基于光谱指数的喀斯特植物叶片叶绿素含量定量估算

为了探讨适合于喀斯特植物叶片叶绿素含量估算的光谱指数,在总结以往基于光谱指数的植物生化参数估算研究基础上发现,常用光谱指数通常采用差值、比值、归一化以及倒数差值方式来构建。因此,我们通过上述4种光谱指数构建方式对所采集的4种典型喀斯特植物——黄荆(Vitex negundo)、盐麸木(Rhus chinensis)、朴树(Celtis sinensis)和红背山麻杆(Alchornea trewioides)叶片原始光谱反射率及其一阶导数值与同步测定的叶片叶绿素含量进行遍历分析,以期获得最优光谱指数并将其应用于喀斯特植物叶片叶绿素含量定量估算研究。结果表明:(1)常用光谱指数中,改良红边归一化指数(modified red-edge normalized difference vegetation index, mND705)对喀斯特植物叶片叶绿素含量估算效果较好(决定系数为0.45,均方根误差为0.26 mg·g^-1)。(2)虽然荧光比值(fluorescence ratio index, FRI1)和叶绿素吸收面积光谱指数(chlorophyll absorption area index, CAAI)在估算喀斯特与非喀斯特植物叶片叶绿素含量能力相当,但是其估算精度相对较低(决定系数小于0.45)。(3)通过差值、比值、归一化以及倒数差值方式构建的光谱指数无论是基于植物叶片原始光谱反射率,还是其一阶导数值,相比常用光谱指数都能更好地估算喀斯特植物叶片叶绿素含量(决定系数大于0.60)。其中,基于植物叶片原始光谱反射率一阶导数值的差值光谱指数 dD(760, 769)]对喀斯特植物叶片叶绿素含量的估算精度最好,其决定系数为0.71,均方根误差为0.19 mg·g^-1。综上可知,结合高光谱遥感技术的光谱指数模型可快速定量估算喀斯特植物叶片叶绿素含量,为典型喀斯特地区植物生长诊断及其对环境胁迫适应性评价提供重要科学依据和技术支持。

Estimation of plant leaf chlorophyll content based on spectral index in karst areas

Leaf chlorophyll content is central to carbon, water and energy exchange between the biosphere and the atmosphere, also to the terrestrial ecosystem function. Quantitative estimates of leaf chlorophyll content with hyperspectral imagery can provide scientific insight for assessing plant''s growth and stress as affected by abiotic and biotic factors. However, few studies have been conducted on the application of spectral indexes in estimation of leaf chlorophyll contents of plants in karst areas, especially in South China. After a review of the application of common spectral indexes in estimation of leaf biochemistry parameters, we found that most of the common spectral indexes were developed based on the difference, simple ratios, normalized difference and inverse difference formulation of leaf spectral reflectance. Therefore, we firstly measured the raw reflectance spectra of leaves from four typical karst species, namely Vitex negundo, Rhus chinensis, Celtis sinensis and Alchornea trewioides with a ASD Field Spec 4(Analytical Spectral Devices, Inc., Boulder, Colorado, US)spectrometers. We then used the above-mentioned four formulations to process the raw reflectance spectra and their first-order derivative spectra. Finally, we analyzed the relation between leaf chlorophyll contents and relative leaf raw reflectance spectra and their first-order derivative spectra, and tried to propose the best spectral index for estimation leaf chlorophyll content of plants of karst areas in South China. The results were as follows:(1)Among the common spectral indexes, the modified normalized difference vegetation index(mND705)performed well in estimation leaf chlorophyll contents of four typical karst species in term of the determination coefficient(R2 was equal to 0.45)and root mean squared error(RMSE was equal to 0.26 mg·g^-1).(2)However, most of the common spectral indices were not suitable for estimation leaf chlorophyll content of plants in karst areas. Thought the prediction capability of fluorescence ratio index(FRI1)and chlorophyll absorption area index(CAAI)were almost the same in estimation of leaf chlorophyll content of plants in karst and non-karst areas, their accuracy of prediction was relative low according to the determination coefficient.(3)The spectral indices proposed in this study performed well in estimation leaf chlorophyll content of plants in karst areas either based on the raw reflectance spectra or their first-order derivative spectra compared against others common spectral indexes, especially for the difference spectral index based on the first-order derivative spectra dD(760, 769)]. Its determination coefficient was 0.71 and the root mean squared error was 0.19 mg·g^-1. We, therefore proposed that the difference spectral index based on the first-order derivative spectra dD(760, 769)] can be used for estimation leaf chlorophyll content of plants in karst areas. Our results indicated that leaf chlorophyll content of plants in karst areas can be quickly and quantitatively estimated using spectral index combined with hyperspectral remote sensing data. These results can also provide scientific insights for estimating plants'' growth and their adaptation to environmental stress.