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| Prediction of Soil Organic Matter for Farmlands Covered With High
Density of Vegetation Based on UAV Hyperspectral Data |
| WANG Jie1, SUN Xiao-lin2*, WU Dan-hua3, ZHOU Ya-nan1, LIU Chang4, CAO Yue4, TANG Ye-tao4, ZHANG Mei-wei1, WANG Xiao-qing1, ZENG Ling-tao1, CUI Yu-pei1 |
1. School of Geography and Planning, Sun Yat-sen University, Guangzhou 510006, China
2. College of Agriculture, Guangxi University, Nanning 530004, China
3. Anfeng Town Integrated Service Center, Yancheng 224221, China
4. School of Environmental Science and Engineering, Sun Yat-sen University, Guangzhou 510006, China
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Abstract Accurate estimation of soil organic matter (SOM) content and its spatial distribution is crucial for sustainable agriculture and ecological management.Traditional SOM content measurement methods are insufficient, especially for high-density vegetation areas in tropical and subtropical regions. The widespread use of unmanned aerial vehicles (UAVs) and numerous studies predicting soil information based on vegetation remote sensing data provide a solution to this problem.To evaluate the proposed approach, a 75-hectare agricultural field in Shaoguan City, Guangdong Province, was selected as the study area. UAV hyperspectral imagery of vegetation at crop maturity was first acquired, and 103 soil samples were collected and transported to the laboratory for hyperspectral measurement and SOM content analysis. Subsequently, the continuous wavelet transform (CWT) was applied to extract features from both the UAV vegetation and laboratory soil hyperspectral data. Finally, SOM content estimation and mapping were performed using random forest algorithms on the hyperspectral data before and after feature extraction, with the results compared to those obtained using Ordinary Kriging-based mapping. The results indicate that: (1) There is a significant correlation between UAV vegetation hyperspectral data and SOM content, although the accuracy of SOM content estimation using UAV vegetation hyperspectral data was slightly lower than that using soil hyperspectral data; (2) After CWT, the accuracy of SOMcontent estimation using UAV vegetation hyperspectral data was superior to that of soil hyperspectral data, though still slightly lower than that of soil hyperspectral data after CWT; (3) The mapping accuracy of SOM content inversion using UAV vegetation hyperspectral data was better than that of the traditional Ordinary Kriging method, and was highly refined. Considering the significant advantages of UAV vegetation hyperspectral data in terms of cost and efficiency, this study suggests that the method of SOM contentestimation and mapping using UAV vegetation hyperspectral data is promising for providing abundant and detailed soil information for smart agriculture and other fields.
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Received: 2025-02-25
Accepted: 2025-06-16
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Corresponding Authors:
SUN Xiao-lin
E-mail: xiaolinsun@gxu.edu.cn
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