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作者	单位
李军玲,彭记永	（中国气象局·河南省农业气象保障与应用技术重点实验室/河南省气象科学研究所，河南郑州 450003）

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中文摘要:

为建立不同生育时期冬小麦叶面积指数（LAI）的高光谱遥感预测模型，2017年在荥阳和鹤壁大田区域进行野外试验，利用便携式光谱仪ASD FieldSpec Handheld测量不同生育时期冬小麦冠层高光谱数据，使用LAI2200冠层分析系统采集冬小麦冠层LAI。通过对高光谱数据进行不同形式的变换以及高光谱特征变量的计算，并与叶面积指数进行相关分析。结果表明，在拔节-抽穗期，LAI与Dr（红边幅值）、SDr（红边面积）、VI₃（红边面积SDr与蓝边面积SDb的比值指数）、VI₅（红边面积SDr与蓝边面积SDb的归一化指数）、VI₆（红边面积SDr与黄边面积SDy的归一化指数）的相关性较大，相关系数均大于0.85；在开花-乳熟期，LAI与Rr（红谷反射率）、VI₁（绿峰反射率Rg与红谷反射率Rr的比值指数）、VI₂（绿峰反射率Rg与红谷反射率Rr的归一化指数）、VI₃、VI₅的相关性较大，相关系数均大于0.7，且均通过0.01水平显著性检验。因此，拔节-抽穗期选择变量Dr、SDr、VI₃、VI₅、VI₆作为估算模型的自变量；开花-乳熟期选择变量Rr、VI₁、VI₂、VI₃、VI₅作为估算模型的自变量。拔节-孕穗期叶面积指数单变量估算模型中大部分变量的二次模型决定系数较大，其中VI₃、VI₅、lg(1/ρ676）、dρ₇₅₀/dλ₇₅₀的二次模型决定系数超过0.6，拟合程度较高，同时dρ₇₅₀/dλ₇₅₀的RMSE值最小，因此认为以dρ₇₅₀/dλ₇₅₀为自变量的二次模型最优。开花-乳熟期单光谱变量建立的叶面积指数估算各类模型中大部分参数的指数模型决定系数较大，其中Rr、VI₃、VI₅的指数模型决定系数超过0.7，拟合程度最高，同时VI₅的RMSE值最小，因此认为以VI₅为自变量的指数模型最优。

英文摘要:

To explore the relationship between hyperspectral reflectance and leaf area index(LAI), the experiment was conducted in Xingyang and Hebi in 2017. Winter wheat canopy hyperspectral data was measured at different growth stages by using the ASD FieldSpec HandHeld, and winter wheat LAI was collected at the same time by using LAI 2200 canopy analysis system. By correlation analysis between the countdown logarithms, first derivative, second derivative transformations of hyperspectral and LAI, the sensitivity spectral band and the index were obtained to estimate winter wheat LAI. The results showed that the correlation coefficients between LAI and Dr, SDr, VI₃, VI₅ and VI₆ were relatively higher from jointing to heading stage, which were more than 0.85. The correlation coefficients between LAI and Rr, VI₁, VI2, VI₃ and VI₅ were relatively higher from flowering to milking stage, which were more than 0.7, at 0.01 significance level. Therefore, from jointing to heading stage, variables such as Dr, SDr, VI₃, VI₅ and VI₆ were used as independent variables to estimate the model, and from flowering to milking stage, variables such as Rr, VI₁, VI₂, VI₃ and VI₅ were used as independent variables to estimate the model. From jointing to booting stage, the model of the most parameters in single spectral variables estimated LAI model had larger r(more than 0.6), including VI₃, VI₅, lg(1/ρ676) and dρ₇₅₀/dλ₇₅₀ , with an maximum fitting degree, while the RMSE of dρ₇₅₀/dλ₇₅₀ is minimum, which is the optimal variable model. From flowering to milking stage, the exponential model of the most parameters in single spectral variables estimated LAI model had larger r(more than 0.7), including Rr, VI₃, VI₅ models with maximum fitting degree, while the RMSE of VI₅ is minimum, which is the optimal variable exponential model.

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