The rapid and accurate assessment of regional soil salinity is crucial for effective salinization management. This study proposes an enhanced remote sensing inversion method by integrating both driving and response environmental variables to address lag effects and incomplete factor consideration in existing models. The Yellow River Delta, a coastal saline–alkaline region, was selected as the study area, where soil salinity-sensitive spectral parameters were derived from Sentinel-2 MSI imagery. Six environmental variables, including precipitation, distance from the sea, and soil moisture, were analyzed. Four scenarios were constructed: (1) using only spectral parameters; (2) spectral parameters with driving variables; (3) spectral parameters with response variables; and (4) combining both types. Four modeling methods were employed to assess inversion accuracy. The results show that incorporating either driving or response variables improved accuracy, with validation R2 increasing by up to 0.149 and RMSE decreasing by up to 0.097 when both were used. The suitable model, integrating soil moisture, distance from the sea, and chlorophyll content, achieved a calibration R2 of 0.813 and validation R2 of 0.722. These findings demonstrate that combining both driving and response variables enhances model performance and provides valuable insights for soil salinization management.
Zhou et al. (Tue,) studied this question.