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

	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
	Li, Xiaosong 1; Zheng, Guoxiong 1; Wang, Jinying 1; Ji, Cuicui 1; Sun, Bin 1; Gao, Zhihai 1
刊名	REMOTE SENSING
	2016
卷号	8 期号:10
关键词	DUAL-POL SAR TARGET DETECTION DETECTION PERFORMANCE RELATIVE PHASE NOTCH FILTER POLARIZATION HYBRID/COMPACT SYMMETRY OCEAN
通讯作者	Gao, ZH (reprint author), Chinese Acad Forestry, Inst Forest Resources Informat Tech, Beijing 100091, Peoples R China.
英文摘要	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.
学科主题	Remote Sensing
类目[WOS]	Remote Sensing
收录类别	SCI
语种	英语
WOS记录号	WOS:000387357300014
内容类型	期刊论文
源URL	[http://ir.radi.ac.cn/handle/183411/39215]
专题	遥感与数字地球研究所_SCI/EI期刊论文_期刊论文
作者单位	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
推荐引用方式 GB/T 7714	Li, Xiaosong,Zheng, Guoxiong,Wang, Jinying,et al. 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[J]. REMOTE SENSING,2016,8(10).
APA	Li, Xiaosong,Zheng, Guoxiong,Wang, Jinying,Ji, Cuicui,Sun, Bin,&Gao, Zhihai.(2016).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.REMOTE SENSING,8(10).
MLA	Li, Xiaosong,et al."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".REMOTE SENSING 8.10(2016).