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- W4313244493 endingPage "139" @default.
- W4313244493 startingPage "139" @default.
- W4313244493 abstract "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." @default.
- W4313244493 created "2023-01-06" @default.
- W4313244493 creator A5016270369 @default.
- W4313244493 creator A5040669657 @default.
- W4313244493 creator A5061747594 @default.
- W4313244493 creator A5069592686 @default.
- W4313244493 date "2022-12-26" @default.
- W4313244493 modified "2023-10-14" @default.
- W4313244493 title "Soil-Moisture Estimation Based on Multiple-Source Remote-Sensing Images" @default.
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