity content (SSC) models, positively impacting soil salinity estimation. The partitioned modeling strategy outperformed the overall modeling strategy in both accuracy and stability, with R2 values reaching 0.84 and 0.80 and corresponding RMSE values of 0.1646% and 0.1710% during the bare soil and vegetation cover periods, respectively. This study proposes an optimized modeling strategy based on regional salinization degrees, providing scientific evidence and technical support for the precise assessment and effective management of soil salinization.