%0 Journal Article
%A Gaofei Yin
%A Jing Li
%A Qinhuo Liu
%A Weiliang Fan
%A Baodong Xu
%A Yelu Zeng
%A Jing Zhao
%+ State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China;;Joint Center for Global Change Studies (JCGCS), Beijing 100875, China;;College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
%T Regional Leaf Area Index Retrieval Based on Remote Sensing: The Role of Radiative Transfer Model Selection
%J Remote Sensing
%D 2015
%N 4
%V 7
%K Leaf Area Index (LAI);radiative transfer (RT) model;model selection;structural attributes;a priori information
%X Physically-based approaches for estimating Leaf Area Index (LAI) using remote sensing data rely on radiative transfer (RT) models. Currently, many RT models are freely available, but determining the appropriate RT model for LAI retrieval is still problematic. This study aims to evaluate the necessity of RT model selection for LAI retrieval and to propose a retrieval methodology using different RT models for different vegetation types. Both actual experimental observations and RT model simulations were used to conduct the evaluation. Each of them includes needleleaf forests and croplands, which have contrasting structural attributes. The scattering from arbitrarily inclined leaves (SAIL) model and the four-scale model, which are 1D and 3D RT models, respectively, were used to simulate the synthetic test datasets. The experimental test dataset was established through two field campaigns conducted in the Heihe River Basin. The results show that the realistic representation of canopy structure in RT models is ver...
%W CNKI