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

被引：0
作者：
Tao T. [1 ]
Li J. [1 ]
Zhu Y. [1 ,2 ]
Wang J. [1 ]
Chen H. [1 ]
Shi M. [1 ]
机构：
[1] College of Civil Engineering, Hefei University of Technology, Hefei
[2] Civil Engineering Disaster Prevention and Mitigation of Anhui Engineering Technology Research Center, Hefei
来源：
Cehui Xuebao/Acta Geodaetica et Cartographica Sinica | 2022年 / 51卷 / 09期
基金：
中国国家自然科学基金;
关键词：
CYGNSS; GNSS-R; neural network; SMAP; soil moisture; time series;
D O I：
10.11947/j.AGCS.2022.20210026
中图分类号：
学科分类号：
摘要：
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. © 2022 SinoMaps Press. All rights reserved.
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页码：1942 / 1950
页数：8
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