Permafrost Soil Moisture Monitoring Using Multi-Temporal TerraSAR-X Data in Beiluhe of Northern Tibet, China
Global change has significant impact on permafrost region in the Tibet Plateau. Soil moisture (SM) of permafrost is one of the most important factors influencing the energy flux, ecosystem, and hydrologic process. The objectives of this paper are to retrieve the permafrost SM using time-series SAR i...
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ftmdpi:oai:mdpi.com:/2072-4292/10/10/1577/ 2023-08-20T04:09:10+02:00 Permafrost Soil Moisture Monitoring Using Multi-Temporal TerraSAR-X Data in Beiluhe of Northern Tibet, China Chao Wang Zhengjia Zhang Simonetta Paloscia Hong Zhang Fan Wu Qingbai Wu 2018-10-01 application/pdf https://doi.org/10.3390/rs10101577 EN eng Multidisciplinary Digital Publishing Institute Remote Sensing Image Processing https://dx.doi.org/10.3390/rs10101577 https://creativecommons.org/licenses/by/4.0/ Remote Sensing; Volume 10; Issue 10; Pages: 1577 permafrost soil moisture SAR multi-mode Tibet Text 2018 ftmdpi https://doi.org/10.3390/rs10101577 2023-07-31T21:45:31Z Global change has significant impact on permafrost region in the Tibet Plateau. Soil moisture (SM) of permafrost is one of the most important factors influencing the energy flux, ecosystem, and hydrologic process. The objectives of this paper are to retrieve the permafrost SM using time-series SAR images, without the need of auxiliary survey data, and reveal its variation patterns. After analyzing the characteristics of time-series radar backscattering coefficients of different landcover types, a two-component SM retrieval model is proposed. For the alpine meadow area, a linear retrieving model is proposed using the TerraSAR-X time-series images based on the assumption that the lowest backscattering coefficient is measured when the soil moisture is at its wilting point and the highest backscattering coefficient represents the water-saturated soil state. For the alpine desert area, the surface roughness contribution is eliminated using the dual SAR images acquired in the winter season with different incidence angles when retrieving soil moisture from the radar signal. Before the model implementation, landcover types are classified based on their backscattering features. 22 TerraSAR-X images are used to derive the soil moisture in Beiluhe, Northern Tibet with different incidence angles. The results obtained from the proposed method have been validated using in-situ soil moisture measurements, thus obtaining RMSE and Bias of 0.062 cm3/cm3 and 4.7%, respectively. The retrieved time-series SM maps of the study area point out the spatial and temporal SM variation patterns of various landcover types. Text permafrost MDPI Open Access Publishing Remote Sensing 10 10 1577 |
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permafrost soil moisture SAR multi-mode Tibet |
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permafrost soil moisture SAR multi-mode Tibet Chao Wang Zhengjia Zhang Simonetta Paloscia Hong Zhang Fan Wu Qingbai Wu Permafrost Soil Moisture Monitoring Using Multi-Temporal TerraSAR-X Data in Beiluhe of Northern Tibet, China |
topic_facet |
permafrost soil moisture SAR multi-mode Tibet |
description |
Global change has significant impact on permafrost region in the Tibet Plateau. Soil moisture (SM) of permafrost is one of the most important factors influencing the energy flux, ecosystem, and hydrologic process. The objectives of this paper are to retrieve the permafrost SM using time-series SAR images, without the need of auxiliary survey data, and reveal its variation patterns. After analyzing the characteristics of time-series radar backscattering coefficients of different landcover types, a two-component SM retrieval model is proposed. For the alpine meadow area, a linear retrieving model is proposed using the TerraSAR-X time-series images based on the assumption that the lowest backscattering coefficient is measured when the soil moisture is at its wilting point and the highest backscattering coefficient represents the water-saturated soil state. For the alpine desert area, the surface roughness contribution is eliminated using the dual SAR images acquired in the winter season with different incidence angles when retrieving soil moisture from the radar signal. Before the model implementation, landcover types are classified based on their backscattering features. 22 TerraSAR-X images are used to derive the soil moisture in Beiluhe, Northern Tibet with different incidence angles. The results obtained from the proposed method have been validated using in-situ soil moisture measurements, thus obtaining RMSE and Bias of 0.062 cm3/cm3 and 4.7%, respectively. The retrieved time-series SM maps of the study area point out the spatial and temporal SM variation patterns of various landcover types. |
format |
Text |
author |
Chao Wang Zhengjia Zhang Simonetta Paloscia Hong Zhang Fan Wu Qingbai Wu |
author_facet |
Chao Wang Zhengjia Zhang Simonetta Paloscia Hong Zhang Fan Wu Qingbai Wu |
author_sort |
Chao Wang |
title |
Permafrost Soil Moisture Monitoring Using Multi-Temporal TerraSAR-X Data in Beiluhe of Northern Tibet, China |
title_short |
Permafrost Soil Moisture Monitoring Using Multi-Temporal TerraSAR-X Data in Beiluhe of Northern Tibet, China |
title_full |
Permafrost Soil Moisture Monitoring Using Multi-Temporal TerraSAR-X Data in Beiluhe of Northern Tibet, China |
title_fullStr |
Permafrost Soil Moisture Monitoring Using Multi-Temporal TerraSAR-X Data in Beiluhe of Northern Tibet, China |
title_full_unstemmed |
Permafrost Soil Moisture Monitoring Using Multi-Temporal TerraSAR-X Data in Beiluhe of Northern Tibet, China |
title_sort |
permafrost soil moisture monitoring using multi-temporal terrasar-x data in beiluhe of northern tibet, china |
publisher |
Multidisciplinary Digital Publishing Institute |
publishDate |
2018 |
url |
https://doi.org/10.3390/rs10101577 |
genre |
permafrost |
genre_facet |
permafrost |
op_source |
Remote Sensing; Volume 10; Issue 10; Pages: 1577 |
op_relation |
Remote Sensing Image Processing https://dx.doi.org/10.3390/rs10101577 |
op_rights |
https://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.3390/rs10101577 |
container_title |
Remote Sensing |
container_volume |
10 |
container_issue |
10 |
container_start_page |
1577 |
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1774721959157825536 |