Leaf area index retrieval of orchards based on airborne MASTER data

Abstract

Leaf area index( LAI) is an important parameter for decrypting canopy structure,and the accurate acquisition of orchards LAI plays an important role in monitoring the growing condition and estimating the yield of orchards. In this paper,the orchard blocks in the middle of California in USA were chosen as the study area,and LAI was retrieved using normalized difference water index( NDWI) through comparing regression models with surveyed leaf area indices and three vegetation indices composed of normalized difference vegetation index( NDVI),normalized difference infrared index( NDII) and NDWI based on the MODIS /ASTER airborne simulator( MASTER) image,which acquired flying along the solar plane. The results show that the image acquired by flying perpendicular to the solar plane has the phenomenon of maximum brightness gradients because of the bidirectional reflectance of surface object,whereas the image acquired by flying along the solar plane fails to show such a phenomenon. The comparison between three vegetation index models also shows that NDVI is easy to reach saturation in higher coverage area,and NDWI is more suitable for LAI retrieval in the study area because NDWI model has higher R2 and smaller RMSE. The results of this study can enrich the LAI retrieval theory and provide theoretical and data support for LAI scale problem.