Fractional vegetation cover estimation by using multi-angle vegetation index

作者:Mu, XH (Mu, Xihan)[ 1,2,3 ] ; Song, WJ (Song, Wanjuan)[ 1,2 ] ; Gao, Z (Gao, Zhan)[ 1,2 ] ; McVicar, TR (McVicar, Tim R.)[ 3 ] ; Donohue, RJ (Donohue, Randall J.)[ 3 ] ; Yan, GJ (Yan, Guangjian)[ 1,2 ]

REMOTE SENSING OF ENVIRONMENT

卷: 216  页: 44-56

DOI: 10.1016/j.rse.2018.06.022

出版年:OCT 2018

摘要

The vegetation index-based (VI-based) mixture model is widely used to derive green fractional vegetation cover (FVC) from remotely sensed data. Two critical parameters of the model are the vegetation index values of fully-vegetated and bare soil pixels (denoted V-x and V-n hereafter). These are commonly empirically set according to spatial and/or temporal statistics. The uncertainty and difficulty of accurately determining V-x and V-n in many ecosystems limits the accuracy of resultant FVC estimates and hence reduces the utility of VI-based mixture model for FVC estimation. Here, an improved method called MultiVI is developed to quantitatively estimate V-x and V-n from angular VI acquired at two viewing angles. The directional VI is calculated from the MODIS Bidirectional Reflectance Distribution Function (BRDF)/Albedo product (MCD43A1) data. The results of simulated evaluation with 10% added noise show that the root mean square deviation (RMSD) of FVC is approximately 0.1 (the valid FVC range is [0, 1]). Direct evaluation against 34 globally-distributed FVC measurements from VAlidation of Land European Remote sensing Instruments (VALERI) sites during 2000 to 2014 demonstrated that the accuracy of MultiVI FVC (R-2 = 0.866, RMSD = 0.092) exceeds than from SPOT/VEGETATION bioGEOphysical product version 1 (GEOV1) FVC (R-2 = 0.795, RMSD = 0.159). MultiVI FVC also exhibits higher correlation to the VALERI reference FVC than does the MODIS fraction of photosynthetically active radiation product (MCD15A2H; R-2 is 0.696). A key advantage of the MultiVI method is obvious in areas where fully-vegetated and/or bare soil pixels do not exist in moderate-coarse spatial resolution imagery when compared to the conventional VI-based mixture modelling. The MultiVI method can be flexibly implemented over regional or global scales to monitor FVC, with maps of V-x and V-n generated as two important byproducts.

作者信息

通讯作者地址: Song, WJ (通讯作者)

Beijing Normal Univ, Fac Geog Sci, Inst Remote Sensing Sci & Engn, State Key Lab Remote Sensing Sci, Beijing 100875, Peoples R China.

地址:

[ 1 ] Chinese Acad Sci, State Key Lab Remote Sensing Sci, Beijing, Peoples R China

[ 2 ] Beijing Normal Univ, Inst Remote Sensing Sci & Engn, Beijing Engn Res Ctr Global Land Remote Sensing P, Fac Geog Sci, Beijing 100875, Peoples R China

[ 3 ] CSIRO Land & Water, GPO Box 1700, Canberra, ACT 2601, Australia