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Remote Sensing for Land & Resources    2021, Vol. 33 Issue (2) : 20-26     DOI: 10.6046/gtzyyg.2020244
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Downscaling FY-3B soil moisture based on apparent thermal inertia and temperature vegetation index
SONG Chengyun1(), HU Guangcheng2, WANG Yanli1, TANG Chao1
1. School of Geomatics, Anhui University of Science and Technology, Huainan 232001, China
2. State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
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Abstract  

In order to further study the method of obtaining high-resolution soil moisture by downscaling FY-3B soil moisture and make it more suitable for agricultural and hydrological simulation, the authors constructed a comprehensive ATI and TVI by using MODIS data in Naqu area. Combined with low resolution FY-3B soil moisture products, the coefficients of soil moisture inversion model under high resolution were obtained by using soil moisture downscaling method, and the high-resolution soil moisture was obtained. Compared with the ground observation data, the R2 of the downscaling soil moisture and the measured data is above 0.4, and the RMSE is between 0.055 and 0.103 cm3/cm3, indicating that the downscaling soil moisture can better reflect the spatial distribution and change of soil moisture.

Keywords downscaling      FY-3B soil moisture      apparent thermal inertia      temperature vegetation index     
ZTFLH:  TP79  
Issue Date: 21 July 2021
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Chengyun SONG
Guangcheng HU
Yanli WANG
Chao TANG
Cite this article:   
Chengyun SONG,Guangcheng HU,Yanli WANG, et al. Downscaling FY-3B soil moisture based on apparent thermal inertia and temperature vegetation index[J]. Remote Sensing for Land & Resources, 2021, 33(2): 20-26.
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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2020244     OR     https://www.gtzyyg.com/EN/Y2021/V33/I2/20
Fig.1  Schematic diagram of elevation and ground observation stations in the study area
Fig.2  The technical route
站点 日期 R a b c d
M08 7月 — — — — —
10月 0.763 0.120 -0.310 -0.005 -0.827
M11 7月 0.878 0.049 -0.040 -0.001 0.770
10月 0.868 0.156 -0.407 -0.007 -1.334
M12 7月 0.705 0.007 0.088 -0.002 0.193
10月 0.860 0.061 0.187 -0.004 0.321
M14 7月 — — — — —
10月 0.779 0.291 -0.837 -0.015 -3.213
M16 7月 — — — — —
10月 0.874 0.163 -0.278 -0.007 -1.291
M17 7月 — — — — —
10月 0.863 0.179 -0.535 -0.006 -1.051
M18 7月 0.861 0.023 0.011 -0.004 1.295
10月 0.755 0.114 -0.269 -0.004 -0.575
M19 7月 0.706 0.023 -0.055 -0.001 0.521
10月 0.907 0.168 -0.276 -0.005 -0.702
M21 7月 — — — — —
10月 0.913 0.168 -0.276 -0.005 -0.702
Tab.1  Model (equation (7)) regression analysis correlation coefficient R and model coefficient
Fig.3  Time series of correlation coefficient
Fig.4  FY-3B soil moisture and its spatial distribution after downscaling
Fig.5  Scatter plots between downscaled soil moisture and ground observations
Fig.6  Time series of FY-3B soil moisture and ground observations
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