| 基于主成分分析和随机森林回归的冬小麦冠层叶绿素含量估算 |
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| 引用本文： | 王 琪,常庆瑞,李 铠,陈晓凯,缪慧玲,史博太,曾学亮,李振发.基于主成分分析和随机森林回归的冬小麦冠层叶绿素含量估算[J].麦类作物学报,2024(4):532-542. |
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| 作者姓名： | 王 琪 常庆瑞 李 铠 陈晓凯 缪慧玲 史博太 曾学亮 李振发 |
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| 作者单位： | （1.西北农林科技大学资源环境学院，陕西杨凌 712100； 2.江西农业大学国土资源与环境学院，江西南昌 330045） |
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| 基金项目： | 国家863计划项目（2013AA102401-2） |
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| 摘 要： | 为提高冬小麦冠层光谱对叶绿素含量的估算精度，以陕西省乾县冬小麦为研究对象，利用SVC-1024i光谱仪和SPAD-502型叶绿素仪实测了冬小麦冠层反射率和叶绿素含量，分析了一阶导数光谱、10种特征参数和9种植被指数与叶绿素含量的相关性，并利用主成分分析(PCA)对叶绿素敏感的可见光波段(390～780 nm)一阶导数光谱进行降维，将特征值大于1的主分量结合特征参数和植被指数形成不同的输入变量，用偏最小二乘回归和随机森林回归构建冬小麦冠层叶绿素估算模型，并利用独立样本对模型进行验证。结果表明，小麦冠层叶绿素含量与一阶导数光谱在751 nm处的相关性最高(r=0.71),特征参数中红边蓝边归一化(SDr-SDb)/(SDr+SDb)与叶绿素含量的相关性最高(r=0.66),植被指数(VI)中修正归一化差异指数(mND705)相关性最高(r=0.74)。在输入变量相同的情况下，基于随机森林(RF)回归的预测模型均优于偏最小二乘回归(PLSR)模型，其中PCA-VI-RF模型的各精度指标均达到最优(r2=0.94,RMSE=1.05,RPD=3.70),是冬小麦冠层叶绿素...
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| 关 键 词： | 冬小麦 冠层叶绿素 主成分分析 偏最小二乘法 随机森林回归 |
Estimation of Winter Wheat Canopy Chlorophyll Content Based on Principal Component Analysis and Random Forest Regression |
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| WANG Qi,CHANG Qingrui,LI Kai,CHEN Xiaokai,MIAO Huiling,SHI Botai,ZENG Xueliang,LI Zhenfa.Estimation of Winter Wheat Canopy Chlorophyll Content Based on Principal Component Analysis and Random Forest Regression[J].Journal of Triticeae Crops,2024(4):532-542. |
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| Authors: | WANG Qi CHANG Qingrui LI Kai CHEN Xiaokai MIAO Huiling SHI Botai ZENG Xueliang LI Zhenfa |
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| Institution: | (1.College of Natural Resources and Environment, Northwest A&F University, Yangling, Shaanxi 712100, China; 2.College of Land Resources and Environment, Jiangxi Agricultural University, Nanchang, Jiangxi 330045, China) |
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| Abstract: | To further improve the accuracy of estimation of chlorophyll content by canopy spectra, winter wheat canopy reflectance and chlorophyll content were measured empirically using SVC-1024i spectrometer and SPAD-502 chlorophyll meter in Qian County, Shaanxi Province. The correlations between the first-order derivative spectra, 10 characteristic parameters and 9 vegetation indices and chlorophyll content were analyzed; the chlorophyll-sensitive first-order derivative spectra in the visible band(390-780 nm) were downscaled using principal component analysis (PCA), and the principal components with eigenvalues greater than 1 were combined with characteristic parameters and vegetation indices to form different input variables using partial least squares regression (PLSR) and random forest (RF) regression to construct a winter wheat canopy chlorophyll content estimation model, and the model was validated using independent samples. The results showed that canopy chlorophyll content had the highest correlation with the first-order derivative spectrum at 751 nm (r=0.71); the highest correlation was achieved between the normalized value of red-edge and blue-edge (SDr-SDb)/(SDr+SDb) and canopy chlorophyll content in the characteristic parameters (r=0.66) and between the modified normalized difference index (mND705) in the vegetation index (r=0.74). The PCA-VI-RF model was the best model for canopy chlorophyll content estimation in winter wheat (r2=0.94, RMSE=1.05, RPD=3.70), as the random forest (RF) regression based model outperformed the PLSR model with the same input independent variables. |
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| Keywords: | Winter wheat Canopy chlorophyll content Principal component analysis Partial least squares Random forest regression |
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