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Soil Moisture Retrival Based on Sentinel-1 Imagery under Sparse Vegetation Coverage

Soil moisture is an important aspect of heat transfer process and energy exchange between land-atmosphere systems, and it is a key link to the surface and groundwater circulation and land carbon cycles. In this study, according to the characteristics of the study area, an advanced integral equation...

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Autores principales: Huang, Shuai, Ding, Jianli, Zou, Jie, Liu, Bohua, Zhang, Junyong, Chen, Wenqian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6387433/
https://www.ncbi.nlm.nih.gov/pubmed/30704120
http://dx.doi.org/10.3390/s19030589
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author Huang, Shuai
Ding, Jianli
Zou, Jie
Liu, Bohua
Zhang, Junyong
Chen, Wenqian
author_facet Huang, Shuai
Ding, Jianli
Zou, Jie
Liu, Bohua
Zhang, Junyong
Chen, Wenqian
author_sort Huang, Shuai
collection PubMed
description Soil moisture is an important aspect of heat transfer process and energy exchange between land-atmosphere systems, and it is a key link to the surface and groundwater circulation and land carbon cycles. In this study, according to the characteristics of the study area, an advanced integral equation model was used for numerical simulation analysis to establish a database of surface microwave scattering characteristics under sparse vegetation cover. Thus, a soil moisture retrieval model suitable for arid area was constructed. The results were as follows: (1) The response of the backscattering coefficient to soil moisture and associated surface roughness is significantly and logarithmically correlated under different incidence angles and polarization modes, and, a database of microwave scattering characteristics of arid soil surface under sparse vegetation cover was established. (2) According to the Sentinel-1 radar system parameters, a model for retrieving spatial distribution information of soil moisture was constructed; the soil moisture content information was extracted, and the results were consistent with the spatial distribution characteristics of soil moisture in the same period in the research area. (3) For the 0–10 cm surface soil moisture, the correlation coefficient between the simulated value and the measured value reached 0.8488, which means that the developed retrieval model has applicability to derive surface soil moisture in the oasis region of arid regions. This study can provide method for real-time and large-scale detection of soil moisture content in arid areas.
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spelling pubmed-63874332019-02-27 Soil Moisture Retrival Based on Sentinel-1 Imagery under Sparse Vegetation Coverage Huang, Shuai Ding, Jianli Zou, Jie Liu, Bohua Zhang, Junyong Chen, Wenqian Sensors (Basel) Article Soil moisture is an important aspect of heat transfer process and energy exchange between land-atmosphere systems, and it is a key link to the surface and groundwater circulation and land carbon cycles. In this study, according to the characteristics of the study area, an advanced integral equation model was used for numerical simulation analysis to establish a database of surface microwave scattering characteristics under sparse vegetation cover. Thus, a soil moisture retrieval model suitable for arid area was constructed. The results were as follows: (1) The response of the backscattering coefficient to soil moisture and associated surface roughness is significantly and logarithmically correlated under different incidence angles and polarization modes, and, a database of microwave scattering characteristics of arid soil surface under sparse vegetation cover was established. (2) According to the Sentinel-1 radar system parameters, a model for retrieving spatial distribution information of soil moisture was constructed; the soil moisture content information was extracted, and the results were consistent with the spatial distribution characteristics of soil moisture in the same period in the research area. (3) For the 0–10 cm surface soil moisture, the correlation coefficient between the simulated value and the measured value reached 0.8488, which means that the developed retrieval model has applicability to derive surface soil moisture in the oasis region of arid regions. This study can provide method for real-time and large-scale detection of soil moisture content in arid areas. MDPI 2019-01-30 /pmc/articles/PMC6387433/ /pubmed/30704120 http://dx.doi.org/10.3390/s19030589 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Huang, Shuai
Ding, Jianli
Zou, Jie
Liu, Bohua
Zhang, Junyong
Chen, Wenqian
Soil Moisture Retrival Based on Sentinel-1 Imagery under Sparse Vegetation Coverage
title Soil Moisture Retrival Based on Sentinel-1 Imagery under Sparse Vegetation Coverage
title_full Soil Moisture Retrival Based on Sentinel-1 Imagery under Sparse Vegetation Coverage
title_fullStr Soil Moisture Retrival Based on Sentinel-1 Imagery under Sparse Vegetation Coverage
title_full_unstemmed Soil Moisture Retrival Based on Sentinel-1 Imagery under Sparse Vegetation Coverage
title_short Soil Moisture Retrival Based on Sentinel-1 Imagery under Sparse Vegetation Coverage
title_sort soil moisture retrival based on sentinel-1 imagery under sparse vegetation coverage
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6387433/
https://www.ncbi.nlm.nih.gov/pubmed/30704120
http://dx.doi.org/10.3390/s19030589
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