1. Institute of Remote Sensing and GIS, Peking University, Beijing 100871, China 2. Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China 3. Harbin Institute of Technology Shenzhen Graduate School, Shenzhen 518055, China
摘要： 目前光谱指数方法已被广泛地应用于植被叶绿素含量的反演中，考虑到不同传感器的光谱响应存在差异，研究了光谱尺度效应对光谱指数反演植被叶片叶绿素含量的影响。基于PROSPECT模型模拟了不同叶绿素含量(5～80 μg·cm-2)下的5 nm叶片光谱反射率数据，并利用高斯光谱响应函数将其分别模拟成10～35 nm六种波段宽的光谱数据，再分析评价5～35 nm波段宽下光谱指数与叶片叶绿素含量的相关性、对叶片叶绿素含量变化及对波段宽变化的敏感性。最后，利用波段宽为40～65 nm的反射率数据对光谱指数反演植被叶绿素含量的光谱尺度效应进行验证。结果表明，通用光谱指数(vegetation index based on universal pattern decomposition method, VIUPD)反演叶绿素含量的精度最高，反演值与真实值拟合程度最好;归一化差值植被指数(normalized difference vegetation index, NDVI)和简单比值指数(simple ratio index, SRI)其次，虽然其决定系数R2高达0.89以上，但反演的叶绿素含量值小于真实值;其他光谱指数的反演结果较差。VIUPD对叶绿素含量具有较好的相关性和敏感性，受光谱尺度效应影响较小，具有较好的反演能力，这一结论恰好验证了其“独立于传感器”的特性，同时证明了VIUPD在多源遥感数据反演植被理化参量的研究中具有更好的应用前景。
关键词：光谱指数;植被叶片叶绿素含量;辐射传输;光谱响应;尺度效应
Abstract：Spectral indices (SIs) method has been widely applied in the prediction of vegetation biochemical parameters. Take the diversity of spectral response of different sensors into consideration, this study aimed at researching spectral scale effect of SIs for estimating vegetation chlorophyll content (VCC). The 5 nm leaf reflectance data under 16 levels of chlorophyll content was got by the radiation transfer model PROSPECT and then simulated to multiple bandwidths spectrum (10～35 nm), using Gaussian spectral response function. Firstly, the correlation between SIs and VCC was studied. And then the sensitivity of SIs to VCC and bandwidth were analyzed and compared. Lastly, 112 samples were selected to verify the results above mentioned. The results show that Vegetation Index Based on Universal Pattern Decomposition Method (VIUPD) is the best spectral index due to its high sensitivity to VCC but low sensitivity to bandwidth, and can be successfully used to estimate VCC with coefficient of determination R2 of 0.99 and RMSE of 3.52 μg·cm-2. Followed by VIUPD, Normalized Difference Vegetation Index (NDVI) and Simple Ratio Index (SRI) presented a comparatively good performance for VCC estimation (R2>0.89) with their prediction value of chlorophyll content was lower than the true value. The worse accuracy of other indices were also tested. Results demonstrate that spectral scale effect must be well-considered when estimating chlorophyll content, using SIs method. VIUPD introduced in the present study has the best performance, which reaffirms its special feature of comparatively sensor-independent and illustrates its potential ability in the area of estimating vegetation biochemical parameters based on multiple satellite data.
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