Soil Moisture Retrieval during the Wheat Growth Cycle Using SAR and Optical Satellite Data
The objective of this paper is to propose a combined approach for the high-precision mapping of soil moisture during the wheat growth cycle based on synthetic aperture radar (SAR) (Radarsat-2) and optical satellite data (Landsat-8). For this purpose, the influence of vegetation was removed from the...
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MDPI AG
2021-01-01
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| Online Access: | https://www.mdpi.com/2073-4441/13/2/135 |
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| author | Min Zhang Fengkai Lang Nanshan Zheng |
| author_facet | Min Zhang Fengkai Lang Nanshan Zheng |
| author_sort | Min Zhang |
| collection | DOAJ |
| description | The objective of this paper is to propose a combined approach for the high-precision mapping of soil moisture during the wheat growth cycle based on synthetic aperture radar (SAR) (Radarsat-2) and optical satellite data (Landsat-8). For this purpose, the influence of vegetation was removed from the total backscatter by using the modified water cloud model (MWCM), which takes the vegetation fraction (<inline-formula><math display="inline"><semantics><mrow><msub><mi>f</mi><mrow><mi>v</mi><mi>e</mi><mi>g</mi></mrow></msub></mrow></semantics></math></inline-formula>) into account. The VV/VH polarization radar backscattering coefficients database was established by a numerical simulation based on the advanced integrated equation model (AIEM) and the cross-polarized ratio of the Oh model. Then the empirical relationship between the bare soil backscattering coefficient and both the soil moisture and the surface roughness was developed by regression analysis. The surface roughness in this paper was described by using the effective roughness parameter and the combined roughness form. The experimental results revealed that using effective roughness as the model input instead of in-situ measured roughness can obtain soil moisture with high accuracy and effectively avoid the uncertainty of roughness measurement. The accuracy of soil moisture inversion could be improved by introducing vegetation fraction on the basis of the water cloud model (WCM). There was a good correlation between the estimated soil moisture and the observed values, with a root mean square error (RMSE) of about 4.14% and the coefficient of determination (<inline-formula><math display="inline"><semantics><mrow><msup><mi>R</mi><mn>2</mn></msup></mrow></semantics></math></inline-formula>) about 0.7390. |
| first_indexed | 2024-03-09T05:31:26Z |
| format | Article |
| id | doaj.art-87813e069b9c4dcdb81840e6d0916446 |
| institution | Directory Open Access Journal |
| issn | 2073-4441 |
| language | English |
| last_indexed | 2024-03-09T05:31:26Z |
| publishDate | 2021-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Water |
| spelling | doaj.art-87813e069b9c4dcdb81840e6d09164462023-12-03T12:32:07ZengMDPI AGWater2073-44412021-01-0113213510.3390/w13020135Soil Moisture Retrieval during the Wheat Growth Cycle Using SAR and Optical Satellite DataMin Zhang0Fengkai Lang1Nanshan Zheng2Jiangsu Key Laboratory of Resources and Environmental Information Engineering, China University of Mining and Technology, Xuzhou 221116, ChinaJiangsu Key Laboratory of Resources and Environmental Information Engineering, China University of Mining and Technology, Xuzhou 221116, ChinaJiangsu Key Laboratory of Resources and Environmental Information Engineering, China University of Mining and Technology, Xuzhou 221116, ChinaThe objective of this paper is to propose a combined approach for the high-precision mapping of soil moisture during the wheat growth cycle based on synthetic aperture radar (SAR) (Radarsat-2) and optical satellite data (Landsat-8). For this purpose, the influence of vegetation was removed from the total backscatter by using the modified water cloud model (MWCM), which takes the vegetation fraction (<inline-formula><math display="inline"><semantics><mrow><msub><mi>f</mi><mrow><mi>v</mi><mi>e</mi><mi>g</mi></mrow></msub></mrow></semantics></math></inline-formula>) into account. The VV/VH polarization radar backscattering coefficients database was established by a numerical simulation based on the advanced integrated equation model (AIEM) and the cross-polarized ratio of the Oh model. Then the empirical relationship between the bare soil backscattering coefficient and both the soil moisture and the surface roughness was developed by regression analysis. The surface roughness in this paper was described by using the effective roughness parameter and the combined roughness form. The experimental results revealed that using effective roughness as the model input instead of in-situ measured roughness can obtain soil moisture with high accuracy and effectively avoid the uncertainty of roughness measurement. The accuracy of soil moisture inversion could be improved by introducing vegetation fraction on the basis of the water cloud model (WCM). There was a good correlation between the estimated soil moisture and the observed values, with a root mean square error (RMSE) of about 4.14% and the coefficient of determination (<inline-formula><math display="inline"><semantics><mrow><msup><mi>R</mi><mn>2</mn></msup></mrow></semantics></math></inline-formula>) about 0.7390.https://www.mdpi.com/2073-4441/13/2/135soil moistureAIEMmodified water cloud modelOh modeleffective roughness |
| spellingShingle | Min Zhang Fengkai Lang Nanshan Zheng Soil Moisture Retrieval during the Wheat Growth Cycle Using SAR and Optical Satellite Data Water soil moisture AIEM modified water cloud model Oh model effective roughness |
| title | Soil Moisture Retrieval during the Wheat Growth Cycle Using SAR and Optical Satellite Data |
| title_full | Soil Moisture Retrieval during the Wheat Growth Cycle Using SAR and Optical Satellite Data |
| title_fullStr | Soil Moisture Retrieval during the Wheat Growth Cycle Using SAR and Optical Satellite Data |
| title_full_unstemmed | Soil Moisture Retrieval during the Wheat Growth Cycle Using SAR and Optical Satellite Data |
| title_short | Soil Moisture Retrieval during the Wheat Growth Cycle Using SAR and Optical Satellite Data |
| title_sort | soil moisture retrieval during the wheat growth cycle using sar and optical satellite data |
| topic | soil moisture AIEM modified water cloud model Oh model effective roughness |
| url | https://www.mdpi.com/2073-4441/13/2/135 |
| work_keys_str_mv | AT minzhang soilmoistureretrievalduringthewheatgrowthcycleusingsarandopticalsatellitedata AT fengkailang soilmoistureretrievalduringthewheatgrowthcycleusingsarandopticalsatellitedata AT nanshanzheng soilmoistureretrievalduringthewheatgrowthcycleusingsarandopticalsatellitedata |