WCSE

GNSS-R Soil Moisture Retrieval Model Based on Elman Neural Network

Abstract— Global Navigation Satellite System-Reflectometry (GNSS-R) in soil moisture retrieval, the traditional method mainly includes linear regression and exponential regression methods. To address the defects of conventional methods such as poor prediction accuracy and sizeable computational effort, the Elman neural network with dynamic learning features is introduced. An Elman neural network-based soil moisture retrieval method is proposed to establish a multi-parameter retrieval model. Finally, the model is trained to validate the feasibility of this model. The results indicate that the soil moisture values estimated by the GNSS-R soil moisture retrieval method based on the Elman neural network have minor errors with the actual measured soil moisture values. Based on this model, the coefficient of determination (R2) is 0.8988, and the Root Mean Square Error (RMSE) of soil moisture is 0.0207. When compared to the traditional linear regression model, the soil moisture values predicted by this method are more accurate and closer to the measured soil moisture values, demonstrating the method's validity and reliability.

Index Terms— Global Navigation Satellite System-Reflectometry (GNSS-R), Elman neural network, soil moisture retrieval.

Yuanhua Liu
School of Communications and Information Engineering, Xi'an University of Posts & Telecommunications
Wenwen Chen
School of Communications and Information Engineering, Xi'an University of Posts & Telecommunications
Xinliang Niu
Xi'an Branch of China Academy of Space Technology, China Academy of Space Technology

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Cite: Yuanhua Liu, Wenwen Chen, Xinliang Niu, " GNSS-R Soil Moisture Retrieval Model Based on Elman Neural Network, " WCSE 2022 Spring Event: 2022 9th International Conference on Industrial Engineering and Applications, pp. 724-731, Sanya, China, April 15-18, 2022.