Acta Geodaetica et Cartographica Sinica ›› 2023, Vol. 52 ›› Issue (8): 1286-1297.doi: 10.11947/j.AGCS.2023.20220277

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A GNSS-IR soil moisture inversion method based on the convolutional neural network optimized by particle swarm optimization

HE Jiaxing1, ZHENG Nanshan1,2, DING Rui1,2, ZHANG Kefei1,2, CHEN Tianyue1   

  1. 1. School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China;
    2. MNR Key Laboratory of Land Environment and Disaster Monitoring, China University of Mining and Technology, Xuzhou 221116, China
  • Received:2022-04-26 Revised:2023-01-31 Published:2023-09-07
  • Supported by:
    The National Natural Science Foundation of China (No. 41974039); The Joint Funds of the National Natural Science Foundation of China (No. U22A20569); The Open Research Fund of Key Laboratory of Land Environment and Disaster Monitoring, Ministry of Natural Resources, China University of [JP5]Mining and Technology (No. LEDM2021B11); The National Key Research and Development Program (No. 2019YFC1805003); The Postgraduate Research & Practice Innovation Program of Jiangsu Province (No. KYCX22＿2594); The Graduate Innovation Program of China University of Mining and Technology (No. 2022WLJCRCZL253)

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Abstract: Global navigation satellite system interferometric reflectometry (GNSS-IR) is an emerging remote sensing technique for earth observation, which can be applied to monitor soil moisture and has a prosperous application prospect. Aiming at the modeling problem of soil moisture inversion, we developed a GNSS-IR soil moisture inversion model integrating particle swarm optimization (PSO) and convolutional neural network (CNN). The metrics extracted from two frequency signal-to-noise ratio (SNR) observation data of GPS satellites were used as the input, and particle swarm optimization algorithm was used to optimize the hyperparameters of the convolutional neural network. Detailed modeling was carried out with the site of P041. Its root mean square error is 0.015 0, which is 60%, 27%, 31% and 21% lower than that based on single satellite linear, multi-satellite linear, conventional CNN and back propagation (BP) models; The applicability of the model was verified by COPR, P183 and P341 sites. The results indicate that the integrated GNSS-IR soil moisture inversion model based on PSO-CNN can effectively restrain the influence of the land surface environmental factors within consideration of multi-source observation data.

Key words: soil moisture, GNSS-IR, SNR, convolutional neural network, particle swarm optimization

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