A Radiative Transfer Model for Heterogeneous Agro-Forestry Scenarios

作者:Zeng, YL (Zeng, Yelu)[ 1,2,3 ] ; Li, J (Li, Jing)[ 1,4,5 ] ; Liu, QH (Liu, Qinhuo)[ 1,4,5 ] ; Huete, AR (Huete, Alfredo R.)[ 6 ] ; Yin, GF (Yin, Gaofei)[ 7 ] ; Xu, BD (Xu, Baodong)[ 1,2,3 ] ; Fan, WL (Fan, Weiliang)[ 8 ] ; Zhao, J (Zhao, Jing)[ 1,4,5 ] ; Yan, K (Yan, Kai)[ 1,4,5 ] ; Mu, XH (Mu, Xihan)[ 1,4,5 ]

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING

卷: 54  期: 8  頁: 4613-4628

DOI: 10.1109/TGRS.2016.2547326

出版年: AUG 2016

摘要

Landscape heterogeneity is a common natural phenomenon but is seldom considered in current radiative transfer (RT) models for predicting the surface reflectance. This paper developed an analytical RT model for heterogeneous Agro-Forestry scenarios (RTAF) by dividing the scenario into nonboundary regions (NRs) and boundary regions (BRs). The scattering contribution of the NRs can be estimated from the scattering-by-arbitrarily-inclined-leaves-with-the-hot-spot-effect model as homogeneous canopies, whereas that of the BRs is calculated based on the bidirectional gap probability by considering the interactions and mutual shadowing effects among different patches. The multiangular airborne observations and discrete-anisotropic-RT model simulations were used to validate and evaluate the RTAF model over an agro-forestry scenario in the Heihe River Basin, China. The results suggest that the RTAF model can accurately simulate the hemispherical-directional reflectance factors (HDRFs) of the heterogeneous scenarios in the red and near-infrared (NIR) bands. The boundary effect can significantly influence the angular distribution of the HDRFs and consequently enlarge the HDRF variations between the backward and forward directions. Compared with the widely used dominant cover type (DCT) and spectral linear mixture (SLM) models, the RTAF model reduced the maximum relative error from 25.7% (SLM) and 23.0% (DCT) to 9.8% in the red band and from 19.6% (DCT) and 13.7% (SLM) to 8.7% in the NIR band. The RTAF model provides a promising way to improve the retrieval of biophysical parameters (e.g., leaf area index) from remote sensing data over heterogeneous agro-forestry scenarios.

通訊作者地址: Li, J; Liu, QH (通訊作者)

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

通訊作者地址: Li, J; Liu, QH (通訊作者)

Beijing Normal Univ, Beijing 100875, 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 ] Joint Ctr Global Change Studies, Beijing 100875, Peoples R China

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

[ 4 ] Beijing Normal Univ, Beijing 100875, Peoples R China

[ 5 ] Joint Ctr Global Change Studies, Beijing 100875, Peoples R China

[ 6 ] Univ Technol Sydney, Plant Funct Biol & Climate Change Cluster C3, Sydney, NSW 2007, Australia

[ 7 ] Chinese Acad Sci, Inst Mt Hazards & Environm, Chengdu 610041, Peoples R China

[ 8 ] Zhejiang A&F Univ, Sch Environm & Resources Sci, Linan 311300, Peoples R China