Retrieving Surface Soil Moisture over Wheat-Covered Areas Using Data from Sentinel-1 and Sentinel-2
Surface soil moisture (SSM) is a major factor that affects crop growth. Combined microwave and optical data have been widely used to improve the accuracy of SSM retrievals. However, the influence of vegetation indices derived from the red-edge spectral bands of multi-spectral optical data on retriev...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-07-01
|
| Series: | Water |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-4441/13/14/1981 |
| _version_ | 1797525867970166784 |
|---|---|
| author | Yan Li Chengcai Zhang Weidong Heng |
| author_facet | Yan Li Chengcai Zhang Weidong Heng |
| author_sort | Yan Li |
| collection | DOAJ |
| description | Surface soil moisture (SSM) is a major factor that affects crop growth. Combined microwave and optical data have been widely used to improve the accuracy of SSM retrievals. However, the influence of vegetation indices derived from the red-edge spectral bands of multi-spectral optical data on retrieval accuracy has not been sufficiently analyzed. In this study, we retrieved soil moisture from wheat-covered surfaces using Sentinel-1/2 data. First, a modified water cloud model (WCM) was proposed to remove the influence of vegetation from the backscattering coefficient of the radar data. The vegetation fraction (FV) was then introduced in this WCM, and the vegetation water content (VWC) was calculated using a multiple linear regression model. Subsequently, the support vector regression technique was used to retrieve the SSM. This approach was validated using in situ measurements of wheat fields in Hebi, located in northern Henan Province, China. The key findings of this study are: (1) Based on vegetation indices obtained from Sentinel-2 data, the proposed VWC estimation model effectively eliminated the influence of vegetation; (2) Compared with vertical transmit and horizontal receive (VH) polarization, vertical transmit and vertical receive (VV) polarization was better for detecting changes in SSM key phenological phases of wheat; (3) The validated model indicates that the proposed approach successfully retrieved SSM in the study area using Sentinel-1 and Sentinel-2 data. |
| first_indexed | 2024-03-10T09:20:07Z |
| format | Article |
| id | doaj.art-afad6ee2306a4290870e34fde0e9ea15 |
| institution | Directory Open Access Journal |
| issn | 2073-4441 |
| language | English |
| last_indexed | 2024-03-10T09:20:07Z |
| publishDate | 2021-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Water |
| spelling | doaj.art-afad6ee2306a4290870e34fde0e9ea152023-11-22T05:17:23ZengMDPI AGWater2073-44412021-07-011314198110.3390/w13141981Retrieving Surface Soil Moisture over Wheat-Covered Areas Using Data from Sentinel-1 and Sentinel-2Yan Li0Chengcai Zhang1Weidong Heng2School of Water Conservancy Engineering, Zhengzhou University, Zhengzhou 450001, ChinaSchool of Water Conservancy Engineering, Zhengzhou University, Zhengzhou 450001, ChinaSchool of Water Conservancy Engineering, Zhengzhou University, Zhengzhou 450001, ChinaSurface soil moisture (SSM) is a major factor that affects crop growth. Combined microwave and optical data have been widely used to improve the accuracy of SSM retrievals. However, the influence of vegetation indices derived from the red-edge spectral bands of multi-spectral optical data on retrieval accuracy has not been sufficiently analyzed. In this study, we retrieved soil moisture from wheat-covered surfaces using Sentinel-1/2 data. First, a modified water cloud model (WCM) was proposed to remove the influence of vegetation from the backscattering coefficient of the radar data. The vegetation fraction (FV) was then introduced in this WCM, and the vegetation water content (VWC) was calculated using a multiple linear regression model. Subsequently, the support vector regression technique was used to retrieve the SSM. This approach was validated using in situ measurements of wheat fields in Hebi, located in northern Henan Province, China. The key findings of this study are: (1) Based on vegetation indices obtained from Sentinel-2 data, the proposed VWC estimation model effectively eliminated the influence of vegetation; (2) Compared with vertical transmit and horizontal receive (VH) polarization, vertical transmit and vertical receive (VV) polarization was better for detecting changes in SSM key phenological phases of wheat; (3) The validated model indicates that the proposed approach successfully retrieved SSM in the study area using Sentinel-1 and Sentinel-2 data.https://www.mdpi.com/2073-4441/13/14/1981surface soil moisturesentinel-1 SARSentinel-2vegetation water contentwater cloud modelsupport vector regression |
| spellingShingle | Yan Li Chengcai Zhang Weidong Heng Retrieving Surface Soil Moisture over Wheat-Covered Areas Using Data from Sentinel-1 and Sentinel-2 Water surface soil moisture sentinel-1 SAR Sentinel-2 vegetation water content water cloud model support vector regression |
| title | Retrieving Surface Soil Moisture over Wheat-Covered Areas Using Data from Sentinel-1 and Sentinel-2 |
| title_full | Retrieving Surface Soil Moisture over Wheat-Covered Areas Using Data from Sentinel-1 and Sentinel-2 |
| title_fullStr | Retrieving Surface Soil Moisture over Wheat-Covered Areas Using Data from Sentinel-1 and Sentinel-2 |
| title_full_unstemmed | Retrieving Surface Soil Moisture over Wheat-Covered Areas Using Data from Sentinel-1 and Sentinel-2 |
| title_short | Retrieving Surface Soil Moisture over Wheat-Covered Areas Using Data from Sentinel-1 and Sentinel-2 |
| title_sort | retrieving surface soil moisture over wheat covered areas using data from sentinel 1 and sentinel 2 |
| topic | surface soil moisture sentinel-1 SAR Sentinel-2 vegetation water content water cloud model support vector regression |
| url | https://www.mdpi.com/2073-4441/13/14/1981 |
| work_keys_str_mv | AT yanli retrievingsurfacesoilmoistureoverwheatcoveredareasusingdatafromsentinel1andsentinel2 AT chengcaizhang retrievingsurfacesoilmoistureoverwheatcoveredareasusingdatafromsentinel1andsentinel2 AT weidongheng retrievingsurfacesoilmoistureoverwheatcoveredareasusingdatafromsentinel1andsentinel2 |