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Remote Sensing Technology and Application  2022, Vol. 37 Issue (6): 1404-1413    DOI: 10.11873/j.issn.1004-0323.2022.6.1404
    
Sentinel-1 and Sentinel-2 Synergistic Retrieval of Surface Soil Moisture
Shaojie Du1,2(),Tianjie Zhao1(),Jiancheng Shi3,Chunfeng Ma4,Defu Zou4,Zhen Wang5,Panpan Yao1,Zhiqing Peng1,Jingyao Zheng6
1.State Key Laboratory of Remote Sensing Science,Institute of Remote Sensing and Digital Earth,Chinese Academy of Sciences,Beijing 100101,China
2.University of Chinese Academy of Sciences,Beijing 100049,China
3.National Space Science Center,Chinese Academy of Sciences,Beijing 100190,China
4.Northwest Institute of Eco-Environment and Resources,Chinese Academy of Sciences,Lanzhou 730000,China
5.National Basic Geographic Information Center,Beijing 100830,China
6.School of Hydrology and Water Resources,Hohai University,Nanjing 210024,China
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Abstract  

Soil moisture is a key parameter in the study of hydrological cycle, ecological environment, climate change, etc., The acquisition of high-resolution long time series soil moisture information is of great significance for agricultural management and crop growth monitoring, and remote sensing monitoring is also a difficult problem in research. Based on the Sentinel-1 radar data and Sentinel-2 optical data of the time series(2019—2020), this paper constructs a synergistic retrieval model of surface soil moisture, that is, a method for detecting changes in surface soil moisture under bare soil conditions, And the normalized vegetation index was used to correct the vegetation impact. The proposed method has achieved soil moisture mapping with a spatial resolution of 100 meters in the permafrost region (Wudaoliang) of the Qinghai-Tibet Plateau. The comparison and validation with the in-situ measured soil moisture observed show that the correlation coefficient between the soil moisture estimates and the ground measurements is 0.672≤R≤0.941, and the unbiased root mean square error (ubRMSE) is between 0.031 m3/m3 and 0.073 m3/m3. Soil moisture changes are closely related to regional precipitation events and characteristics, verifying that the change detection method proposed in this study has high applicability in the flat terrain and sparse vegetation areas on the Qinghai-Tibet Plateau.

Key words:  Soil moisture      Change detection      Permafrost      Qinghai-Tibet Plateau      Sentinel-1/2     
Received:  22 May 2022      Published:  15 February 2023
ZTFLH:  S152.5  
Corresponding Authors:  Tianjie Zhao     E-mail:  dushaojie19@mails.ucas.ac.cn;zhaotj@aircas.ac.cn
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Articles by authors
Shaojie Du
Tianjie Zhao
Jiancheng Shi
Chunfeng Ma
Defu Zou
Zhen Wang
Panpan Yao
Zhiqing Peng
Jingyao Zheng

Cite this article: 

Shaojie Du,Tianjie Zhao,Jiancheng Shi,Chunfeng Ma,Defu Zou,Zhen Wang,Panpan Yao,Zhiqing Peng,Jingyao Zheng. Sentinel-1 and Sentinel-2 Synergistic Retrieval of Surface Soil Moisture. Remote Sensing Technology and Application, 2022, 37(6): 1404-1413.

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http://www.rsta.ac.cn/EN/10.11873/j.issn.1004-0323.2022.6.1404     OR     http://www.rsta.ac.cn/EN/Y2022/V37/I6/1404

Fig.1  Overview of the study area
Fig.2  The influence of NDVI on the variation of backscattering coefficient
Fig.3  The trend of backscattering variation under different NDVI in the study area
Fig.4  Comparison of soil moisture inversion values and ground observations
Fig.5  Time series verification of soil moisture inversion values and ground observations at 10 sites
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R0.8060.6920.8490.7850.8110.7720.9150.9440.8560.876
RMSE/(m3/m3)0.0320.0490.0200.0820.0320.0780.1100.0820.0270.038
ubRMSE/(m3/m3)0.0310.0490.0170.0470.0290.0520.0720.0500.0270.036
Table 1  Validation indicators of soil moisture inversion values and ground observations at 10 sites
Fig.6  Comparison of average soil moisture in the study area
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