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Remote Sensing Technology and Application  2021, Vol. 36 Issue (3): 564-570    DOI: 10.11873/j.issn.1004-0323.2021.3.0564
    
Multi-source Remote Sensing Data Cooperates to Retrieve Forest Surface Soil Moisture
Jingxia Sun(),Dongyou Zhang(),Yuchu Hou
Heilongjiang Province Key Laboratory of Geographical Environment Monitoring and Spatial Information Service in Cold Regions,Harbin Normal University,School of Geographical Sciences,Harbin Normal University,Harbin 150025,China
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Abstract  

Soil moisture is an important index in soil monitoring, which has an important impact on agricultural production, ecological environment and water resources management. With the remote sensing modeling and remote sensing inversion theory have gradually become important techniques and means to estimate soil indicators. Therefore, using the optical image data and radar image data, with Mohe City of Daxing'anling area as research area, to establish model of soil moisture inversion based on Landsat8 data and the model based on Landsat8 image data and high-resolution 3 remote sensing image data, the inversion results compared with the measured data analysis, and make evaluation on the model. The results showed that: (1) The surface temperature in the study area was inverted, and the TS-NDMI feature space was constructed by using surface temperature (Ts) and normalized difference humidity index NDMI. Combined with the measured data, it could be found that the inversion results of ts-NDMI feature space soil water inversion model were negatively correlated with the measured soil water content;(2) The soil moisture retrieval model based on GF-3 satellite data and Landsat 8 remote sensing data can get better retrieval results, and in areas with high vegetation coverage, the results obtained from this model are more accurate than those from a single optical data source, which provides a new way for the study of soil moisture in high vegetation coverage areas.

Key words:  Landsat 8      GF-3      Soil moisture      Collaborative inversion      Temperature Vegetation Drought Index     
Received:  27 June 2020      Published:  22 July 2021
ZTFLH:  S152.7  
Corresponding Authors:  Dongyou Zhang     E-mail:  sunjx0410@126.com;zhangdy@163.com
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Jingxia Sun
Dongyou Zhang
Yuchu Hou

Cite this article: 

Jingxia Sun,Dongyou Zhang,Yuchu Hou. Multi-source Remote Sensing Data Cooperates to Retrieve Forest Surface Soil Moisture. Remote Sensing Technology and Application, 2021, 36(3): 564-570.

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http://www.rsta.ac.cn/EN/10.11873/j.issn.1004-0323.2021.3.0564     OR     http://www.rsta.ac.cn/EN/Y2021/V36/I3/564

Fig.1  The dry and wet sides fit the equation diagram
经验参数草地冬小麦放牧地所有植被
A0.001 40.001 80.000 90.001 2
B0.084 00.138 00.032 00.091 0
Table 1  Empirical parameters of water cloud model
Fig.2  Comparison of optical data inversion results and measured values
Fig.3  Comparison of optical microwave remote sensing inversion results and measured values
模型相关系数(R2均方根误差(RMSE)
光学数据反演模型0.3520.0469
雷达数据协同光学数据反演模型0.6680.0250
Table 2  Comparison of inversion results of two models
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