Precise Monitoring of Soil Salinity in China's Yellow River Delta Using UAV-Borne Multispectral Imagery and a Soil Salinity Retrieval Index

Monitoring salinity information of salinized soil efficiently and precisely using the unmanned aerial vehicle (UAV) is critical for the rational use and sustainable development of arable land resources. The sensitive parameter and a precise retrieval method of soil salinity, however, remain unknown. This study strived to explore the sensitive parameter and construct an optimal method for retrieving soil salinity. The UAV-borne multispectral image in China's Yellow River Delta was acquired to extract band reflectance, compute vegetation indexes and soil salinity indexes. Soil samples collected from 120 different study sites were used for laboratory salt content measurements. Grey correlation analysis and Pearson correlation coefficient methods were employed to screen sensitive band reflectance and indexes. A new soil salinity retrieval index (SSRI) was then proposed based on the screened sensitive reflectance. The Partial Least Squares Regression (PLSR), Multivariable Linear Regression (MLR), Back Propagation Neural Network (BPNN), Support Vector Machine (SVM), and Random Forest (RF) methods were employed to construct retrieval models based on the sensitive indexes. The results found that green, red, and near-infrared (NIR) bands were sensitive to soil salinity, which can be used to build SSRI. The SSRI-based RF method was the optimal method for accurately retrieving the soil salinity. Its modeling determination coefficient (R-2) and Root Mean Square Error (RMSE) were 0.724 and 1.764, respectively; and the validation R-2, RMSE, and Residual Predictive Deviation (RPD) were 0.745, 1.879, and 2.211.

Automated summary

Monitoring soil salinity using UAV can significantly contribute to the rational use and sustainable development of arable land resources. This study identified the sensitive bands and developed an optimal method for retrieving soil salinity using UAV-borne multispectral images and laboratory measurements. The results showed that green, red, and NIR bands were sensitive to soil salinity, and a new soil salinity retrieval index was proposed based on these bands. The RF method using the proposed index achieved the highest accuracy for soil salinity retrieval. The findings of this study are valuable for soil salinity management and sustainable agricultural development.