2013•Anhui nongye kexueRequires access

Estimation Models of Leaf Area Index(LAI) Based on Remote Sensing Image of CHRIS/PROBA

Jianjun Cao, Gu ZhuJun, Jianhua Xu, Yongjuan Liu

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Abstract

The ESA-mission CHRIS-PROBA(Compact High Resolution Imaging Spectrometer onboard the Project for On-board Autonomy) was used for providing space borne imaging spectrometer and multiangular data to assess the LAI.Five spectral vegetation indices(VI) were derived from CHRIS-PROBA image,including normalized difference vegetation index(NDVI),perpendicular vegetation index(PVI),modified soil adjusted vegetation index(MSAVI),ratio vegetation index(RVI),atmospheric resistance vegetation index(ARVI).Three hundreds LAI-VI correlation models were established.The VI-LAI correlation coefficients varied greatly across vegetation,vegetation indices,as well as image angular.In all models,from the perspective of angular,the best model is 0° image,R2=0.591,RMSE=0.650,the worst model is-55° image,R2=0.551,RMSE=0.821,from the perspective vegetation types,the best model is coniferous forest,followed by the broadleaf forests,shrubs,coniferous forests and grasslands,from the types of vegetation model,exponential model is better than one regression model,from the perspective vegetation index,the best model is PVI,followed by MSAVI,NDVI,RVI,ARVI.

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The ESA-mission CHRIS-PROBA(Compact High Resolution Imaging Spectrometer onboard the Project for On-board Autonomy) was used for providing space borne imaging spectrometer and multiangular data to assess the LAI.Five spectral vegetation indices(VI) were derived from CHRIS-PROBA image,including normalized difference vegetation index(NDVI),perpendicular vegetation index(PVI),modified soil adjusted vegetation index(MSAVI),ratio vegetation index(RVI),atmospheric resistance vegetation index(ARVI).Three hundreds LAI-VI correlation models were established.The VI-LAI correlation coefficients varied greatly across vegetation,vegetation indices,as well as image angular.In all models,from the perspective of angular,the best model is 0° image,R2=0.591,RMSE=0.650,the worst model is-55° image,R2=0.551,RMSE=0.821,from the perspective vegetation types,the best model is coniferous forest,followed by the broadleaf forests,shrubs,coniferous forests and grasslands,from the types of vegetation model,exponential model is better than one regression model,from the perspective vegetation index,the best model is PVI,followed by MSAVI,NDVI,RVI,ARVI.

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Available abstract

The ESA-mission CHRIS-PROBA(Compact High Resolution Imaging Spectrometer onboard the Project for On-board Autonomy) was used for providing space borne imaging spectrometer and multiangular data to assess the LAI.Five spectral vegetation indices(VI) were derived from CHRIS-PROBA image,including normalized difference vegetation index(NDVI),perpendicular vegetation index(PVI),modified soil adjusted vegetation index(MSAVI),ratio vegetation index(RVI),atmospheric resistance vegetation index(ARVI).Three hundreds LAI-VI correlation models were established.The VI-LAI correlation coefficients varied greatly across vegetation,vegetation indices,as well as image angular.In all models,from the perspective of angular,the best model is 0° image,R2=0.591,RMSE=0.650,the worst model is-55° image,R2=0.551,RMSE=0.821,from the perspective vegetation types,the best model is coniferous forest,followed by the broadleaf forests,shrubs,coniferous forests and grasslands,from the types of vegetation model,exponential model is better than one regression model,from the perspective vegetation index,the best model is PVI,followed by MSAVI,NDVI,RVI,ARVI.

Key concepts: Normalized Difference Vegetation Index, Vegetation (pathology), Enhanced vegetation index, Leaf area index, Remote sensing, Vegetation Index, Environmental science, Mean squared error

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