Estimating soil profile salinity under vegetation cover based on UAV multi-source remote sensing

Soil salinization is the most common land degradation problem in arid, semi-arid and coastal areas of China, which seriously affects local crop yield, economic development, and environmental sustainability. There are few studies on estimating soil salinity at different depths under vegetation cover. In this study, field soil control experiments were employed to collect multi-source remote sensing data under barley growth, and soil salt content (SSC) with various depths. Three types of feature variables were built based on images and were filtered by the boosting decision tree (BDT) method. Besides, four machine learning algorithms coupling with seven variable combination groups were used to comprehensively establish soil salinity estimation model. Finally, the performances of estimation model for different crop over ratios were evaluated. The results showed that the gaussian process regression (GPR) model based on the full variable group at the depths of 0 ~ 10 cm and 30 ~ 40 cm is more accurate than other models. The validation R ² is 0.774 and 0.705, and the RMSE is 0.185% and 0.31%;The random forest (RF) models based on spectral index and texture data at 10 ~ 20 cm and 20 ~ 30 cm depths are more accurate, with R ² of 0.666 and 0.714. SSC may be quantitatively inverted at various depths using the machine learning model based on multi-source remote sensing, which also serves as a guide for monitoring soil salinization.