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| DOI：10.13522/j.cnki.ggps.2026030 |
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| UAV hyperspectral remote sensing combined with machine learning for dynamic monitoring of soil moisture in mountainous chili pepper fields |
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Zeng Xitong, Zhang Wei, Fu Jing, Wang Jie, Li Changjiang, He Yupu, Wan Jiawei
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1. Nanjing Hydraulic Research Institute, Nanjing 210029, China;
2. Jiangsu Province Rural Water Conservancy Science and Technology Development Center, Nanjing 210029, China;
3. Guizhou Provincial Institute of Water Resources Science, Guiyang 550002, China
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| Abstract: |
| 【Objective】Chili pepper is an important economic crop in Guizhou Province, and its healthy growth depends on a favourable soil moisture environment. Traditional soil moisture monitoring systems are costly and point-based with poor spatial representativeness. Although UAV hyperspectral remote sensing and machine learning enable soil moisture inversion, the optimal spectral feature combination and inversion algorithm for chili pepper grown in complex hilly regions remain unclear. This study aims to fill this technological gap.【Method】The field experiment was conducted in a fragmented mountainous chili irrigation area in Guizhou Province. Nine phases of UAV hyperspectral images and the corresponding soil moisture content (SMC) data were measured in situ in the field. After screening SMC-sensitive spectral features and optimizing the spectral feature space, five machine learning algorithms, including Partial Least Squares Regression (PLSR), L2 Regularization Regression (L2), Decision Tree (DT), Random Forest (RF), and Categorical Boosting (CatBoost), were used to construct the SMC inversion model. The accuracy of each model was tested against ground-truth data.【Result】The optimal model inputs were 36 SMC-sensitive spectral bands (represented by B5 at 400.878 nm and B128 at 926.664 nm) and three spectral indices: Enhanced Vegetation Index (EVI), Difference Vegetation Index (DVI), and Ratio Vegetation Index 2 (RVI2). Among all tested algorithms, CatBoost yielded the most accurate inversion on the test set, with a coefficient of determination of 0.758, a root mean square error of 2.897, and a mean absolute percentage error of 5.902%.【Conclusion】For monitoring soil moisture content in fragmented mountainous chili pepper fields, the CatBoost algorithm achieved the highest accuracy. Integrating the UAV hyperspectral technique and CatBoost model can reliably monitor soil moisture dynamics, facilitate precise water regulation and improve water use efficiency. |
| Key words: chilli pepper; UAV remote sensing; hyperspectral data; soil moisture content; CatBoost |
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