气候变化领域集成服务门户(CCPortal): Quantification of Alpine Grassland Fractional Vegetation Cover Retrieval Uncertainty Based on Multiscale Remote Sensing Data

CCPortal

DOI	10.1109/LGRS.2021.3109725
	Quantification of Alpine Grassland Fractional Vegetation Cover Retrieval Uncertainty Based on Multiscale Remote Sensing Data
	Lin, Xingchen; Chen, Jianjun; Lou, Peiqing; Yi, Shuhua; Zhou, Guoqing; You, Haotian; Han, Xiaowen
通讯作者	Chen, JJ (通讯作者)，Guilin Univ Technol, Coll Geomat & Geoinformat, Guangxi Key Lab Spatial Informat & Geomat, Guilin 541004, Peoples R China.
发表日期	2022
ISSN	1545-598X
EISSN	1558-0571
卷号	19
英文摘要	Fractional vegetation cover (FVC) retrieval results of high spatial resolution satellite remote sensing images are usually upscaled as training and validation data (FVCUIH) for low spatial resolution satellite remote sensing images. However, few studies have focused on the impact of the spatial scale conversion on the evaluation of FVC retrieval accuracy. In this study, we first investigated the influence of spatial scale conversion on FVC retrieval accuracy based on FVC measured by unmanned aerial vehicle (FVCUAV) at three scales (Sentinel-2 MSI, Landsat-8 OLI, and MODIS). Then, the NDVI threshold method is proposed to further analyze the uncertainty caused by the underlying surface heterogeneity. The results showed that the use of FVCUIH as training and validation data in the process of spatial scale conversion led to overestimation of FVC accuracy, and its influence on FVC retrieval cannot be ignored. In addition, the uncertainty of the underlying surface heterogeneity at the measured sites increased the uncertainty of the FVC retrieval, while these results could be optimized by detecting the underlying surface heterogeneity. Our results suggested that both spatial scale conversion and underlying surface heterogeneity would cause the inaccurate FVC retrieval, while the latter could be optimized by detecting the underlying surface heterogeneity. This study provided a reference for the improvement of multiscale FVC retrieval accuracy based on single-scale FVC-measured data.
关键词	SOIL INDEX REGRESSION NDVI
英文关键词	Remote sensing; Earth; Artificial satellites; MODIS; Vegetation mapping; Spatial resolution; Unmanned aerial vehicles; Fractional vegetation cover (FVC); multiscale; spatial scale conversion; underlying surface heterogeneity; unmanned aerial vehicle (UAV)
语种	英语
WOS研究方向	Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology
WOS类目	Geochemistry & Geophysics ; Engineering, Electrical & Electronic ; Remote Sensing ; Imaging Science & Photographic Technology
WOS记录号	WOS:000730789400149
来源期刊	IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
来源机构	中国科学院西北生态环境资源研究院
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/254839
作者单位	[Lin, Xingchen; Chen, Jianjun; Zhou, Guoqing; You, Haotian; Han, Xiaowen] Guilin Univ Technol, Coll Geomat & Geoinformat, Guangxi Key Lab Spatial Informat & Geomat, Guilin 541004, Peoples R China; [Lou, Peiqing] Chinese Acad Sci, Northwest Inst Ecoenvironm & Resource, State Key Lab Cryospher Sci, Cryosphere Res Stn Qinghai Tibet Plateau, Lanzhou 730000, Peoples R China; [Lou, Peiqing] Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China; [Yi, Shuhua] Nantong Univ, Sch Geog Sci, Inst Fragile Ecoenvironm, Nantong 226007, Peoples R China
推荐引用方式 GB/T 7714	Lin, Xingchen,Chen, Jianjun,Lou, Peiqing,et al. Quantification of Alpine Grassland Fractional Vegetation Cover Retrieval Uncertainty Based on Multiscale Remote Sensing Data[J]. 中国科学院西北生态环境资源研究院,2022,19.
APA	Lin, Xingchen.,Chen, Jianjun.,Lou, Peiqing.,Yi, Shuhua.,Zhou, Guoqing.,...&Han, Xiaowen.(2022).Quantification of Alpine Grassland Fractional Vegetation Cover Retrieval Uncertainty Based on Multiscale Remote Sensing Data.IEEE GEOSCIENCE AND REMOTE SENSING LETTERS,19.
MLA	Lin, Xingchen,et al."Quantification of Alpine Grassland Fractional Vegetation Cover Retrieval Uncertainty Based on Multiscale Remote Sensing Data".IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 19(2022).