基于HyperScan成像光谱数据的植被叶绿素反演

Inversion of chlorophyll content based on HyperScan imaging spectral data

准确估算叶绿素含量对研究植被的生态效应具有重要意义。针对基于高光谱数据的植被叶绿素含量反演问题,在介绍HyperScan高光谱遥感成像系统组成、传感器特性以及辐射定标原理和遥感反射率计算方法基础上,利用HyperScan采集的不同植被叶片的高光谱图像数据,采用归一化植被指数(NDVI)、简单比值指数(SR)、绿波段叶绿素指数(CI green)、土壤调节植被指数(SAVI)、差值植被指数(DVI)、改进的土壤调节植被指数2(MSAVI2)、三角植被指数(TVI)和叶绿素吸收比值指数(CARI)等8种植被指数反演模型进行了叶绿素含量反演实验;同时进行了波段合并实验,并比较了在逐步合并波段情况下各种模型的反演精度。实验结果表明,在选取的植被指数中,2波段植被指数反演精度普遍高于3波段植被指数。在波段合并实验中,随着波段的合并,模型精度逐渐下降。研究中模型精度普遍较高,说明利用地面高光谱图像进行叶片叶绿素含量反演是可行的,且采用叶片内光谱反射率均值进行叶片叶绿素含量反演有很高的反演精度。

Accurate estimation of chlorophyll content has great significance in the study of the ecological effects of vegetation. In order to investigate inversion of vegetation chlorophyll content based hyperspectral data, the authors introduced the composition of HyperScan hyper - spectral remote sensing imaging system, the characteristics of remote sensor, the principle of radiometric calibration and the algorithm of remote sensing reflection efficiency. Based on the hyper - spectral image data collected with HyperScan, the authors adopted 8 vegetation index inversion models, i. e. , NDVI, SR, CI, SAVI, DVI, MSAVI2, TVI and CAR1, implemented the band merging experiment, and made a comparative study of the accuracy of each model under the circumstance of gradual band merging. The results show that, among the vegetation indexes collected in this paper, the two - band vegetation index generally has higher accuracy than the three - band vegetation index. In the band merging experiment, the model accuracy was decreasing gradually with the band mergence. The model accuracy is generally high in this study, which suggests that the use of ground hyper - spectral images to implement the inversion of chlorophyll is feasible, and the relatively high inversion accuracy can be achieved by adopting the mean value of internal optical spectrum reflection to implement the inversion of the volume of chlorophyll.