Land surface leaf area index estimation based on time series multi-angular remote sensing data

Abstract

Time series leaf area index (LAI) derived from remote sensing data is a key parameter for environment researches especially on dynamic changes of land surface. In this study, a new approach was developed to retrieve LAI from time series Moderate Resolution Imaging Spectroradiometer (MODIS) multi-angular remote sensing data. Based on radiative transfer theory, we used Ross Thick-Li Sparse Reciprocal (RTLSR) kernel driven model to generate specific directional BRFs and corresponding anisotropy information, employed Scattering by Arbitrarily Inclined Leaves with Hotspot (SAILH) model to fill in missing data and Data-Based Mechanistic modeling (DBM) procedure to model and estimate time series vegetation LAI. The preliminary results indicated that the LAIs derived in this study have good agreement with ground LAI measurements and their continuity of the time series are superior to MODIS LAI product.