阶段模型修正的星载GNSS-R土壤湿度反演方法

doi:10.11947/j.AGCS.2022.20210026

测绘学报 ›› 2022, Vol. 51 ›› Issue (9): 1942-1950.doi: 10.11947/j.AGCS.2022.20210026

阶段模型修正的星载GNSS-R土壤湿度反演方法

陶庭叶¹, 李江洋¹, 朱勇超^1,2, 汪俊涛¹, 陈皓¹, 时梦杰¹

1. 合肥工业大学土木与水利工程学院, 安徽合肥 230009;
2. 土木工程防灾减灾安徽省工程技术研究中心, 安徽合肥 230009

收稿日期:2021-01-19 修回日期:2021-12-21 发布日期:2022-09-29
通讯作者: 朱勇超 E-mail:yczhu@hfut.edu.cn
作者简介:陶庭叶(1980—),男,博士,副教授,研究方向为卫星导航定位算法及应用,激光雷达技术与应用。E-mail:taotingye@hfut.edu.cn
基金资助:
安徽省自然科学基金（2108085QD176）；国家自然科学基金（42104019）；武汉大学测绘遥感信息工程国家重点实验室开放基金（20P04）；安徽省自然科学基金（1808085MD105）

Spaceborne GNSS-R for retrieving soil moisture based on the correction of stage model

TAO Tingye¹, LI Jiangyang¹, ZHU Yongchao^1,2, WANG Juntao¹, CHEN Hao¹, SHI Mengjie¹

1. College of Civil Engineering, Hefei University of Technology, Hefei 230009, China;
2. Civil Engineering Disaster Prevention and Mitigation of Anhui Engineering Technology Research Center, Hefei 230009, China

Received:2021-01-19 Revised:2021-12-21 Published:2022-09-29
Supported by:
The National Natural Science Foundation of Anhui Province of China(No. 2108085QD176); The National Natural Science Foundation of China(No. 42104019); Open Fund of State Key Laboratory of Information Engineering of Surveying, Mapping and Remote Sensing, Wuhan University(No. 20P04);The National Natural Science Foundation of Anhui Province of China(No. 1808085MD105)

摘要/Abstract

摘要： 本文提出了一种基于CYGNSS数据的星载GNSS-R土壤湿度反演方法。首先，基于CYGNSS数据提取地表反射率参数，联合SMAP数据中提取的植被光学厚度、地表粗糙度和温度等辅助信息，初步构建了土壤湿度反演理论模型，并利用神经网络模型确定了土壤湿度反演的精细数学模型；然后，将该模型处理获得的土壤湿度以35%为分界点，利用本文提出的阶段函数模型提高反演精度，并使用2018年10月—2019年5月的CYGNSS数据，获得了全球范围内星载GNSS-R土壤湿度；最后，通过与SMAP提供的土壤湿度数据进行对比，评估了本文提出的星载GNSS-R土壤湿度反演方法的有效性，并对获取的星载GNSS-R土壤湿度进行了时间序列分析。结果表明，本文提出的土壤湿度反演方法的结果与SMAP土壤湿度具有良好的一致性，且随时间变化的趋势也相符合，为高精度土壤湿度反演提供了一种思路。

关键词: CYGNSS, GNSS-R, SMAP, 土壤湿度, 神经网络, 时间序列

Abstract: This paper proposes a spaceborne GNSS-R soil moisture retrieval method based on CYGNSS data. Firstly, the theoretical model of soil moisture retrieval is constructed by combining the surface reflectance parameters extracted from CYGNSS data and the auxiliary information of vegetation optical depth, surface roughness and temperature extracted from SMAP data. The fine mathematical model of soil moisture retrieval is determined by using the neural network model. Then, the soil moisture obtained by the proposed model is processed at an interval of 0.35, and the stage model proposed in this paper is used to improve the soil moisture retrieval accuracy, and the spaceborne GNSS-R soil moisture is obtained globally by using the CYGNSS data from October 2018 to May 2019. Finally, the effectiveness of the spaceborne GNSS-R soil moisture retrieval method proposed in this paper is evaluated through comparing with the soil moisture data provided by SMAP, and the time series of spaceborne GNSS-R soil moisture is analyzed. The results show that the soil moisture obtained by the method proposed in this paper is in good agreement with the soil moisture obtained by SMAP, and the trend of variation with time is also consistent with the actual situation, which provides a new idea for high-precision soil moisture retrieval.

Key words: CYGNSS, GNSS-R, SMAP, soil moisture, neural network, time series

中图分类号:

P237

陶庭叶, 李江洋, 朱勇超, 汪俊涛, 陈皓, 时梦杰. 阶段模型修正的星载GNSS-R土壤湿度反演方法[J]. 测绘学报, 2022, 51(9): 1942-1950.

TAO Tingye, LI Jiangyang, ZHU Yongchao, WANG Juntao, CHEN Hao, SHI Mengjie. Spaceborne GNSS-R for retrieving soil moisture based on the correction of stage model[J]. Acta Geodaetica et Cartographica Sinica, 2022, 51(9): 1942-1950.

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阶段模型修正的星载GNSS-R土壤湿度反演方法

Spaceborne GNSS-R for retrieving soil moisture based on the correction of stage model

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