摘要:
叶面积指数(leaf area index,LAI)作为植被冠层的重要参数,对作物长势监测及产量估算具有重要意义.本研究以黑河流域张掖绿洲试验区为例,基于机载航空高光谱遥感影像(compact airborne spectrographic imager,CASI)数据,利用物理模型与统计模型对研究区的LAI进行估测反演.首先,利用归一化植被指数(normalized difference vegetation index,NDVI)与相应实测LAI数据建立最佳线性回归模型;然后,基于混合像元分解模型和多次散射植被冠层模型构建物理模型;最后,以线性回归模型为参比修正多次散射植被冠层模型,构建半经验LAI反演模型,并比较上述模型拟合效果.研究结果表明,半经验模型为绿洲区LAI反演最优模型,模型估算精度R2达到0.89,精度提高较显著.研究对提升作物LAI的估算精度有一定意义,并将进一步推动精细农业定量遥感理论的研究与应用.
Abstract:
As the vegetation canopy' s important parameter, the leaf area index ( LAI) has important significance for crop growth monitoring and yield estimation. In this study, the authors used the hyperspectral compact airborne spectrographic imager ( CASI ) data of Zhangye Oasis experimental area in Heihe River Basin as the experiment object and relied on physical and statistical model to estimate the inversion of the LAI. The process is as follows:First, the optimal linear regression model is established by using the normalized difference vegetation index ( NDVI) and the corresponding measured LAI data. Then the physical model is adopted based on the combination of the mixed pixel decomposition model and the multiple scattering vegetation canopy model. With the linear regression model as the reference, the multiple scattering vegetation canopy model is modified, and the semi -empirical LAI inversion model is constructed. Finally, the fitting effects of the models are compared with each other. The results show that the semi-empirical model is the best model for LAI inversion in oasis area and its estimation accuracy of R2 increases significantly to 0. 89. This study provides technical support for the estimation of crop leaf area index in high precision, and will further promote the study and application of quantitative remote sensing theory about precision agriculture.
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