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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2406.09812 |
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| _version_ | 1866911918202028032 |
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| author | Zhao, Weiying Chuluunbat, Ganzorig Unagaev, Aleksei Efremova, Natalia |
| author_facet | Zhao, Weiying Chuluunbat, Ganzorig Unagaev, Aleksei Efremova, Natalia |
| contents | This study introduces a framework for forecasting soil nitrogen content, leveraging multi-modal data, including multi-sensor remote sensing images and advanced machine learning methods. We integrate the Land Use/Land Cover Area Frame Survey (LUCAS) database, which covers European and UK territory, with environmental variables from satellite sensors to create a dataset of novel features. We further test a broad range of machine learning algorithms, focusing on tree-based models such as CatBoost, LightGBM, and XGBoost. We test the proposed methods with a variety of land cover classes, including croplands and grasslands to ensure the robustness of this approach. Our results demonstrate that the CatBoost model surpasses other methods in accuracy. This research advances the field of agricultural management and environmental monitoring and demonstrates the significant potential of integrating multisensor remote sensing data with machine learning for environmental analysis. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_09812 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Soil nitrogen forecasting from environmental variables provided by multisensor remote sensing images Zhao, Weiying Chuluunbat, Ganzorig Unagaev, Aleksei Efremova, Natalia Information Retrieval This study introduces a framework for forecasting soil nitrogen content, leveraging multi-modal data, including multi-sensor remote sensing images and advanced machine learning methods. We integrate the Land Use/Land Cover Area Frame Survey (LUCAS) database, which covers European and UK territory, with environmental variables from satellite sensors to create a dataset of novel features. We further test a broad range of machine learning algorithms, focusing on tree-based models such as CatBoost, LightGBM, and XGBoost. We test the proposed methods with a variety of land cover classes, including croplands and grasslands to ensure the robustness of this approach. Our results demonstrate that the CatBoost model surpasses other methods in accuracy. This research advances the field of agricultural management and environmental monitoring and demonstrates the significant potential of integrating multisensor remote sensing data with machine learning for environmental analysis. |
| title | Soil nitrogen forecasting from environmental variables provided by multisensor remote sensing images |
| topic | Information Retrieval |
| url | https://arxiv.org/abs/2406.09812 |