文献类型：期刊文献

英文题名：Comparison of Methods for Estimating Fractional Cover of Photosynthetic and Non-Photosynthetic Vegetation in the Otindag Sandy Land Using GF-1 Wide-Field View Data

作者：Li, Xiaosong[1] Zheng, Guoxiong[1] Wang, Jinying[1] Ji, Cuicui[1,2] Sun, Bin[3] Gao, Zhihai[3]

第一作者：Li, Xiaosong

通信作者：Gao, ZH[1]

机构：[1]Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Key Lab Digital Earth Sci, Beijing 100094, Peoples R China;[2]Wuhan Univ, Sch Remote Sensing & Informat Engn, Wuhan 430079, Peoples R China;[3]Chinese Acad Forestry, Inst Forest Resources Informat Tech, Beijing 100091, Peoples R China

年份：2016

卷号：8

期号：10

外文期刊名：REMOTE SENSING

收录：;EI(收录号：20172203712319);Scopus(收录号：2-s2.0-85019749569);WOS:【SCI-EXPANDED(收录号:WOS:000387357300014)】；

基金：This research was funded by the National Natural Science Foundation of China (Grant No. 41571421 and 41361091) and Program for International S&T Cooperation Projects of Ministry of S&T of China (Grant No. 2015DFR31130). Thanks to the Chinese Academy of Forestry staff, Junjun Wu, Xiangyuan Ding, Xiaolong Hu, and Bin Liu for the many enthusiastic hours of step-point data collection.

语种：英文

外文关键词：non-photosynthetic vegetation; fractional cover; multispectral data; endmember variability; bare soil; GF-1 WFV

摘要：Photosynthetic vegetation (PV) and non-photosynthetic vegetation (NPV) are important ground cover types for desertification monitoring and land management. Hyperspectral remote sensing has been proven effective for separating NPV from bare soil, but few studies determined fractional cover of PV (f(pv)) and NPV (f(npv)) using multispectral information. The purpose of this study is to evaluate several spectral unmixing approaches for retrieval of f(pv) and f(npv) in the Otindag Sandy Land using GF-1 wide-field view (WFV) data. To deal with endmember variability, pixel-invariant (Spectral Mixture Analysis, SMA) and pixel-variable (Multi-Endmember Spectral Mixture Analysis, MESMA, and Automated Monte Carlo Unmixing Analysis, AutoMCU) endmember selection approaches were applied. Observed fractional cover data from 104 field sites were used for comparison. For f(pv), all methods show statistically significant correlations with observed data, among which AutoMCU had the highest performance (R-2 = 0.49, RMSE = 0.17), followed by MESMA (R-2 = 0.48, RMSE = 0.21), and SMA (R-2 = 0.47, RMSE = 0.27). For f(npv), MESMA had the lowest performance (R-2 = 0.11, RMSE = 0.24) because of coupling effects of the NPV and bare soil endmembers, SMA overestimates f(npv) (R-2 = 0.41, RMSE = 0.20), but is significantly correlated with observed data, and AutoMCU provides the most accurate predictions of f(npv) (R-2 = 0.49, RMSE = 0.09). Thus, the AutoMCU approach is proven to be more effective than SMA and MESMA, and GF-1 WFV data are capable of distinguishing NPV from bare soil in the Otindag Sandy Land.