Inversion of leaf area index in Heihe Oasis based on CASI data
YANG Yuwei1, DAI Xiaoai1,2, NIU Yutian1, LIU Hanhu1, YANG Xiaoxia1, LAN Yan1
1. Academic of Earth Sciences, Chengdu University of Technology, Chengdu 610059, China; 2. Key Laboratory of Geoscience Spatial Information Technology, Ministry of Land and Resources of China, Chengdu 610059, China
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.
杨雨薇, 戴晓爱, 牛育天, 刘汉湖, 杨晓霞, 兰燕. 基于CASI数据的黑河绿洲区叶面积指数反演[J]. 国土资源遥感, 2017, 29(4): 179-184.
YANG Yuwei, DAI Xiaoai, NIU Yutian, LIU Hanhu, YANG Xiaoxia, LAN Yan. Inversion of leaf area index in Heihe Oasis based on CASI data. REMOTE SENSING FOR LAND & RESOURCES, 2017, 29(4): 179-184.
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2017, Vol. 29 
