光谱指数的植物叶片叶绿素含量估算模型

摘要

叶片叶绿素能够有效监测植被的生长状况,利用光谱指数反演植被叶绿素含量是目前的通用方法.实测了盐生植物光谱反射率和叶片叶绿素含量.对SPAD值进行变换,对比Pearson与VIP方法探讨盐生植被叶片叶绿素含量与植被指数的相关性并进行精度验证,从中选出最佳拟合模型.研究表明,通过对Pearson与 VIP相关性分析,最终选定 VIP方法建立植被指数的叶片叶绿素估算模型,NDVI705,ARVI, CIred edge,PRI,VARI,PSRI和NPCI的VIP值均大于0.8,因此选定这七个植被指数为最优植被指数;预测结果显示,所有模型的相关性都在0.7以上,预测值与实测值相关性最好的是经过倒数变换的SPAD值,R=0.816,RMSE=0.007.基于VIP方法的反演模型能较好地估算研究区植被叶绿素含量,该方法为植物叶绿素含量诊断的实际应用提供了重要的理论依据和技术支持.%The chlorophyll can effectively monitoring vegetation growth status,currently the hyperspectral vegetation index(VI) is one of the common methods that have been widely applied to estimate the leaf chlorophyll content(LCC)inversion.Non-de-structive rapid estimation of chlorophyll using hyperspectral remote sensing technology is a prerequisite to dynamically monitor chlorophyll content,and it is an important research issue of vegetation remote sensing.The author measured the leaf spectral re-flectance and chlorophyll relative content of desert plants,analyzed the spectral curves of different desert plants under the same chlorophyll content,then transformed the SPAD value,compared Pearson and VIP methods to study the correlation between chlorophyll content and vegetation index of desert vegetation.Finally,the author selected the best fitting model from accuracy test.The results showed that:Based on the comparative analysis between Pearson and VIP,established the chlorophyll content estimation model by VIP method,selected 7 vegetation indices,which was NDVI705,ARVI,CIred edge,PRI,VARI,PSRI and NPCI respectively,the value of VIP all greater than 0.8,thus these 7 vegetation indices were the optimal vegetation indices. The prediction results indicated that the correlation of all models was more than 0.7,the best correlation between the predicted value and the measured value was the SPAD value of the reciprocal transformation,R=0.816,RMSE=0.007.The inversion model based on VIP method can estimate the chlorophyll content of vegetation in the study area,it provides an important theo-retical basis and technical support for the practical application in the diagnosis of plant chlorophyll content.