Layered Soil Moisture Retrieval and Agricultural Application Based on Multi-Source Remote Sensing and Vegetation Suppression Technology: A Case Study of Youyi Farm, China
Soil moisture dynamics are a key parameter in regulating agricultural productivity and ecosystem functioning. The accurate monitoring and quantitative retrieval of soil moisture play a crucial role in optimizing agricultural water resource management. In recent years, the development of multi-source...
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MDPI AG
2025-06-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/13/2130 |
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| author | Zhonghe Zhao Yuyang Li Kun Liu Chunsheng Wu Bowei Yu Gaohuan Liu Youxiao Wang |
| author_facet | Zhonghe Zhao Yuyang Li Kun Liu Chunsheng Wu Bowei Yu Gaohuan Liu Youxiao Wang |
| author_sort | Zhonghe Zhao |
| collection | DOAJ |
| description | Soil moisture dynamics are a key parameter in regulating agricultural productivity and ecosystem functioning. The accurate monitoring and quantitative retrieval of soil moisture play a crucial role in optimizing agricultural water resource management. In recent years, the development of multi-source remote sensing technologies—such as high spatiotemporal resolution optical, radar, and thermal infrared sensors—has opened new avenues for efficient soil moisture retrieval. However, the accuracy of soil moisture retrieval decreases significantly when the soil is covered by vegetation. This study proposes a multi-modal remote sensing collaborative retrieval framework that integrates UAV-based multispectral imagery, Sentinel-1 radar data, and in situ ground sampling. By incorporating a vegetation suppression technique, a random-forest-based quantitative soil moisture model was constructed to specifically address the interference caused by dense vegetation during crop growing seasons. The results demonstrate that the retrieval performance of the model was significantly improved across different soil depths (0–5 cm, 5–10 cm, 10–15 cm, 15–20 cm). After vegetation suppression, the coefficient of determination (R<sup>2</sup>) exceeded 0.8 for all soil layers, while the mean absolute error (MAE) decreased by 35.1% to 49.8%. This research innovatively integrates optical–radar–thermal multi-source data and a physically driven vegetation suppression strategy to achieve high-accuracy, meter-scale dynamic mapping of soil moisture in vegetated areas. The proposed method provides a reliable technical foundation for precision irrigation and drought early warning. |
| format | Article |
| id | doaj-art-eb0ed67648014dc4a86df01ccb11663b |
| institution | Kabale University |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
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| series | Remote Sensing |
| spelling | doaj-art-eb0ed67648014dc4a86df01ccb11663b2025-08-20T03:28:25ZengMDPI AGRemote Sensing2072-42922025-06-011713213010.3390/rs17132130Layered Soil Moisture Retrieval and Agricultural Application Based on Multi-Source Remote Sensing and Vegetation Suppression Technology: A Case Study of Youyi Farm, ChinaZhonghe Zhao0Yuyang Li1Kun Liu2Chunsheng Wu3Bowei Yu4Gaohuan Liu5Youxiao Wang6Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, ChinaCollege of Geoexploration Science and Technology, Jilin University, Changchun 130300, ChinaCollege of Agronomy, Inner Mongolia Agricultural University, Hohhot 010019, ChinaInstitute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, ChinaState Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Beijing Normal University, Beijing 100875, ChinaInstitute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, ChinaSchool of Surveying and Geo-Informatics, Shandong Jianzhu University, Jinan 250101, ChinaSoil moisture dynamics are a key parameter in regulating agricultural productivity and ecosystem functioning. The accurate monitoring and quantitative retrieval of soil moisture play a crucial role in optimizing agricultural water resource management. In recent years, the development of multi-source remote sensing technologies—such as high spatiotemporal resolution optical, radar, and thermal infrared sensors—has opened new avenues for efficient soil moisture retrieval. However, the accuracy of soil moisture retrieval decreases significantly when the soil is covered by vegetation. This study proposes a multi-modal remote sensing collaborative retrieval framework that integrates UAV-based multispectral imagery, Sentinel-1 radar data, and in situ ground sampling. By incorporating a vegetation suppression technique, a random-forest-based quantitative soil moisture model was constructed to specifically address the interference caused by dense vegetation during crop growing seasons. The results demonstrate that the retrieval performance of the model was significantly improved across different soil depths (0–5 cm, 5–10 cm, 10–15 cm, 15–20 cm). After vegetation suppression, the coefficient of determination (R<sup>2</sup>) exceeded 0.8 for all soil layers, while the mean absolute error (MAE) decreased by 35.1% to 49.8%. This research innovatively integrates optical–radar–thermal multi-source data and a physically driven vegetation suppression strategy to achieve high-accuracy, meter-scale dynamic mapping of soil moisture in vegetated areas. The proposed method provides a reliable technical foundation for precision irrigation and drought early warning.https://www.mdpi.com/2072-4292/17/13/2130soil moisturemulti-source remote sensingvegetation suppressionrandom forestUAV multispectral imagery |
| spellingShingle | Zhonghe Zhao Yuyang Li Kun Liu Chunsheng Wu Bowei Yu Gaohuan Liu Youxiao Wang Layered Soil Moisture Retrieval and Agricultural Application Based on Multi-Source Remote Sensing and Vegetation Suppression Technology: A Case Study of Youyi Farm, China Remote Sensing soil moisture multi-source remote sensing vegetation suppression random forest UAV multispectral imagery |
| title | Layered Soil Moisture Retrieval and Agricultural Application Based on Multi-Source Remote Sensing and Vegetation Suppression Technology: A Case Study of Youyi Farm, China |
| title_full | Layered Soil Moisture Retrieval and Agricultural Application Based on Multi-Source Remote Sensing and Vegetation Suppression Technology: A Case Study of Youyi Farm, China |
| title_fullStr | Layered Soil Moisture Retrieval and Agricultural Application Based on Multi-Source Remote Sensing and Vegetation Suppression Technology: A Case Study of Youyi Farm, China |
| title_full_unstemmed | Layered Soil Moisture Retrieval and Agricultural Application Based on Multi-Source Remote Sensing and Vegetation Suppression Technology: A Case Study of Youyi Farm, China |
| title_short | Layered Soil Moisture Retrieval and Agricultural Application Based on Multi-Source Remote Sensing and Vegetation Suppression Technology: A Case Study of Youyi Farm, China |
| title_sort | layered soil moisture retrieval and agricultural application based on multi source remote sensing and vegetation suppression technology a case study of youyi farm china |
| topic | soil moisture multi-source remote sensing vegetation suppression random forest UAV multispectral imagery |
| url | https://www.mdpi.com/2072-4292/17/13/2130 |
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