作者:You, DQ (You, Dongqin)[ 1,2,3 ] ; Wen, JG (Wen, Jianguang)[ 1,2 ] ; Xiao, Q (Xiao, Qing)[ 1 ] ; Liu, Q (Liu, Qiang)[ 4 ] ; Liu, QH (Liu, Qinhuo)[ 1,2 ] ; Tang, Y (Tang, Yong)[ 1 ] ; Dou, BC (Dou, Baocheng)[ 1 ] ; Peng, JJ (Peng, Jingjing)[ 1 ]

REMOTE SENSING

卷: 7  期: 6  页: 6784-6807

DOI: 10.3390/rs70606784

出版年: JUN 2015

摘要

A land-cover-based linear BRDF (bi-directional reflectance distribution function) unmixing (LLBU) algorithm based on the kernel-driven model is proposed to combine the compact airborne spectrographic imager (CASI) reflectance with the moderate resolution imaging spectroradiometer (MODIS) daily reflectance product to derive the BRDF/albedo of the two sensors simultaneously in the foci experimental area (FEA) of the Heihe Watershed Allied Telemetry Experimental Research (HiWATER), which was carried out in the Heihe River basin, China. For each land cover type, an archetypal BRDF, which characterizes the shape of its anisotropic reflectance, is extracted by linearly unmixing from the MODIS reflectance with the assistance of a high-resolution classification map. The isotropic coefficients accounting for the differences within a class are derived from the CASI reflectance. The BRDF is finally determined by the archetypal BRDF and the corresponding isotropic coefficients. Direct comparisons of the cropland archetypal BRDF and CASI albedo with in situ measurements show good agreement. An indirect validation which compares retrieved BRDF/albedo with that of the MCD43A1 standard product issued by NASA and aggregated CASI albedo also suggests reasonable reliability. LLBU has potential to retrieve the high spatial resolution BRDF/albedo product for airborne and spaceborne sensors which have inadequate angular samplings. In addition, it can shorten the timescale for coarse spatial resolution product like MODIS.

通讯作者地址: Wen, JG (通讯作者)

       Chinese Acad Sci, Inst Remote Sensing & Digital Earth, State Key Lab Remote Sensing Sci, POB 9718, 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

       [ 4 ] Beijing Normal Univ, Coll Global Change & Earth Syst Sci, Beijing 100875, Peoples R China