Soil Moisture Retrievals Using Multi-Temporal Sentinel-1 Data over Nagqu Region of Tibetan Plateau
This paper presents an approach for retrieval of soil moisture in Nagqu region of Tibetan Plateau using VV-polarized Sentinel-1 SAR and MODIS optical data, by coupling the semi-empirical Oh-2004 model and the Water Cloud Model (WCM). The Oh model is first used to estimate the surface roughness param...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-05-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/13/10/1913 |
| _version_ | 1797534148294868992 |
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| author | Mengying Yang Hongquan Wang Cheng Tong Luyao Zhu Xiaodong Deng Jinsong Deng Ke Wang |
| author_facet | Mengying Yang Hongquan Wang Cheng Tong Luyao Zhu Xiaodong Deng Jinsong Deng Ke Wang |
| author_sort | Mengying Yang |
| collection | DOAJ |
| description | This paper presents an approach for retrieval of soil moisture in Nagqu region of Tibetan Plateau using VV-polarized Sentinel-1 SAR and MODIS optical data, by coupling the semi-empirical Oh-2004 model and the Water Cloud Model (WCM). The Oh model is first used to estimate the surface roughness parameter based on the hypothesis that the roughness is invariant among SAR acquisitions. Afterward, the vegetation water content (VWC) in the WCM is calculated from the daily MODIS NDVI data obtained by temporal interpolation. To improve the performance of the model, the parameters A, B, and <i>α</i> of the WCM are analyzed and optimized using randomly selected half of the sampled dataset. Then, the soil moisture is retrieved by minimizing a cost function between the simulated and measured backscattering coefficients. The comparison of the retrieved soil moisture with the ground measurements shows the determination coefficient R<sup>2</sup> and the Root Mean Square Error (RMSE) are 0.46 and 0.08 m<sup>3</sup>/m<sup>3</sup>, respectively. These results demonstrate the capability and reliability of Sentinel-1 SAR data for estimating the soil moisture over the Tibetan Plateau. |
| first_indexed | 2024-03-10T11:26:32Z |
| format | Article |
| id | doaj.art-19a8f09a71ed4d28a0c51d5f8d27bf97 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T11:26:32Z |
| publishDate | 2021-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-19a8f09a71ed4d28a0c51d5f8d27bf972023-11-21T19:36:55ZengMDPI AGRemote Sensing2072-42922021-05-011310191310.3390/rs13101913Soil Moisture Retrievals Using Multi-Temporal Sentinel-1 Data over Nagqu Region of Tibetan PlateauMengying Yang0Hongquan Wang1Cheng Tong2Luyao Zhu3Xiaodong Deng4Jinsong Deng5Ke Wang6College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310029, ChinaCollege of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310029, ChinaCollege of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310029, ChinaCollege of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310029, ChinaCollege of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310029, ChinaCollege of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310029, ChinaCollege of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310029, ChinaThis paper presents an approach for retrieval of soil moisture in Nagqu region of Tibetan Plateau using VV-polarized Sentinel-1 SAR and MODIS optical data, by coupling the semi-empirical Oh-2004 model and the Water Cloud Model (WCM). The Oh model is first used to estimate the surface roughness parameter based on the hypothesis that the roughness is invariant among SAR acquisitions. Afterward, the vegetation water content (VWC) in the WCM is calculated from the daily MODIS NDVI data obtained by temporal interpolation. To improve the performance of the model, the parameters A, B, and <i>α</i> of the WCM are analyzed and optimized using randomly selected half of the sampled dataset. Then, the soil moisture is retrieved by minimizing a cost function between the simulated and measured backscattering coefficients. The comparison of the retrieved soil moisture with the ground measurements shows the determination coefficient R<sup>2</sup> and the Root Mean Square Error (RMSE) are 0.46 and 0.08 m<sup>3</sup>/m<sup>3</sup>, respectively. These results demonstrate the capability and reliability of Sentinel-1 SAR data for estimating the soil moisture over the Tibetan Plateau.https://www.mdpi.com/2072-4292/13/10/1913Sentinel-1MODISsoil moisturewater cloud modelOh model |
| spellingShingle | Mengying Yang Hongquan Wang Cheng Tong Luyao Zhu Xiaodong Deng Jinsong Deng Ke Wang Soil Moisture Retrievals Using Multi-Temporal Sentinel-1 Data over Nagqu Region of Tibetan Plateau Remote Sensing Sentinel-1 MODIS soil moisture water cloud model Oh model |
| title | Soil Moisture Retrievals Using Multi-Temporal Sentinel-1 Data over Nagqu Region of Tibetan Plateau |
| title_full | Soil Moisture Retrievals Using Multi-Temporal Sentinel-1 Data over Nagqu Region of Tibetan Plateau |
| title_fullStr | Soil Moisture Retrievals Using Multi-Temporal Sentinel-1 Data over Nagqu Region of Tibetan Plateau |
| title_full_unstemmed | Soil Moisture Retrievals Using Multi-Temporal Sentinel-1 Data over Nagqu Region of Tibetan Plateau |
| title_short | Soil Moisture Retrievals Using Multi-Temporal Sentinel-1 Data over Nagqu Region of Tibetan Plateau |
| title_sort | soil moisture retrievals using multi temporal sentinel 1 data over nagqu region of tibetan plateau |
| topic | Sentinel-1 MODIS soil moisture water cloud model Oh model |
| url | https://www.mdpi.com/2072-4292/13/10/1913 |
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