Soil Moisture Retrieval From Sentinel-1 and Sentinel-2 Data Using Ensemble Learning Over Vegetated Fields
Soil moisture (SM) is valuable basic data in climate, hydrological models, and agricultural applications. The rapid development of remote sensing technology can be used to monitor changes in SM at multiple spatial and temporal scales. In this article, we unfolded an SM retrieval method using ensembl...
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10037202/ |
| _version_ | 1797905450262331392 |
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| author | Liguo Wang Ya Gao |
| author_facet | Liguo Wang Ya Gao |
| author_sort | Liguo Wang |
| collection | DOAJ |
| description | Soil moisture (SM) is valuable basic data in climate, hydrological models, and agricultural applications. The rapid development of remote sensing technology can be used to monitor changes in SM at multiple spatial and temporal scales. In this article, we unfolded an SM retrieval method using ensemble learning combined with the Water Cloud Model (WCM) by Sentinel-1 and Sentinel-2 with multisource datasets. First, using the WCM, the influence of vegetation cover on the backscattering coefficient was removed, where we use three vegetation index (enhanced vegetation index (EVI), normalized difference vegetation index, and normalized difference water index) for analysis and comparison. Then, combined with other multisource datasets, an SM retrieval model was established based on the ensemble learning algorithm. Here, we choose two familiar ensemble learning algorithms for analysis and comparison, using Pearson correlation significance analysis, which are the random forest (RF) and the adaptive boosting (AdaBoost). The results revealed that the RF model performed is slightly superior to the AdaBoost model. The optimal performance mean absolute error, root-mean-square error (RMSE), and the unbiased RMSE of RF model are 2.289 vol%, 2.934 vol%, 2.934 vol%, respectively, which are slightly better than the AdaBoost model. EVI is suitable for WCM model to remove vegetation scattering effect. It shows that it is attainable to utilize the ensemble learning method to inversion of SM using radar data. The proposed framework maximizes the potential of WCM, RF model, and multisource datasets in deriving spatiotemporally continuous SM estimates, which should be valuable for SM inversion development. |
| first_indexed | 2024-04-10T10:05:30Z |
| format | Article |
| id | doaj.art-42ebdeee9c7d4b0284ea9c3f93ac5550 |
| institution | Directory Open Access Journal |
| issn | 2151-1535 |
| language | English |
| last_indexed | 2024-04-10T10:05:30Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj.art-42ebdeee9c7d4b0284ea9c3f93ac55502023-02-16T00:00:05ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing2151-15352023-01-01161802181410.1109/JSTARS.2023.324226410037202Soil Moisture Retrieval From Sentinel-1 and Sentinel-2 Data Using Ensemble Learning Over Vegetated FieldsLiguo Wang0https://orcid.org/0000-0001-9373-6233Ya Gao1https://orcid.org/0000-0002-5060-860XHarbin Engineering University, Harbin, ChinaHarbin Engineering University, Harbin, ChinaSoil moisture (SM) is valuable basic data in climate, hydrological models, and agricultural applications. The rapid development of remote sensing technology can be used to monitor changes in SM at multiple spatial and temporal scales. In this article, we unfolded an SM retrieval method using ensemble learning combined with the Water Cloud Model (WCM) by Sentinel-1 and Sentinel-2 with multisource datasets. First, using the WCM, the influence of vegetation cover on the backscattering coefficient was removed, where we use three vegetation index (enhanced vegetation index (EVI), normalized difference vegetation index, and normalized difference water index) for analysis and comparison. Then, combined with other multisource datasets, an SM retrieval model was established based on the ensemble learning algorithm. Here, we choose two familiar ensemble learning algorithms for analysis and comparison, using Pearson correlation significance analysis, which are the random forest (RF) and the adaptive boosting (AdaBoost). The results revealed that the RF model performed is slightly superior to the AdaBoost model. The optimal performance mean absolute error, root-mean-square error (RMSE), and the unbiased RMSE of RF model are 2.289 vol%, 2.934 vol%, 2.934 vol%, respectively, which are slightly better than the AdaBoost model. EVI is suitable for WCM model to remove vegetation scattering effect. It shows that it is attainable to utilize the ensemble learning method to inversion of SM using radar data. The proposed framework maximizes the potential of WCM, RF model, and multisource datasets in deriving spatiotemporally continuous SM estimates, which should be valuable for SM inversion development.https://ieeexplore.ieee.org/document/10037202/Adaptive boosting (AdaBoost)ensemble learningrandom forest (RF)Sentinel-1/2soil moisture (SM)Water Cloud Model (WCM) |
| spellingShingle | Liguo Wang Ya Gao Soil Moisture Retrieval From Sentinel-1 and Sentinel-2 Data Using Ensemble Learning Over Vegetated Fields IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Adaptive boosting (AdaBoost) ensemble learning random forest (RF) Sentinel-1/2 soil moisture (SM) Water Cloud Model (WCM) |
| title | Soil Moisture Retrieval From Sentinel-1 and Sentinel-2 Data Using Ensemble Learning Over Vegetated Fields |
| title_full | Soil Moisture Retrieval From Sentinel-1 and Sentinel-2 Data Using Ensemble Learning Over Vegetated Fields |
| title_fullStr | Soil Moisture Retrieval From Sentinel-1 and Sentinel-2 Data Using Ensemble Learning Over Vegetated Fields |
| title_full_unstemmed | Soil Moisture Retrieval From Sentinel-1 and Sentinel-2 Data Using Ensemble Learning Over Vegetated Fields |
| title_short | Soil Moisture Retrieval From Sentinel-1 and Sentinel-2 Data Using Ensemble Learning Over Vegetated Fields |
| title_sort | soil moisture retrieval from sentinel 1 and sentinel 2 data using ensemble learning over vegetated fields |
| topic | Adaptive boosting (AdaBoost) ensemble learning random forest (RF) Sentinel-1/2 soil moisture (SM) Water Cloud Model (WCM) |
| url | https://ieeexplore.ieee.org/document/10037202/ |
| work_keys_str_mv | AT liguowang soilmoistureretrievalfromsentinel1andsentinel2datausingensemblelearningovervegetatedfields AT yagao soilmoistureretrievalfromsentinel1andsentinel2datausingensemblelearningovervegetatedfields |