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All-Sky Soil Moisture Estimation over Agriculture Areas from the Full Polarimetric SAR GF-3 Data

Dayou Luo / Xingping Wen / Junlong Xu

This study aims to estimate the soil moisture (SM) in all-sky agriculture areas using fully polarimetric synthetic aperture radar (SAR) Gaofen-3 (GF-3) data. The radar vegetation index (RVI) is obtained using the radar SAR data, which overcomes the difficulty that the optical data cannot construct the vegetation index in cloud-covered areas. The RVI is introduced into the water cloud model (WCM) to remove the contribution of vegetation to the total radar backscatter $σ^{0}$ and obtain the soil backscattering coefficients $σ_{soil}^{0}$ with HH and VV polarization. Subsequently, $σ_{soil}^{0}$ and radar frequency data are introduced into the Chen model, and a semi-empirical model of SM estimation is established. The main findings of this study are as follows: (1) Compared with the $σ^{0}$ , the $σ_{soil}^{0}$ obtained by the WCM has a stronger correlation with the SM. (2) In the cloud covered area, the accuracy of the estimated SM by synergistically using the WCM and the Chen model is ideal. An RMSE of 0.05 and a correlation coefficient (r) of 0.69 are achieved. In this study, the SM estimation method is not affected by clouds, and it shows many advantages for sustainable development, monitoring soil drought degree, and other related research.

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All-Sky Soil Moisture Estimation over Agriculture Areas from the Full Polarimetric SAR GF-3 Data

Dayou Luo / Xingping Wen / Junlong Xu

This study aims to estimate the soil moisture (SM) in all-sky agriculture areas using fully polarimetric synthetic aperture radar (SAR) Gaofen-3 (GF-3) data. The radar vegetation index (RVI) is obtained using the radar SAR data, which overcomes the difficulty that the optical data cannot construct the vegetation index in cloud-covered areas. The RVI is introduced into the water cloud model (WCM) to remove the contribution of vegetation to the total radar backscatter $σ^{0}$ and obtain the soil backscattering coefficients $σ_{soil}^{0}$ with HH and VV polarization. Subsequently, $σ_{soil}^{0}$ and radar frequency data are introduced into the Chen model, and a semi-empirical model of SM estimation is established. The main findings of this study are as follows: (1) Compared with the $σ^{0}$ , the $σ_{soil}^{0}$ obtained by the WCM has a stronger correlation with the SM. (2) In the cloud covered area, the accuracy of the estimated SM by synergistically using the WCM and the Chen model is ideal. An RMSE of 0.05 and a correlation coefficient (r) of 0.69 are achieved. In this study, the SM estimation method is not affected by clouds, and it shows many advantages for sustainable development, monitoring soil drought degree, and other related research.

All-Sky Soil Moisture Estimation over Agriculture Areas from the Full Polarimetric SAR GF-3 Data

Dayou Luo / Xingping Wen / Junlong Xu

This study aims to estimate the soil moisture (SM) in all-sky agriculture areas using fully polarimetric synthetic aperture radar (SAR) Gaofen-3 (GF-3) data. The radar vegetation index (RVI) is obtained using the radar SAR data, which overcomes the difficulty that the optical data cannot construct the vegetation index in cloud-covered areas. The RVI is introduced into the water cloud model (WCM) to remove the contribution of vegetation to the total radar backscatter $σ^{0}$ and obtain the soil backscattering coefficients $σ_{soil}^{0}$ with HH and VV polarization. Subsequently, $σ_{soil}^{0}$ and radar frequency data are introduced into the Chen model, and a semi-empirical model of SM estimation is established. The main findings of this study are as follows: (1) Compared with the $σ^{0}$ , the $σ_{soil}^{0}$ obtained by the WCM has a stronger correlation with the SM. (2) In the cloud covered area, the accuracy of the estimated SM by synergistically using the WCM and the Chen model is ideal. An RMSE of 0.05 and a correlation coefficient (r) of 0.69 are achieved. In this study, the SM estimation method is not affected by clouds, and it shows many advantages for sustainable development, monitoring soil drought degree, and other related research.

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Title:

All-Sky Soil Moisture Estimation over Agriculture Areas from the Full Polarimetric SAR GF-3 Data

Contributors:

Dayou Luo (author) / Xingping Wen (author) / Junlong Xu (author)

Published in:

Sustainability, Vol 14, Iss 17, p 10866 (2022)

Publication date:

2022

ISSN:

DOI:

https://doi.org/10.3390/su141710866

Type of media:

Article (Journal)

Type of material:

Electronic Resource

Language:

Unknown

Keywords:

synthetic aperture radar (SAR) , full polarimetric SAR , Gaofen-3 (GF-3) , soil moisture (SM) , water cloud model (WCM) , Chen model , Environmental effects of industries and plants , TD194-195 , Renewable energy sources , TJ807-830 , Environmental sciences , GE1-350

Metadata by DOAJ is licensed under CC BY-SA 1.0

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