利用光谱指数反演植被叶绿素含量的精度及稳定性研究

农业遥感中，利用光谱指数方法反演作物叶绿素含量一直得到广泛地应用。利用PSR-3500光谱仪及SPAD-502叶绿素仪同步获取了冬小麦冠层光谱数据及对应叶片的叶绿素相对含量(SPAD值)，并利用高斯光谱响应模型将PSR获取的地面连续光谱数据重采样为多光谱Landsat-TM7及高光谱Hyperion光谱数据，然后分别计算基于两种传感器的归一化差值植被指数(normalized difference vegetation index, NDVI)、综合叶绿素光谱指数(MCARI/OSAVI，the ratio of the modified transformed chlorophyll absorption ratio index (MCARI) to optimized soil adjusted vegetation index(OSAVI))、三角形植被指数(triangle vegetation index, TVI)及通用植被指数(vegetation index based on universal pattern decomposition method, VIUPD)，再将四种光谱指数与叶绿素含量进行回归分析。结果表明，针对重采样后的TM和Hyperion两种传感器数据，VIUPD反演叶绿素含量精度(决定系数R2)最高，反演能力最稳定，这与其“不受传感器影响”的特性密不可分;MCARI/OSAVI反演精度和稳定性次之，是因为引入的OSAVI削弱了土壤背景的影响;宽波段指数NDVI和TVI对模拟TM数据有较好的反演精度，对Hyperion数据反演精度却很低，可能是因为两种指数的构成形式简单，考虑的影响因素较少。以冬小麦为例，对利用光谱指数反演植被叶绿素含量的精度和稳定性进行了研究并分析了其影响因素，经比较发现利用植被指数VIUPD进行植被叶绿素含量反演时，其精度和稳定性最好。

Research on Accuracy and Stability of Inversing Vegetation Chlorophyll Content by Spectral Index Method

Spectral index method was widely applied to the inversion of crop chlorophyll content. In the present study, PSR3500 spectrometer and SPAD-502 chlorophyll fluorometer were used to acquire the spectrum and relative chlorophyll content (SPAD value) of winter wheat leaves on May 2nd 2013 when it was at the jointing stage of winter wheat. Then the measured spectra were resampled to simulate TM multispectral data and Hyperion hyperspectral data respectively, using the Gaussian spectral response function. We chose four typical spectral indices including normalized difference vegetation index (NDVI), triangle vegetation index (TVI), the ratio of modified transformed chlorophyll absorption ratio index(MCARI) to optimized soil adjusted vegetation index(OSAVI) (MCARI/OSAVI) and vegetation index based on universal pattern decomposition (VIUPD), which were constructed with the feature bands sensitive to the vegetation chlorophyll. After calculating these spectral indices based on the resampling TM and Hyperion data, the regression equation between spectral indices and chlorophyll content was established. For TM, the result indicates that VIUPD has the best correlation with chlorophyll (R2=0.819 7) followed by NDVI (R2=0.791 8), while MCARI/OSAVI and TVI also show a good correlation with R2 higher than 0.5. For the simulated Hyperion data, VIUPD again ranks first with R2=0.817 1, followed by MCARI/OSAVI (R2=0.658 6), while NDVI and TVI show very low values with R2 less than 0.2. It was demonstrated that VIUPD has the best accuracy and stability to estimate chlorophyll of winter wheat whether using simulated TM data or Hyperion data, which reaffirms that VIUPD is comparatively sensor independent. The chlorophyll estimation accuracy and stability of MCARI/OSAVI also works well, partly because OSAVI could reduce the influence of backgrounds. Two broadband spectral indices NDVI and TVI are weak for the chlorophyll estimation of simulated Hyperion data mainly because of their dependence on few bands and the strong influence of atmosphere, solar altitude, viewing angle of sensor, background and so on. In conclusion, the stability and consistency of chlorophyll estimation is equally important to the estimation accuracy by spectral index method. VIUPD introduced in the study has the best performance to estimate winter wheat chlorophyll, which illustrates its potential ability in the area of estimating vegetation biochemical parameters.