Global Sensitivity Analysis of a Water Cloud Model toward Soil Moisture Retrieval over Vegetated Agricultural Fields
The release of high-spatiotemporal-resolution Sentinel-1 Synthetic Aperture Radar (SAR) data to the public has provided an unprecedented opportunity to map soil moisture at watershed and agricultural field scales. However, the existing retrieval algorithms fail to derive soil moisture with expected...
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| Formaat: | Artikel |
| Taal: | English |
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MDPI AG
2021-09-01
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| Reeks: | Remote Sensing |
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| Online toegang: | https://www.mdpi.com/2072-4292/13/19/3889 |
| _version_ | 1827680630282911744 |
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| author | Chunfeng Ma Shuguo Wang Zebin Zhao Hanqing Ma |
| author_facet | Chunfeng Ma Shuguo Wang Zebin Zhao Hanqing Ma |
| author_sort | Chunfeng Ma |
| collection | DOAJ |
| description | The release of high-spatiotemporal-resolution Sentinel-1 Synthetic Aperture Radar (SAR) data to the public has provided an unprecedented opportunity to map soil moisture at watershed and agricultural field scales. However, the existing retrieval algorithms fail to derive soil moisture with expected accuracy. Insufficient understanding of the effects of soil and vegetation parameters on the backscatters is an important reason for this failure. To this end, we present a Sensitivity Analysis (SA) to quantify the effects of parameters on the dual-polarized backscatters of Sentinel-1 based on a Water Cloud Model (WCM) and multiple global SA methods. The identification of the incidence angle and polarization of Sentinel-1 and the description scheme of vegetation parameters (A, B and <i>α</i>) in WCM are especially emphasized in this analysis towards an optimal estimation of parameters. Multiple SA methods derive identical parameter importance ranks, indicating that a highly reasonable and reliable SA is performed. Comparison between two existing vegetation description schemes shows that the scheme using Vegetation Water Content (VWC) outperforms the scheme combing particle moisture content and VWC. Surface roughness, soil moisture, VWC, and B, are most sensitive on the backscatters. Variation of parameter sensitivity indices with incidence angle at different polarizations indicates that VV- and VH- polarized backscatters at small incidence angles are the optimal options for soil moisture and surface roughness estimation, respectively, while VV-polarized backscatter at larger incidence angles is well-suited for VWC and B estimation and HH-polarized backscatter is well suited for roughness estimation. This analysis improves the understanding of the effects of vegetated surface parameters on multi-angle and multi-polarized backscatters of Sentinel-1 SAR, informing improvement in SAR-based soil moisture retrieval. |
| first_indexed | 2024-03-10T06:53:19Z |
| format | Article |
| id | doaj.art-b6ca8ff2eee94f1289a23ea490f36b45 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T06:53:19Z |
| publishDate | 2021-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-b6ca8ff2eee94f1289a23ea490f36b452023-11-22T16:42:20ZengMDPI AGRemote Sensing2072-42922021-09-011319388910.3390/rs13193889Global Sensitivity Analysis of a Water Cloud Model toward Soil Moisture Retrieval over Vegetated Agricultural FieldsChunfeng Ma0Shuguo Wang1Zebin Zhao2Hanqing Ma3Key Laboratory of Remote Sensing of Gansu Province, Heihe Remote Sensing Experimental Research Station, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, ChinaSchool of Geography, Geomatics and Planning, Jiangsu Normal University, Xuzhou 221116, ChinaKey Laboratory of Remote Sensing of Gansu Province, Heihe Remote Sensing Experimental Research Station, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, ChinaLanzhou Information Center, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, ChinaThe release of high-spatiotemporal-resolution Sentinel-1 Synthetic Aperture Radar (SAR) data to the public has provided an unprecedented opportunity to map soil moisture at watershed and agricultural field scales. However, the existing retrieval algorithms fail to derive soil moisture with expected accuracy. Insufficient understanding of the effects of soil and vegetation parameters on the backscatters is an important reason for this failure. To this end, we present a Sensitivity Analysis (SA) to quantify the effects of parameters on the dual-polarized backscatters of Sentinel-1 based on a Water Cloud Model (WCM) and multiple global SA methods. The identification of the incidence angle and polarization of Sentinel-1 and the description scheme of vegetation parameters (A, B and <i>α</i>) in WCM are especially emphasized in this analysis towards an optimal estimation of parameters. Multiple SA methods derive identical parameter importance ranks, indicating that a highly reasonable and reliable SA is performed. Comparison between two existing vegetation description schemes shows that the scheme using Vegetation Water Content (VWC) outperforms the scheme combing particle moisture content and VWC. Surface roughness, soil moisture, VWC, and B, are most sensitive on the backscatters. Variation of parameter sensitivity indices with incidence angle at different polarizations indicates that VV- and VH- polarized backscatters at small incidence angles are the optimal options for soil moisture and surface roughness estimation, respectively, while VV-polarized backscatter at larger incidence angles is well-suited for VWC and B estimation and HH-polarized backscatter is well suited for roughness estimation. This analysis improves the understanding of the effects of vegetated surface parameters on multi-angle and multi-polarized backscatters of Sentinel-1 SAR, informing improvement in SAR-based soil moisture retrieval.https://www.mdpi.com/2072-4292/13/19/3889microwave remote sensingsynthetic aperture radarglobal sensitivity analysissoil moisture |
| spellingShingle | Chunfeng Ma Shuguo Wang Zebin Zhao Hanqing Ma Global Sensitivity Analysis of a Water Cloud Model toward Soil Moisture Retrieval over Vegetated Agricultural Fields Remote Sensing microwave remote sensing synthetic aperture radar global sensitivity analysis soil moisture |
| title | Global Sensitivity Analysis of a Water Cloud Model toward Soil Moisture Retrieval over Vegetated Agricultural Fields |
| title_full | Global Sensitivity Analysis of a Water Cloud Model toward Soil Moisture Retrieval over Vegetated Agricultural Fields |
| title_fullStr | Global Sensitivity Analysis of a Water Cloud Model toward Soil Moisture Retrieval over Vegetated Agricultural Fields |
| title_full_unstemmed | Global Sensitivity Analysis of a Water Cloud Model toward Soil Moisture Retrieval over Vegetated Agricultural Fields |
| title_short | Global Sensitivity Analysis of a Water Cloud Model toward Soil Moisture Retrieval over Vegetated Agricultural Fields |
| title_sort | global sensitivity analysis of a water cloud model toward soil moisture retrieval over vegetated agricultural fields |
| topic | microwave remote sensing synthetic aperture radar global sensitivity analysis soil moisture |
| url | https://www.mdpi.com/2072-4292/13/19/3889 |
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