干旱区生态环境知识资源中心(Arid): Comparison of Different Multispectral Sensors for Photosynthetic and Non-Photosynthetic Vegetation-Fraction Retrieval

Arid

DOI	10.3390/rs12010115
	Comparison of Different Multispectral Sensors for Photosynthetic and Non-Photosynthetic Vegetation-Fraction Retrieval
	Ji, Cuicui 1,2; Li, Xiaosong 1; Wei, Huaidong 3,4; Li, Sike 5
通讯作者	Li, Xiaosong
来源期刊	REMOTE SENSING
EISSN	2072-4292
出版年	2020
卷号	12 期号:1
英文摘要	It is very difficult and complex to acquire photosynthetic vegetation (PV) and non-PV (NPV) fractions (f(PV) and f(NPV)) using multispectral satellite sensors because estimations of f(PV) and f(NPV) are influenced by many factors, such as background-noise interference of pixel-, spatial-, and spectral-scale effects. In this study, comparisons between Sentinel-2A Multispectral Instrument (S2 MSI), Landsat-8 Operational Land Imager (L8 OLI), and GF1 Wide Field View (GF1 WFV) sensors for retrieving sparse photosynthetic and non-photosynthetic vegetation coverage are presented. The analysis employed a linear spectral-mixture model (LSMM) and nonlinear spectral-mixture model (NSMM) to unmix pixels with different spectral and spatial resolution images based on field endmembers; the estimated endmember fractions were later validated with reference to fraction measurements. The results demonstrated that: (1) with higher spatial and spectral resolution, the S2 MSI sensor had a clear advantage for retrieving PV and NPV fractions compared to L8 OLI and GF1 WFV sensors; (2) through incorporating more red edge (RE) and near-infrared (NIR) bands, the accuracy of NPV fraction estimation could be greatly improved; (3) nonlinear spectral mixing effects were not obvious on the 10-30 m spatial scale for desert vegetation; (4) in arid regions, a shadow endmember is a significant factor for sparse vegetation coverage estimated with remote-sensing data. The estimated NPV fractions were especially affected by the shadow effects and could increase root mean square by 50%. The utilized approaches in the study could effectively assess the performance of major multispectral sensors to extract f(PV) and f(NPV) through the novel method of spectral-mixture analysis.
英文关键词	Sentinel-2A MSI GF1 WFV Landsat-8 OLI photosynthetic vegetation non-photosynthetic vegetation linear and nonlinear spectral-mixture analysis
类型	Article
语种	英语
国家	Peoples R China ; Australia
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:000515391700115
WOS关键词	SPECTRAL MIXTURE ANALYSIS ; HYPERSPECTRAL DATA ; NONLINEAR ESTIMATION ; SPATIAL-RESOLUTION ; EO-1 HYPERION ; COVER ; SOIL ; INDEXES ; REFLECTANCE ; FOREST
WOS类目	Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS研究方向	Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/315403
作者单位	1.Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Key Lab Digital Earth Sci, Beijing 100094, Peoples R China; 2.Chongqing Jiaotong Univ, Sch Civil Engn, Chongqing 400074, Peoples R China; 3.Gansu Desert Control Res Inst, State Key Lab Desertificat & Aeolian Sand Disaste, Lanzhou 730070, Peoples R China; 4.Northwest Normal Univ, Sch Geog & Environm Sci, Lanzhou 730070, Peoples R China; 5.Monash Univ, Sci Fac, Earth Atmosphere & Environm, Clayton, Vic 3800, Australia
推荐引用方式 GB/T 7714	Ji, Cuicui,Li, Xiaosong,Wei, Huaidong,et al. Comparison of Different Multispectral Sensors for Photosynthetic and Non-Photosynthetic Vegetation-Fraction Retrieval[J],2020,12(1).
APA	Ji, Cuicui,Li, Xiaosong,Wei, Huaidong,&Li, Sike.(2020).Comparison of Different Multispectral Sensors for Photosynthetic and Non-Photosynthetic Vegetation-Fraction Retrieval.REMOTE SENSING,12(1).
MLA	Ji, Cuicui,et al."Comparison of Different Multispectral Sensors for Photosynthetic and Non-Photosynthetic Vegetation-Fraction Retrieval".REMOTE SENSING 12.1(2020).