| 引用本文: | 张 昊,王祖基,李彩霞,等.融合多光谱与热红外特征的夏玉米植株水分估算研究[J].灌溉排水学报,2026,45(7):19-27. |
| Zhang Hao,Wang Zuji,Li Caixia,et al.融合多光谱与热红外特征的夏玉米植株水分估算研究[J].灌溉排水学报,2026,45(7):19-27. |
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| 摘要: |
| 【目的】作物含水率是反映作物水分胁迫与生理状态的重要指标，其高精度估算对于农业水分管理、干旱监测及作物生长评估具有重要意义。传统地面监测方法成本高、仅代表点信息，而无人机遥感提供了无损、大尺度监测手段，但其依赖的光谱特征主要反应植株静态结构属性，对水分胁迫的响应滞后于作物生理状态的冠层温度变化。因此，融合多源遥感特征成为实现作物水分实时、高精度估算的关键途径。【方法】融合无人机多光谱和热红外数据，构建了多源数据融合模型，结合随机森林（RF）、K近邻回归（KNN）和支持向量回归（SVR）算法，估算玉米生育期的植株含水率（PWC）。【结果】①KNN在NDVI+GNDVI+RVI植被指数为特征的PWC模拟中表现最优（R2=0.713，rRMSE=6.610%）；②RF在多源数据的综合特殊指数估算PWC模拟中精度显著优于SVR、KNN模型（R2=0.887，rRMSE=4.298%）；③NDVI+GNDVI+RVI+LST特征指数结合RF能够高精度反映研究区植株水分状况的时序变化及空间分布特征。【结论】多源数据融合结合RF能够实现对玉米植株水分的高精度估算。 |
| 关键词: 植株水分；多光谱；热红外；随机森林；植被指数 |
| DOI：10.13522/j.cnki.ggps.2025289 |
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| Estimating maize plant water content based on UAV multispectral-thermal infrared fusion and machine learning |
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Zhang Hao, Wang Zuji, Li Caixia, Ma Yanchuan, James E. Kanneh, Zhong Daokuan, Li Shenglin
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1. North China University of Water Resources and Electric Power, Zhengzhou 450046, China; 2. Institute of Farmland Irrigation, Chinese Academy of Agricultural Sciences, Xinxiang 453002, China; 3. Shandong Agricultural University, Tai’an 271018, China;
4. Henan Institute of Science and Technology, Xinxiang 453003, China
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| Abstract: |
| 【Background & Objective】Crop water content is a critical indicator for characterizing crop water stress and physiological status; its accurate estimation is essential for agricultural water management, drought monitoring, and crop growth assessment. Traditional ground-based crop water monitoring methods are costly and rely on limited point-observations, which cannot support large-scale and real-time drought diagnosis. The advances in unmanned aerial vehicle (UAV) remote sensing provide a non-destructive, large-scale alternative to conventional methods. However, UAV-derived spectral features mainly reflect static crop structural properties and have a response delay to crop water stress. Canopy temperature directly characterizes crop physiological changes. In this context, this study proposed a method that integrates multi-source UAV remote sensing features to construct a high-precision, real-time crop water content estimation model.【Method】The method fused UAV-acquired multispectral and thermal infrared images to construct a multi-source data fusion model. Three machine learning algorithms, including Random Forest (RF), K-Nearest Neighbor regression (KNN), and Support Vector Regression (SVR), were used to estimate the variation in plant water content (PWC) of maize throughout its growing season.【Result】①The KNN algorithm combined with NDVI + GNDVI + RVI was most accurate for estimating PWC, with a coefficient of determination (R2) of 0.713 and a relative root mean square error (rRMSE) of 6.610%. ②Based on integrated multi-source spectral-thermal indices, the RF algorithm significantly outperformed SVR and KNN in PWC estimation, with an R2 of 0.887 and an rRMSE of 4.298%. ③The combination of spectral features (NDVI + GNDVI + RVI) and thermal feature (LST) coupled with the RF algorithm effectively captured the spatiotemporal dynamics of PWC in the study area.【Conclusion】The fusion of multi-source UAV remote sensing data combined with the RF algorithm can accurately estimate maize plant water content; it has good adaptability for crop water estimation under multidimensional feature conditions and offers a reliable technical support for crop water monitoring and drought assessment. |
| Key words: plant water content; multispectral; thermal infrared; random forest; vegetation indices |
