Validating GEOV1 Fractional Vegetation Cover Derived From Coarse-Resolution Remote Sensing Images Over Croplands

作者:Mu, XH (Mu, Xihan)[ 1,2 ] ; Huang, S (Huang, Shuai)[ 1,2 ] ; Ren, HZ (Ren, Huazhong)[ 3 ] ; Yan, GJ (Yan, Guangjian)[ 1,2 ] ; Song, WJ (Song, Wanjuan)[ 1,2 ] ; Ruan, GY (Ruan, Gaiyan)[ 1,2 ]

IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING

卷: 8  期: 2  页: 439-446

DOI: 10.1109/JSTARS.2014.2342257

出版年: FEB 2015

摘要

Fractional vegetation cover (FVC) is one of the most important criteria for surface vegetation status. This criterion corresponds to the complement of gap fraction unity at the nadir direction and accounts for the amount of horizontal vegetation distribution. This study aims to directly validate the accuracy of FVC products over crops at coarse resolutions (1 km) by employing field measurements and high-resolution data. The study area was within an oasis in the Heihe Basin, Northwest China, where the Heihe Watershed Allied Telemetry Experimental Research was conducted. Reference FVC was generated through upscaling, which fitted field-measured data with spaceborne and airborne data to retrieve high-resolution FVC, and then high-resolution FVC was aggregated with a coarse scale. The fraction of green vegetation cover product (i.e., GEOV1 FVC) of SPOT/VEGETATION data taken during the GEOLAND2 project was compared with reference data. GEOV1 FVC was generally overestimated for crops in the study area compared with our estimates. Reference FVC exhibits a systematic uncertainty, and GEOV1 can overestimate FVC by up to 0.20. This finding indicates the necessity of reanalyzing and improving GEOV1 FVC over croplands.

通讯作者地址: Mu, XH (通讯作者)

Beijing Normal Univ, State Key Lab Remote Sensing Sci, Res Ctr Remote Sensing & GIS, Beijing 100875, Peoples R China.

地址:

[ 1 ] Beijing Normal Univ, State Key Lab Remote Sensing Sci, Res Ctr Remote Sensing & GIS, Beijing 100875, Peoples R China

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

[ 3 ] Peking Univ, Inst Remote Sensing & Geog Informat Syst, Beijing 100871, Peoples R China