Remote sensing inversion of vegetation-covered soil moisture is often affected by crop canopy, surface roughness, and other factors. In order to eliminate the scattering influence of vegetation effectively, this paper developed a modified vegetation backscattering model to retrieve vegetation-covered soil moisture based on multi-temporal RADARSAT-2 data and field measurements. This model combined the advantages of optical and radar methods by considering scattering contributions of underlying bare soil and vegetation canopy. Vegetation coverage was used to separate the scattering mechanism of the vegetation from bare soil component in a pixel. In addition, advanced integral equation method was presented to define the scattering of underlying bare soil. PROSAIL optical model was applied to calculate crown water content, which is an important variable associated with the scattering of vegetation canopy. Results demonstrated that the modified model on March 29, 2014 performed better in soil moisture retrieval than that at other growth stages with R2 of 0.806 and root-mean-square error of 0.043 m3·m−3, respectively. Soil moisture can be effectively retrieved by using the modified model in an agricultural region where the surface type is ranging from relatively sparse to full cover. Overall, the modified model provides an insight into extensive application of vegetation-covered soil moisture retrieval in agricultural regions.

In order to eliminate the influences of surface roughness and vegetation on radar signals in the vegetation-covered soil moisture estimation, the present paper proposes a combining method based on modified particle swarm optimization (MPSO) and back-propagation (BP) neural network algorithm. This method combines optical and radar data at the field scale and uses MPSO to optimize the weight of the neural network. An effective inertia weight is introduced in the MPSO and an implicit relationship between backscatter coefficient and soil moisture is established. Experimental results show that the combining method produces better accuracy than other inversion methods with R² of 72.2% and Root Mean Square Error (RMSE) of 0.033 cm³/cm³, respectively. Meanwhile, the estimated accuracy of surface soil moisture using radar and optical data simultaneously is much higher than that using only a single data source as input with R² of 0.827 and RMSE of 0.029 cm³/cm³. Therefore, the combining method can effectively improve the accuracy of soil moisture retrieval and provide support for large-scale agricultural monitoring.