作者:Yin, GF (Yin, Gaofei)[ 1,2,3 ] ; Li, J (Li, Jing)[ 1,2 ] ; Liu, QH (Liu, Qinhuo)[ 1,2 ] ; Fan, WL (Fan, Weiliang)[ 1 ] ; Xu, BD (Xu, Baodong)[ 1,3 ] ; Zeng, YL (Zeng, Yelu)[ 1,3 ] ; Zhao, J (Zhao, Jing)[ 1 ]

REMOTE SENSING

卷: 7  期: 4  页: 4604-4625

DOI: 10.3390/rs70404604

出版年: APR 2015

摘要

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 very important for LAI retrieval. If an unsuitable RT model is used, then the root mean squared error (RMSE) will increase from 0.43 to 0.60 in croplands and from 0.52 to 0.63 in forests. In addition, an RT model's potential to retrieve LAI is limited by the availability of a priori information on RT model parameters. 3D RT models require more a priori information, which makes them have poorer generalization capability than 1D models. Therefore, physically-based retrieval algorithms should embed more than one RT model to account for the availability of a priori information and variations in structural attributes among different vegetation types.

通讯作者地址: Li, J (通讯作者)

Chinese Acad Sci, Inst Remote Sensing & Digital Earth, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China.

地址:

[ 1 ] Chinese Acad Sci, Inst Remote Sensing & Digital Earth, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China

[ 2 ] JCGCS, Beijing 100875, Peoples R China

[ 3 ] Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China