Analysis of Extracting Prior BRDF from MODIS BRDF Data

作者:Zhang, H (Zhang, Hu)[ 1 ] ; Jiao, ZT (Jiao, Ziti)[ 2,3 ] ; Dong, YD (Dong, Yadong)[ 2,3 ] ; Du, P (Du, Peng)[ 1 ] ; Li, Y (Li, Yang)[ 2,3 ] ; Lian, Y (Lian, Yi)[ 1 ] ; Cui, TJ (Cui, Tiejun)[ 1 ]

REMOTE SENSING

卷: 8  期: 12

文献号: 1004

DOI: 10.3390/rs8121004

出版年: DEC 2016

摘要

Many previous studies have attempted to extract prior reflectance anisotropy knowledge from the historical MODIS Bidirectional Reflectance Distribution Function (BRDF) product based on land cover or Normalized Difference Vegetation Index (NDVI) data. In this study, the feasibility of the method is discussed based on MODIS data and archetypal BRDFs. The BRDF is simplified into six archetypal BRDFs that represent different reflectance anisotropies. Five-year time series of MODIS BRDF data over three tiles are classified into six BRDF archetype classes according to the Anisotropy Flat indeX (AFX). The percentage of each BRDF archetype class in different land cover classes or every 0.1-NDVI interval is determined. Nadir BRDF-Adjusted Reflectances (NBARs) and NDVIs simulated from different archetypal BRDFs and the same multi-angular observations are compared to MODIS results to study the effectiveness of the method. The results show that one land cover type, or every 0.1-NDVI interval, contains all the potential BRDF shapes and that one BRDF archetypal class makes up no more than 40% of all data. Moreover, the differences between the NBARs and NDVIs simulated from different archetypal BRDFs are insignificant. In terms of the archetypal BRDF method and MODIS BRDF product, this study indicates that the land cover or NDVI is not necessarily related to surface reflectance anisotropy.

通讯作者地址: Jiao, ZT (通讯作者)

Beijing Normal Univ, State Key Lab Remote Sensing Sci, Beijing 100875, Peoples R China.

通讯作者地址: Jiao, ZT (通讯作者)

Beijing Normal Univ, Sch Geog, Beijing 100875, Peoples R China.

地址:

[ 1 ] Tianjin Normal Univ, Coll Urban & Environm Sci, Tianjin 300387, Peoples R China

[ 2 ] Beijing Normal Univ, State Key Lab Remote Sensing Sci, Beijing 100875, Peoples R China

[ 3 ] Beijing Normal Univ, Sch Geog, Beijing 100875, Peoples R China