| 引用本文: | 张 彦,贺 佳,张晓飞,等.基于土壤背景剔除的玉米早期根域
土壤含水率反演方法研究[J].灌溉排水学报,2025,44(10):1-7. |
| ZHANG Yan,HE Jia,ZHANG Xiaofei,et al.基于土壤背景剔除的玉米早期根域
土壤含水率反演方法研究[J].灌溉排水学报,2025,44(10):1-7. |
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| 摘要: |
| 【目的】探究土壤含水率反演精度的影响因素，筛选最优数据组合与建模方法，优化无人机遥感监测玉米早期土壤含水率的反演方法。【方法】在玉米覆盖度差异显著时期，采集无人机影像与地面数据，运用阈值法剔除土壤背景，计算植被覆盖度。通过提取多种光谱和纹理特征，引入植被覆盖度构建多种数据组合模式。采用随机森林回归、岭回归及偏最小二乘回归分别构建表层土壤含水率反演模型，进而分析不同数据组合模式对表层土壤含水率估算的精度。【结果】剔除背景对模型精度的提升效果因方法与数据集而异，RGB传感器剔除土壤背景后反演精度提升，而TIR传感器剔除土壤背景后的反演精度下降。可见光和热红外结合可显著提升模型精度，为模型提供更丰富的信息并提高鲁棒性。加入覆盖度后，未剔除与剔除背景下的表层土壤含水率预测精度均有提高，未剔除土壤背景RGB+TIR+FVC模式相较于RGB+TIR模式的决定系数（R2）提升了0.01；剔除背景后R2提升了0.11。【结论】不同数据结合及覆盖度对模型精度影响不同，筛选较优组合与方法可为玉米早期土壤含水率监测提供参考。 |
| 关键词: 玉米早期；表层土壤含水率；无人机；热红外；可见光；植被覆盖度 |
| DOI：10.13522/j.cnki.ggps.2025012 |
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| Improving UAV-based soil moisture measurement using optimal feature selections and background information removal |
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ZHANG Yan, HE Jia, ZHANG Xiaofei, GUO Yan, YANG Xiuzhong,
ZHANG Hongli, LIU Ting, WEI Panpan, WANG Laigang
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1. Institute of Agricultural Economic and Information, Henan Academy of Agricultural Sciences/
Key Laboratory of Huang-Huai-Hai Smart Agricultural Technology, Ministry of Agriculture and Rural Affairs/
Henan Province Engineering Research Center for Crop Planting Monitoring and Early Warning, Zhengzhou 450002, China;
2. Hebi Agricultural and Rural Development Service Center, Hebi 458030, China
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| Abstract: |
| 【Background and Objective】Topsoil water content is a critical factor influencing crop growth and yield, yet traditional measurement methods are often limited in efficiency and scalability. UAV-based remote sensing provides a promising alternative for rapid, high-resolution in situ measurements. This paper evaluates the factors that affect the accuracy of UAV-based soil water content inversion and identifies the optimal combinations of data types, features, and modelling approaches for improving the accuracy of the UAV-based method.【Method】The experiment was conducted in a maize field during its early growth stage, characterized by substantial variation in canopy coverage. UAV imageries and ground-truth measurements were collected simultaneously. A threshold method was applied to remove the influence of soil background information and calculate vegetation coverage. Spectral and texture features were extracted, and vegetation coverage was integrated into different data combination patterns. Three regression methods: random forest regression, ridge regression and partial least squares regression, were used to construct the inversion model for estimating topsoil water content; comparison of their performance was analyzed under different scenarios.【Result】① The effect of background information removal on model accuracy varied with regression method and the data extracted from sensors. In particular, inversion accuracy improved after soil background information removal for RGB sensors but decreased for TIR sensors. ② The combination of visible and thermal infrared data significantly improved model accuracy, providing richer information and improving robustness. ③ Incorporating vegetation coverage improved accuracy of the predicted topsoil water content both with and without background information removal. For datasets without background information removal, the R2 of the methods using the RGB+TIR+FVC pattern increased by 0.01 compared to that of using the RGB+TIR pattern. After background information removal, their R2 increased by 0.11.【Conclusion】Our results show that different data combinations and inclusion of vegetation coverage had varying effects on the accuracy of UAV-based method for topsoil water content estimation. We screened optimal combinations and methods to increase the accuracy of the method for estimating topsoil water content in the early maize growing stage. |
| Key words: early stage of corn; soil surface moisture content; unmanned aerial vehicle; thermal infrared; visible light; vegetation coverage |
