Retrieval for soil moisture in farmland using multi-source remote sensing data and feature selection with GA-BP neural network

Measurements of soil moisture have great significance for agricultural production and environmental protection. A variety of technologies have been introduced into the current monitoring of surface soil moisture, particularly widely-used optical remote sensing and synthetic aperture radar (SAR) microwave remote sensing. In this study, retrieval for surface soil moisture in farmland was proposed using multi-source remote sensing data and feature dimension reduction with GA-BP neural networks. Sentinel-1 microwave and Sentinel-2 optical remote sensing data were used with a high spatial and temporal resolution. Three field surveys and sampling were carried out simultaneously during the transit time of the Sentinel-1 satellite. A total of 20 sampling points were set on the ground in the study area to collect soil moisture in the longitude and latitude coordinates. Firstly, Sentinel-1 and Sentinel-2 data were preprocessed to extract 21 feature parameters, including 9 backscatter coefficient, 5 polarization characteristic parameters, surface roughness, and 6 vegetation indices. Then, the differential evolution feature selection (DEFS) was utilized to obtain an optimal feature subset with 10 parameters, including the incident angle, ?0 VV, ?0 VH, ?0 VH/?0 VV, Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Water Band Index (WBI), and Fusion Vegetation Index (FVI). Principal component analysis (PCA) was used to further reduce the dimension of the optimal feature subset. The feature subset was reduced to eight dimensions, where an eight-dimensional feature matrix was obtained. After that, back propagation (BP) neural network was established to describe the nonlinear relationship between characteristic parameters and surface soil moisture, whereas, genetic algorithm (GA) was used to optimize the node weights and accelerated the learning speed of the BP neural network. The feature matrix after dimension reduction and some measured data of soil moisture were input into the GA-BP network for training, where the distribution map of soil moisture was obtained in the study area. Finally, taking a winter wheat field in Henan Province as the study area, three comparative experimental schemes were set to verify the accuracy of inversion using the measured data. The experimental schemes with DEFS and PCA presented the highest accuracy, where the coefficient of determination was 0.789 3, and the root mean square error was 0.028 7 cm3/cm3, compared with the genetic BP neural network, indicating the coefficient of determination increased by 0.215 7, and the root mean square error was reduced by 0.029 5 cm3/cm3. Meanwhile, the frequency distribution of soil moisture inversion was basically consistent with the measured soil moisture of sampling points. The experimental results demonstrated that the GA-BP network combining with DEFS and PCA can eliminate the redundant characteristic parameters for high inversion accuracy, and a high-resolution distribution map of surface soil moisture with a large area. The finding can offer some advantages and application potentials to the surface soil moisture retrieval in farmland.