LAUSR

This study discusses measures for improving the precision of optical remote-sensing detection of soil salinity and the possibility of soil salinity detection at different depths of 0–10 cm, 10–30 cm and 30–50 cm using optical remote-sensing data, and analyzes the mechanism by which deep-layer soil salinity influences the soil spectrum. The findings show that there is a high and significant correlation between soil-spectral reflectance and soil salinity, and that the correlation between soil-spectral reflectance and soil salinity decreases gradually from the blue band to the shortwave infrared band of ETM + images. The partial least squares regression model is used to estimate soil salinity in the 0–10-cm surface-layer, confirming that the selected soil-salinity-detecting bands of Band 1 and Band 4, the established difference soil salinity index, the derivative of the normalized differential vegetation index, and the deep-layer soil moisture can improve the precision of remote-sensing detection of surface-layer soil salinity. The precise estimation of the 0–10-cm surface-layer soil salinity with variables features an R2 = 0.752, an RMSE = 26.84 g/Kg, and a p = 0.000. There is a strong mediating effect between deep-layer soil salinity, 0–10-cm surface-layer soil salinity, and soil spectral reflectance in the study area; namely, deep-layer soil salinity influences soil spectral reflectance by influencing surface-layer soil salinity. There is a significant and very strong power function relation between 0 and 10-cm surface-layer soil salinity and deep-layer soil salinity. Based on this relationship, this study estimates deep-layer soil salinity using optical remote-sensing images.