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运用高光谱探测技术,获取不同密度下两个不同品种春玉米冠层不同时期的光谱特征反射参数,对高光谱原始反射率、反射率对数、反射率一阶导数、反射率二阶导数以及DVI、RVI、NDVI、SAVI 4种植被指数与玉米的LAI和FPAR进行相关分析,并建立两者之间的预测方程,通过对模型检验选出最佳预测模型。研究表明,一阶导数和4种植被指数都能很好地预测LAI和FPAR,其中,对LAI预测最好的是利用光谱一阶导数建立的指数函数模型,对FPAR预测最好的是利用NDVI建立的二次多项式模型。851 nm处的一阶导数对玉米的整个时期变化都比较敏感,LAI的最佳预测模型在玉米的整个时期都具有较好的预测性;855、758 nm波段组合的NDVI对FPAR的变化比较敏感,但当FPAR较大时,模型对其预测能力降低。
Using hyperspectral detection technology, the spectral characteristic reflectance parameters of two different varieties of spring maize canopy at different densities were obtained at different densities. The spectral reflectance, logarithm of reflectance, first derivative of reflectance, second derivative of reflectance, DVI, RVI, NDVI and SAVI correlation analysis were conducted with LAI and FPAR of maize, and the prediction equation was established between them, and the best prediction model was selected by model test. The results show that both the first derivative and the four vegetation indices are good predictors of LAI and FPAR. Among them, the best predictor of LAI is the exponential function model based on the first derivative of the spectrum. The best prediction for FPAR is using NDVI The established quadratic polynomial model. The first derivative at 851 nm was sensitive to the whole period of maize, and the best prediction model of LAI had better predictability over the whole maize period. NDVI at 855,758 nm band was more sensitive to the change of FPAR , But when the FPAR is large, the model reduces its ability to predict.