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, Ronggao8; Chen, Jing M.1,6,7; Shang, Rong1; Liu, Yang8; Qi, Lin8; Croft, Holly5; Ju, Weimin4; Zhang, Yongguang4; He, Yuhong3 |
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 |
DOI | 10.1016/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.Zhejiang A&F Univ, Coll Environm & Resource Sci, Hangzhou 311300, Peoples R China 3.Univ Toronto Mississauga, Dept Geog Geomat & Environm, Mississauga, ON L5L 1C6, Canada 4.Nanjing Univ, Int Inst Earth Syst Sci, Nanjing 210023, Peoples R China 5.Univ Sheffield, Sch Biosci, Sheffield S10 2TN, England 6.Univ Toronto, Program Planning, Toronto, ON M5S 3G3, Canada 7.Univ Toronto, Dept Geog, Toronto, ON M5S 3G3, Canada 8.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, 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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