Soil-Moisture Estimation Based on Multiple-Source Remote-Sensing Images

Soil moisture plays a significant role in the global hydrological cycle, which is an important component of soil parameterization. Remote sensing is one of the most important methods used to estimate soil moisture. In this study, we developed a new nonlinear Erf-BP neural network method to establish a soil-moisture-content-estimation model with integrated multiple-resource remote-sensing data from high-resolution, hyperspectral and microwave sensors. Next, we compared the result with the single-resource remote-sensing data for SMC (soil-moisture content) estimation models by using the linear-fitting method. The results showed that the soil-moisture estimation model offers better accuracy by using multiple-resource remote-sensing data. Furthermore, the SMC predicted the results by using the new Erf-BP neural network with multiple-resource remote-sensing data and a good overall correlation coefficient of 0.6838. Compared with the linear model's estimation results, the accuracy of the SMC estimation using the Erf-BP method was increased, and the RMSE decreased from 0.017 g/g to 0.0146 g/g, a decrease of 16.44%. These results also indicate that the improved algorithm of the Erf-BP artificial neural network has better fitting results and precision. This research provides a reference for multiple-resource remote-sensing data for soil-moisture estimation.

Automated summary

Soil moisture is an important component of soil parameterization and plays a significant role in the global hydrological cycle. Remote sensing is a crucial method for estimating soil moisture, and this study developed a new nonlinear Erf-BP neural network method using integrated multiple-resource remote sensing data to establish a soil moisture estimation model. The results showed that using multiple-resource remote sensing data provided better accuracy for the soil moisture estimation model. Moreover, the SMC predicted results using the new Erf-BP neural network with multiple-resource remote sensing data had a good overall correlation coefficient of 0.6838 and improved accuracy compared to the linear model.