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Retrieving global leaf chlorophyll content from MERIS data using a neural network method

文献类型：期刊论文

作者Xu, Mingzhu1; Liu, Ronggao2; Chen, Jing M.1,3,4; Shang, Rong1; Liu, Yang2; Qi, Lin2; Croft, Holly5; Ju, Weimin6; Zhang, Yongguang6; He, Yuhong7
刊名ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
出版日期2022-10-01
卷号192页码:66-82
ISSN号0924-2716
关键词Global mapping Machine learning Leaf pigment Leaf biochemical parameter Phenology
DOI10.1016/j.isprsjprs.2022.08.003
通讯作者Liu, Ronggao(liurg@igsnrr.ac.cn) ; Chen, Jing M.(jing.chen@utoronto.ca)
英文摘要Leaf chlorophyll content (LCC) is an indicator of plant physiological function and is an important parameter in estimating the carbon and water fluxes of terrestrial ecosystems. Spatiotemporally continuous LCC products are therefore needed at scales from the site level to the globe. In this study, we developed a neural network model for LCC retrieval from ENVISAT MERIS data based on radiative transfer model simulations. By considering the influence of canopy non-photosynthetic materials and the co-variations between LCC and biophysical parameters, a synthetic database was generated using the PROSAIL model with a good approximation to the canopy reflectance collection of MERIS data. Using a neural network trained from the synthetic database, we derived more realistic seasonal patterns of LCC than those using neural network models trained from synthetic databases generated without considering the influence of canopy non-photosynthetic materials or the parameter co -variations. A new global LCC product (GLOBMAP MERIS LCC) at 300-m resolution in 2003-2012 was generated using the neural network. It shows an improvement over the previous MERIS LCC product in capturing LCC seasonal variations in different plant functional types, and is potentially useful in improving the integration of physiological information within terrestrial ecosystem modeling and ecological monitoring across a range of spatial and temporal scales.
WOS关键词GROSS PRIMARY PRODUCTION ; REMOTE ESTIMATION ; AREA INDEX ; BIOPHYSICAL VARIABLES ; OPTICAL-PROPERTIES ; GREEN LAI ; MODIS-LAI ; CANOPY ; VEGETATION ; REFLECTANCE
资助项目Strategic Priority Research Program of the Chinese Academy of Sciences[XDA19080303] ; China Post- doctoral Science Foundation[2021M690638] ; National Natural Science Foundation of China[42101367] ; National Key Research and Development Program of China[2016YFA0600201]
WOS研究方向Physical Geography ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
语种英语
出版者ELSEVIER
WOS记录号WOS:000848879100001
资助机构Strategic Priority Research Program of the Chinese Academy of Sciences ; China Post- doctoral Science Foundation ; National Natural Science Foundation of China ; National Key Research and Development Program of China
源URL[http://ir.igsnrr.ac.cn/handle/311030/182301]  
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.Fujian Normal Univ, Coll Geog Sci, Key Lab Humid Subtrop Ecogeog Proc, Minist Educ, Fuzhou 350007, Peoples R China
2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China
3.Univ Toronto, Dept Geog, Toronto, ON M5S 3G3, Canada
4.Univ Toronto, Program Planning, Toronto, ON M5S 3G3, Canada
5.Univ Sheffield, Sch Biosci, Sheffield S10 2TN, England
6.Nanjing Univ, Int Inst Earth Syst Sci, Nanjing 210023, Peoples R China
7.Univ Toronto Mississauga, Dept Geog Geomat & Environm, Mississauga, ON L5L 1C6, Canada
8.Zhejiang A&F Univ, Coll Environm & Resource Sci, Hangzhou 311300, Peoples R China
推荐引用方式
GB/T 7714		 Xu, Mingzhu,Liu, Ronggao,Chen, Jing M.,et al. Retrieving global leaf chlorophyll content from MERIS data using a neural network method[J]. ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING,2022,192:66-82.
APA Xu, Mingzhu.,Liu, Ronggao.,Chen, Jing M..,Shang, Rong.,Liu, Yang.,...&Lin, Qinan.(2022).Retrieving global leaf chlorophyll content from MERIS data using a neural network method.ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING,192,66-82.
MLA Xu, Mingzhu,et al."Retrieving global leaf chlorophyll content from MERIS data using a neural network method".ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 192(2022):66-82.

入库方式： OAI收割

来源：地理科学与资源研究所

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