Combined Sentinel-1A With Sentinel-2A to Estimate Soil Moisture in Farmland
In this article, seven filter algorithms were compared. The Lee sigma method was more suitable for estimating soil moisture (SM) than the other filtering methods under different land cover types. First, we used a combination of roughness and the dual-polarized Sentinel-1A backscattering coefficients...
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| Format: | Article |
| Language: | English |
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IEEE
2021-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
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| Online Access: | https://ieeexplore.ieee.org/document/9288891/ |
| _version_ | 1818890131974651904 |
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| author | Ying Liu Jiaxin Qian Hui Yue |
| author_facet | Ying Liu Jiaxin Qian Hui Yue |
| author_sort | Ying Liu |
| collection | DOAJ |
| description | In this article, seven filter algorithms were compared. The Lee sigma method was more suitable for estimating soil moisture (SM) than the other filtering methods under different land cover types. First, we used a combination of roughness and the dual-polarized Sentinel-1A backscattering coefficients (VV and VH) to estimate SM in bare soil areas. Second, we employed water cloud model (WCM) to remove the influence of vegetation signals on the land surface backscattering and estimate SM in vegetation-covered areas. SM was also retrieved by modified soil moisture monitoring index (MSMMI) and modified perpendicular drought index (MPDI) of Sentinel-2A images. The results show that MSMMI can more accurately monitor SM in bare soil areas, which was slightly better than synthetic aperture radar (SAR) results. The SAR backscattering coefficients after the removal of vegetation influence by WCM can more precisely estimate SM in vegetation-covered areas, which is significantly better than MSMMI and MPDI, especially in high vegetation-covered areas. Optics and SAR differ in their abilities to estimate SM under different land cover, but the powerful fitting ability of machine learning can make full use of their advantages. We employed the generalized regression neural network (GRNN), support vector regression (SVR), random forest regression (RFR), and deep neural network (DNN) algorithms to estimate SM combining Sentinel-1A with Sentinel-2A images. The estimation accuracies of SM by regression algorithms were higher than those by the semiempirical SAR and optical models. The accuracy of estimated SM by DNN was higher than that of GRNN and RFR, which were better than SVR. |
| first_indexed | 2024-12-19T17:20:03Z |
| format | Article |
| id | doaj.art-5d72079e212a48c3a7a5ba75baec4a7c |
| institution | Directory Open Access Journal |
| issn | 2151-1535 |
| language | English |
| last_indexed | 2024-12-19T17:20:03Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj.art-5d72079e212a48c3a7a5ba75baec4a7c2022-12-21T20:12:42ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing2151-15352021-01-01141292131010.1109/JSTARS.2020.30436289288891Combined Sentinel-1A With Sentinel-2A to Estimate Soil Moisture in FarmlandYing Liu0Jiaxin Qian1https://orcid.org/0000-0002-8681-5731Hui Yue2https://orcid.org/0000-0003-0989-1738Xi'an University of Science and Technology, Xi'an, ChinaXi'an University of Science and Technology, Xi'an, ChinaXi'an University of Science and Technology, Xi'an, ChinaIn this article, seven filter algorithms were compared. The Lee sigma method was more suitable for estimating soil moisture (SM) than the other filtering methods under different land cover types. First, we used a combination of roughness and the dual-polarized Sentinel-1A backscattering coefficients (VV and VH) to estimate SM in bare soil areas. Second, we employed water cloud model (WCM) to remove the influence of vegetation signals on the land surface backscattering and estimate SM in vegetation-covered areas. SM was also retrieved by modified soil moisture monitoring index (MSMMI) and modified perpendicular drought index (MPDI) of Sentinel-2A images. The results show that MSMMI can more accurately monitor SM in bare soil areas, which was slightly better than synthetic aperture radar (SAR) results. The SAR backscattering coefficients after the removal of vegetation influence by WCM can more precisely estimate SM in vegetation-covered areas, which is significantly better than MSMMI and MPDI, especially in high vegetation-covered areas. Optics and SAR differ in their abilities to estimate SM under different land cover, but the powerful fitting ability of machine learning can make full use of their advantages. We employed the generalized regression neural network (GRNN), support vector regression (SVR), random forest regression (RFR), and deep neural network (DNN) algorithms to estimate SM combining Sentinel-1A with Sentinel-2A images. The estimation accuracies of SM by regression algorithms were higher than those by the semiempirical SAR and optical models. The accuracy of estimated SM by DNN was higher than that of GRNN and RFR, which were better than SVR.https://ieeexplore.ieee.org/document/9288891/Machine learning (ML) regressionSentinel-1Sentinel-2soil moisture (SM)speckle filter |
| spellingShingle | Ying Liu Jiaxin Qian Hui Yue Combined Sentinel-1A With Sentinel-2A to Estimate Soil Moisture in Farmland IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Machine learning (ML) regression Sentinel-1 Sentinel-2 soil moisture (SM) speckle filter |
| title | Combined Sentinel-1A With Sentinel-2A to Estimate Soil Moisture in Farmland |
| title_full | Combined Sentinel-1A With Sentinel-2A to Estimate Soil Moisture in Farmland |
| title_fullStr | Combined Sentinel-1A With Sentinel-2A to Estimate Soil Moisture in Farmland |
| title_full_unstemmed | Combined Sentinel-1A With Sentinel-2A to Estimate Soil Moisture in Farmland |
| title_short | Combined Sentinel-1A With Sentinel-2A to Estimate Soil Moisture in Farmland |
| title_sort | combined sentinel 1a with sentinel 2a to estimate soil moisture in farmland |
| topic | Machine learning (ML) regression Sentinel-1 Sentinel-2 soil moisture (SM) speckle filter |
| url | https://ieeexplore.ieee.org/document/9288891/ |
| work_keys_str_mv | AT yingliu combinedsentinel1awithsentinel2atoestimatesoilmoistureinfarmland AT jiaxinqian combinedsentinel1awithsentinel2atoestimatesoilmoistureinfarmland AT huiyue combinedsentinel1awithsentinel2atoestimatesoilmoistureinfarmland |