Potential of Remote Sensing Images for Soil Moisture Retrieving Using Ensemble Learning Methods in Vegetation-Covered Area
Soil moisture (SM) plays a critical role in various fields such as agriculture, hydrology, and land-atmosphere interactions. This study aims to evaluate the performance of the categorical boosting algorithm (CatBoost) in comparison to other multiple-boosting algorithms for SM prediction. Appropriate...
| Main Authors: | , , , , |
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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/10237270/ |
| _version_ | 1797682049084030976 |
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| author | Ya Gao Liguo Wang Geji Zhong Yitong Wang Jinghui Yang |
| author_facet | Ya Gao Liguo Wang Geji Zhong Yitong Wang Jinghui Yang |
| author_sort | Ya Gao |
| collection | DOAJ |
| description | Soil moisture (SM) plays a critical role in various fields such as agriculture, hydrology, and land-atmosphere interactions. This study aims to evaluate the performance of the categorical boosting algorithm (CatBoost) in comparison to other multiple-boosting algorithms for SM prediction. Appropriate feature selection is vital for achieving accurate predictions, and this study focuses on identifying relevant features and assessing CatBoost's suitability for the task. The study incorporates several boosting algorithms including Gradient Boosting Decision Tree (GBDT), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and CatBoost to estimate SM. Results indicate that radar backscatter coefficient, soil roughness, and digital elevation model (DEM) are crucial features for SM retrieval. Comparatively, CatBoost outperforms GBDT, XGBoost, and LightGBM in various feature combinations. The most favorable results are obtained when utilizing all features as inputs for the algorithm. These optimal results yield a mean absolute error (MAE) of 2.40 vol.%, mean relative error (MRE) of 0.16 vol.%, root mean square error (RMSE) of 3.26 vol.%, and Pearson correlation coefficient of 0.73. Additionally, the study analyzes the inversion results for different ranges of SM and Normalized Difference Vegetation Index (NDVI). Within the range of SM from 0 to 25 vol.% and NDVI from 0 to 0.7, utilizing all features yields the most accurate results. Using CatBoost, this approach achieves an MAE of 1.52 vol.%, MRE of 0.12 vol.%, RMSE of 2.11 vol.%, and R of 0.81. The study suggests that applying boosting algorithms, especially CatBoost, holds promise in accurately estimating surface SM. |
| first_indexed | 2024-03-11T23:53:52Z |
| format | Article |
| id | doaj.art-382370bd677c4d3184570df00f462782 |
| institution | Directory Open Access Journal |
| issn | 2151-1535 |
| language | English |
| last_indexed | 2024-03-11T23:53:52Z |
| 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-382370bd677c4d3184570df00f4627822023-09-18T23:00:14ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing2151-15352023-01-01168149816510.1109/JSTARS.2023.331109610237270Potential of Remote Sensing Images for Soil Moisture Retrieving Using Ensemble Learning Methods in Vegetation-Covered AreaYa Gao0https://orcid.org/0000-0002-5060-860XLiguo Wang1https://orcid.org/0000-0001-9373-6233Geji Zhong2Yitong Wang3Jinghui Yang4College of Information and Communication Engineering, Harbin Engineering University, Harbin, ChinaCollege of Information and Communication Engineering, Harbin Engineering University, Harbin, ChinaSchool of Ethnology and Sociology, Minzu University of China, Beijing, ChinaCollege of Information and Communication Engineering, Harbin Engineering University, Harbin, ChinaSchool of Information Engineering, China University of Geosciences, Beijing, ChinaSoil moisture (SM) plays a critical role in various fields such as agriculture, hydrology, and land-atmosphere interactions. This study aims to evaluate the performance of the categorical boosting algorithm (CatBoost) in comparison to other multiple-boosting algorithms for SM prediction. Appropriate feature selection is vital for achieving accurate predictions, and this study focuses on identifying relevant features and assessing CatBoost's suitability for the task. The study incorporates several boosting algorithms including Gradient Boosting Decision Tree (GBDT), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and CatBoost to estimate SM. Results indicate that radar backscatter coefficient, soil roughness, and digital elevation model (DEM) are crucial features for SM retrieval. Comparatively, CatBoost outperforms GBDT, XGBoost, and LightGBM in various feature combinations. The most favorable results are obtained when utilizing all features as inputs for the algorithm. These optimal results yield a mean absolute error (MAE) of 2.40 vol.%, mean relative error (MRE) of 0.16 vol.%, root mean square error (RMSE) of 3.26 vol.%, and Pearson correlation coefficient of 0.73. Additionally, the study analyzes the inversion results for different ranges of SM and Normalized Difference Vegetation Index (NDVI). Within the range of SM from 0 to 25 vol.% and NDVI from 0 to 0.7, utilizing all features yields the most accurate results. Using CatBoost, this approach achieves an MAE of 1.52 vol.%, MRE of 0.12 vol.%, RMSE of 2.11 vol.%, and R of 0.81. The study suggests that applying boosting algorithms, especially CatBoost, holds promise in accurately estimating surface SM.https://ieeexplore.ieee.org/document/10237270/Boostingcategorical boosting algorithm (CatBoost)extreme gradient boosting (XGBoost)gradient boosting decision tree (GBDT)light gradient boosting machine (lightGBM)sentinel-1 |
| spellingShingle | Ya Gao Liguo Wang Geji Zhong Yitong Wang Jinghui Yang Potential of Remote Sensing Images for Soil Moisture Retrieving Using Ensemble Learning Methods in Vegetation-Covered Area IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Boosting categorical boosting algorithm (CatBoost) extreme gradient boosting (XGBoost) gradient boosting decision tree (GBDT) light gradient boosting machine (lightGBM) sentinel-1 |
| title | Potential of Remote Sensing Images for Soil Moisture Retrieving Using Ensemble Learning Methods in Vegetation-Covered Area |
| title_full | Potential of Remote Sensing Images for Soil Moisture Retrieving Using Ensemble Learning Methods in Vegetation-Covered Area |
| title_fullStr | Potential of Remote Sensing Images for Soil Moisture Retrieving Using Ensemble Learning Methods in Vegetation-Covered Area |
| title_full_unstemmed | Potential of Remote Sensing Images for Soil Moisture Retrieving Using Ensemble Learning Methods in Vegetation-Covered Area |
| title_short | Potential of Remote Sensing Images for Soil Moisture Retrieving Using Ensemble Learning Methods in Vegetation-Covered Area |
| title_sort | potential of remote sensing images for soil moisture retrieving using ensemble learning methods in vegetation covered area |
| topic | Boosting categorical boosting algorithm (CatBoost) extreme gradient boosting (XGBoost) gradient boosting decision tree (GBDT) light gradient boosting machine (lightGBM) sentinel-1 |
| url | https://ieeexplore.ieee.org/document/10237270/ |
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