Monitoring Surface Soil Moisture Content over the Vegetated Area by Integrating Optical and SAR Satellite Observations in the Permafrost Region of Tibetan Plateau

Surface soil moisture (SSM), the average water content of surface soil (up to 5 cm depth), plays a key role in the energy exchange within the ecosystem. We estimated SSM in areas with vegetation cover (grassland) by combining microwave and optical satellite measurements in the central Tibetan Platea...

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Published in:Remote Sensing
Main Authors: Chenyang Xu, John J. Qu, Xianjun Hao, Di Wu
Format: Text
Language:English
Published: Multidisciplinary Digital Publishing Institute 2020
Subjects:
Online Access:https://doi.org/10.3390/rs12010183
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spelling ftmdpi:oai:mdpi.com:/2072-4292/12/1/183/ 2023-08-20T04:09:14+02:00 Monitoring Surface Soil Moisture Content over the Vegetated Area by Integrating Optical and SAR Satellite Observations in the Permafrost Region of Tibetan Plateau Chenyang Xu John J. Qu Xianjun Hao Di Wu agris 2020-01-03 application/pdf https://doi.org/10.3390/rs12010183 EN eng Multidisciplinary Digital Publishing Institute Remote Sensing in Geology, Geomorphology and Hydrology https://dx.doi.org/10.3390/rs12010183 https://creativecommons.org/licenses/by/4.0/ Remote Sensing; Volume 12; Issue 1; Pages: 183 surface soil moisture optical microwave vegetation cover NDWI NDII Tibetan Plateau Text 2020 ftmdpi https://doi.org/10.3390/rs12010183 2023-07-31T22:57:55Z Surface soil moisture (SSM), the average water content of surface soil (up to 5 cm depth), plays a key role in the energy exchange within the ecosystem. We estimated SSM in areas with vegetation cover (grassland) by combining microwave and optical satellite measurements in the central Tibetan Plateau (TP) in 2015. We exploited TERRA moderate resolution imaging spectroradiometer (MODIS) and Sentinel-1A synthetic aperture radar (SAR) observations to estimate SSM through a simplified water-cloud model (sWCM). This model considers the impact of vegetation water content (VWC) to SSM retrieval by integrating the vegetation index (VI), the normalized difference water index (NDWI), or the normalized difference infrared index (NDII). Sentinel-1 SAR C-band backscattering coefficients, incidence angle, and NDWI/NDII were assimilated in the sWCM to monitor SSM. The soil moisture and temperature monitoring network on the central TP (CTP-SMTMN) measures SSM within the study area, and ground measurements were applied to train and validate the model. Via the proposed methods, we estimated the SSM in vegetated area with an R2 of 0.43 and a ubRMSE of 0.06 m3/m3 when integrating the NDWI and with an R2 of 0.45 and a ubRMSE of 0.06 m3/m3 when integrating the NDII. Text permafrost MDPI Open Access Publishing Remote Sensing 12 1 183
institution Open Polar
collection MDPI Open Access Publishing
op_collection_id ftmdpi
language English
topic surface soil moisture
optical
microwave
vegetation cover
NDWI
NDII
Tibetan Plateau
spellingShingle surface soil moisture
optical
microwave
vegetation cover
NDWI
NDII
Tibetan Plateau
Chenyang Xu
John J. Qu
Xianjun Hao
Di Wu
Monitoring Surface Soil Moisture Content over the Vegetated Area by Integrating Optical and SAR Satellite Observations in the Permafrost Region of Tibetan Plateau
topic_facet surface soil moisture
optical
microwave
vegetation cover
NDWI
NDII
Tibetan Plateau
description Surface soil moisture (SSM), the average water content of surface soil (up to 5 cm depth), plays a key role in the energy exchange within the ecosystem. We estimated SSM in areas with vegetation cover (grassland) by combining microwave and optical satellite measurements in the central Tibetan Plateau (TP) in 2015. We exploited TERRA moderate resolution imaging spectroradiometer (MODIS) and Sentinel-1A synthetic aperture radar (SAR) observations to estimate SSM through a simplified water-cloud model (sWCM). This model considers the impact of vegetation water content (VWC) to SSM retrieval by integrating the vegetation index (VI), the normalized difference water index (NDWI), or the normalized difference infrared index (NDII). Sentinel-1 SAR C-band backscattering coefficients, incidence angle, and NDWI/NDII were assimilated in the sWCM to monitor SSM. The soil moisture and temperature monitoring network on the central TP (CTP-SMTMN) measures SSM within the study area, and ground measurements were applied to train and validate the model. Via the proposed methods, we estimated the SSM in vegetated area with an R2 of 0.43 and a ubRMSE of 0.06 m3/m3 when integrating the NDWI and with an R2 of 0.45 and a ubRMSE of 0.06 m3/m3 when integrating the NDII.
format Text
author Chenyang Xu
John J. Qu
Xianjun Hao
Di Wu
author_facet Chenyang Xu
John J. Qu
Xianjun Hao
Di Wu
author_sort Chenyang Xu
title Monitoring Surface Soil Moisture Content over the Vegetated Area by Integrating Optical and SAR Satellite Observations in the Permafrost Region of Tibetan Plateau
title_short Monitoring Surface Soil Moisture Content over the Vegetated Area by Integrating Optical and SAR Satellite Observations in the Permafrost Region of Tibetan Plateau
title_full Monitoring Surface Soil Moisture Content over the Vegetated Area by Integrating Optical and SAR Satellite Observations in the Permafrost Region of Tibetan Plateau
title_fullStr Monitoring Surface Soil Moisture Content over the Vegetated Area by Integrating Optical and SAR Satellite Observations in the Permafrost Region of Tibetan Plateau
title_full_unstemmed Monitoring Surface Soil Moisture Content over the Vegetated Area by Integrating Optical and SAR Satellite Observations in the Permafrost Region of Tibetan Plateau
title_sort monitoring surface soil moisture content over the vegetated area by integrating optical and sar satellite observations in the permafrost region of tibetan plateau
publisher Multidisciplinary Digital Publishing Institute
publishDate 2020
url https://doi.org/10.3390/rs12010183
op_coverage agris
genre permafrost
genre_facet permafrost
op_source Remote Sensing; Volume 12; Issue 1; Pages: 183
op_relation Remote Sensing in Geology, Geomorphology and Hydrology
https://dx.doi.org/10.3390/rs12010183
op_rights https://creativecommons.org/licenses/by/4.0/
op_doi https://doi.org/10.3390/rs12010183
container_title Remote Sensing
container_volume 12
container_issue 1
container_start_page 183
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