测绘学报 ›› 2023, Vol. 52 ›› Issue (8): 1286-1297.doi: 10.11947/j.AGCS.2023.20220277

• 大地测量学与导航 • 上一篇    下一篇

粒子群优化卷积神经网络GNSS-IR土壤湿度反演方法

何佳星1, 郑南山1,2, 丁锐1,2, 张克非1,2, 陈天悦1   

  1. 1. 中国矿业大学环境与测绘学院, 江苏 徐州 221116;
    2. 中国矿业大学自然资源部国土环境与灾害监测重点实验室, 江苏 徐州 221116
  • 收稿日期:2022-04-26 修回日期:2023-01-31 发布日期:2023-09-07
  • 通讯作者: 郑南山 E-mail:znshcumt@163.com
  • 作者简介:何佳星(1999-),男,硕士生,研究方向为GNSS遥感。E-mail:HEJiaxing@cumt.edu.cn
  • 基金资助:
    国家自然科学基金(41974039);国家自然科学基金联合重点(U22A20569);自然资源部国土环境与灾害监测重点实验室开放基金(LEDM2021B11);国家重点研发计划课题(2019YFC1805003);江苏省研究生科研与实践创新计划(KYCX22_2594);中国矿业大学研究生创新计划(2022WLJCRCZL253)

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)

摘要: 全球卫星导航系统干涉测量法(GNSS-interferometric reflectometry,GNSS-IR)是一种新兴对地观测遥感技术,利用该方法能实现土壤湿度监测,具有很高的应用潜力。针对土壤湿度反演的建模问题,本文构建一种集成粒子群优化算法(particle swarm optimization,PSO)和卷积神经网络(convolutional neural network,CNN)的GNSS-IR土壤湿度反演模型,将多颗GPS卫星两个频点信噪比(signal-to-noise ratio,SNR)观测数据提取的特征参数作为模型输入,通过粒子群算法求解卷积神经网络超参数,对模型进行优化实现高精度反演。以P041站点为例详细描述了模型建立过程,本文方法的均方根误差为0.015 0,相较于基于单星线性、多星线性、未优化CNN和BP神经网络模型分别降低约60%、27%、31%和21%;并通过位于不同地理环境的COPR、P183、P341站点验证模型的可靠性和适用性。试验结果表明,融合多源观测数据建立PSO优化CNN的GNSS-IR土壤湿度反演模型,能有效反演土壤湿度,一定程度上抑制了不同下垫面环境的影响,具有较强的适用性。

关键词: 土壤湿度, GNSS-IR, SNR, 卷积神经网络, 粒子群

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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