Cooperative Inversion of Winter Wheat Covered Surface Soil Moisture by Multi-Source Remote Sensing

Abstract

Soil moisture is an important parameter affecting environmental processes such as hydrology, ecology and climate. Microwave remote sensing is an effective means of surface soil moisture measurement. Aiming at the influence of vegetation cover in the process of surface soil moisture inversion of winter wheat farmland by microwave remote sensing, a cooperative inversion method using multi-source remote sensing data is proposed in this paper. Thirty-three feature parameters are extracted from Radarsat-2 full polarization SAR data and Sentinel-2 optical data, and ten parameters with high correlation with soil moisture are selected to participate in soil moisture inversion by Pearson correlation analysis. Combined with the ground sampling data, four machine learning models, including Random Forest, Generalized Regression Neural Network, Radial Basis Function and Extreme Learning Machine, are used for quantitative inversion of soil moisture to reduce the impact of vegetation and improve the inversion accuracy. The experimental results show that the Random Forest model is the optimal. The average of determination coefficient is 0.63959, and the average of root mean square error is 0.0317 cm3 / cm3, which provides a reference for the inversion of soil moisture in farmland using multi-source remote sensing data.