应用高光谱植被指数反演冬小麦叶绿素含量的光谱指标敏感性研究

Spectral Index Sensitivity Study of Winter Wheat Chlorophyll Inversion Using Hyperspectral Remote Sensing Vegetation Index

高光谱植被指数反演叶绿素含量的精度除与模型有关外,光谱指标中心波长、波段宽度、信噪比等的差异也会带来一定的影响。研究基于实测光谱数据,结合波段模拟、噪音分析等方法,研究不同的光谱指标对植被指数反演叶绿素含量的影响,分析用于反演的光谱指标的敏感性,结果表明:1最佳中心波长的位置与适用于高低覆盖的植被指数类型有关,反演精度在一定范围内并不随着波段宽度的增加而提高;2不同植被指数抗噪声能力有一定的差异,其中DVI(difference vegetation index),NDVI(normalized difference vegetation index)等抗噪能力比较强,MCARI(modified chlorophyll absorption ratio index)和TCARI(transformed chlorophyll absorption ratio index)抗噪能力比较弱;3联合反演模型反演结果为R^2=0.741 5,RMSE=0.402 6,优于MTCI(MERIS terrestrial chlorophyll index)的反演结果,通过模拟HJ1A-HSI,Hyperion等数据,研究出联合反演模型在不同高光谱传感器下有一定的适用性。

Chlorophyll is an important parameter to monitor the stresses and health status of vegetation. Currently the hyperspectral vegetation index（ VI） is one of the methods that have been widely applied to estimate the chlorophyll content inversion of wheat. The remote sensing inversion accuracy of chlorophyll content is related not only to the model,but also to the uncertainty resulting from the differences of central wavelength,band width,signal to noise ratio（ S / N） etc. The influence of central wavelength,band width,and signal to noise ratio（ S / N） was studied to chlorophyll inversion based on simulated intelligent observation mode. And the research analyzed the validity and applicability of the spectral index by the methods such as digital simulation,noise analysis,etc. The results show that：（ i） The best position of central wavelength was related to vegetation index types which were suitable for high and low vegetation coverage,and the precision of inversion results wasn＇t improved with the increase of band width. Meantime,the differences of ability that vegetation indices resisted the influence of noise were discovered.（ ii） The inversion resulted from the combination of NDVI_（705） and MCARI / OSAVI（ R2= 0. 741 5,RMSE = 0. 402 6showed a higher precision than only one vegetation index（ R2= 0. 615 3,RMSE = 0. 936） involved;（ iii） the united multiple VIs model had certain applicability in different hyperspectral sensors by simulating HJ1A-HIS,Hyperion data.