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作者	单位
王琪，常庆瑞，李铠，陈晓凯，缪慧玲，史博太,曾学亮,李振发	（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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中文摘要:

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含量估算的最优模型。

英文摘要:

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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