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A 21-Year Time Series of Global Leaf Chlorophyll Content Maps From MODIS Imagery

文献类型：期刊论文

作者Xu, Mingzhu7; Liu, Ronggao3; Chen, Jing M.7; Liu, Yang3; Wolanin, Aleksandra4; Croft, Holly5; He, Liming1; Shang, Rong3; Ju, Weimin2; Zhang, Yongguang2
刊名IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
出版日期2022
卷号60页码:13
关键词Leaf area index (LAI) leaf biochemical parameter leaf physiology leaf pigment multispectral remote sensing
ISSN号0196-2892
DOI10.1109/TGRS.2022.3204185
通讯作者Liu, Ronggao(liurg@igsnrr.ac.cn) ; Chen, Jing M.(jing.chen@utoronto.ca)
英文摘要The leaf chlorophyll content (LCC) is an important plant physiological trait and is critical for accurate modeling of vegetation photosynthesis over time and space. To date, there is still a lack of a global long time-series dataset of LCC. In this study, we developed an algorithm to retrieve global LCC from Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance data from 2000 to 2020. An essential requirement for generating LCC time series is to capture its seasonal dynamics. This issue was addressed by using a matrix system with two pairs of vegetation indices to minimize the impacts of leaf area index and canopy nonphotosynthetic material on LCC estimation in different seasons. The matrix system algorithm was applied to Landsat data and MODIS data, respectively. The validation based on Landsat data and ground measurements reveals that the algorithm has the ability to catch the seasonal variations of LCC in different plant functional types, and the MODIS-derived LCC shows good agreement with Landsat-upscaled LCC (R-2 = 0.77 and RMSE = 6.9 mu g/cm(2)). The global eight-day LCC data at 500-m resolution in 2000-2020 were generated using the matrix system from MODIS and presented distinct temporal and spatial variations, which provides a new opportunity for analyzing vegetation physiological dynamics in climate change studies.
WOS关键词RED-EDGE BANDS ; SURFACE REFLECTANCE ; REMOTE ESTIMATION ; AREA INDEX ; VEGETATION INDEXES ; GREEN LAI ; CANOPY ; VALIDATION ; RETRIEVAL ; SENTINEL-2
资助项目Strategic Priority Research Program of the Chinese Academy of Sciences[XDA19080303] ; National Key Research and Development Program of China[2016YFA0600201] ; China Postdoctoral Science Foundation[2021M690638] ; National Natural Science Foundation of China[42101367]
WOS研究方向Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology
语种英语
WOS记录号WOS:000856251200036
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
资助机构Strategic Priority Research Program of the Chinese Academy of Sciences ; National Key Research and Development Program of China ; China Postdoctoral Science Foundation ; National Natural Science Foundation of China
源URL[http://ir.igsnrr.ac.cn/handle/311030/184934]  
专题中国科学院地理科学与资源研究所
通讯作者Liu, Ronggao; Chen, Jing M.
作者单位1.Nat Resources Canada, Canada Ctr Remote Sensing, Ottawa, ON K1A 0E4, Canada
2.Nanjing Univ, Int Inst Earth Syst Sci, Nanjing 210023, Peoples R China
3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China
4.Helmholtz Ctr Potsdam GFZ, German Res Ctr Geosci, D-14473 Potsdam, Germany
5.Univ Sheffield, Sch Biosci, Sheffield S10 2TN, S Yorkshire, England
6.Univ Toronto Mississauga, Dept Geog Geomat & Environm, Mississauga, ON L5L 1C6, Canada
7.Fujian Normal Univ, Coll Geog Sci, Key Lab Humid Subtrop Ecogeog Proc, Minist Educ, Fuzhou 350007, Peoples R China
推荐引用方式
GB/T 7714		 Xu, Mingzhu,Liu, Ronggao,Chen, Jing M.,et al. A 21-Year Time Series of Global Leaf Chlorophyll Content Maps From MODIS Imagery[J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,2022,60:13.
APA Xu, Mingzhu.,Liu, Ronggao.,Chen, Jing M..,Liu, Yang.,Wolanin, Aleksandra.,...&Wang, Rong.(2022).A 21-Year Time Series of Global Leaf Chlorophyll Content Maps From MODIS Imagery.IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,60,13.
MLA Xu, Mingzhu,et al."A 21-Year Time Series of Global Leaf Chlorophyll Content Maps From MODIS Imagery".IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 60(2022):13.

入库方式： OAI收割

来源：地理科学与资源研究所

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