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LIBRIS Formathandbok  (Information om MARC21)
FältnamnIndikatorerMetadata
00006202naa a2200733 4500
001oai:lup.lub.lu.se:d73171d4-11b1-4b85-a2dc-6225e5df3187
003SwePub
008170510s2017 | |||||||||||000 ||eng|
0247 a https://lup.lub.lu.se/record/d73171d4-11b1-4b85-a2dc-6225e5df31872 URI
0247 a https://doi.org/10.1002/2016JG0036402 DOI
040 a (SwePub)lu
041 a engb eng
042 9 SwePub
072 7a art2 swepub-publicationtype
072 7a ref2 swepub-contenttype
1001 a Ichii, Kazuhitou National Institute for Environmental Studies of Japan,Chiba University,Japan Agency for Marine-Earth Science and Technology4 aut
2451 0a New data-driven estimation of terrestrial CO2 fluxes in Asia using a standardized database of eddy covariance measurements, remote sensing data, and support vector regression
264 1c 2017
520 a The lack of a standardized database of eddy covariance observations has been an obstacle for data-driven estimation of terrestrial CO2 fluxes in Asia. In this study, we developed such a standardized database using 54 sites from various databases by applying consistent postprocessing for data-driven estimation of gross primary productivity (GPP) and net ecosystem CO2 exchange (NEE). Data-driven estimation was conducted by using a machine learning algorithm: support vector regression (SVR), with remote sensing data for 2000 to 2015 period. Site-level evaluation of the estimated CO2 fluxes shows that although performance varies in different vegetation and climate classifications, GPP and NEE at 8days are reproduced (e.g., r2=0.73 and 0.42 for 8day GPP and NEE). Evaluation of spatially estimated GPP with Global Ozone Monitoring Experiment 2 sensor-based Sun-induced chlorophyll fluorescence shows that monthly GPP variations at subcontinental scale were reproduced by SVR (r2=1.00, 0.94, 0.91, and 0.89 for Siberia, East Asia, South Asia, and Southeast Asia, respectively). Evaluation of spatially estimated NEE with net atmosphere-land CO2 fluxes of Greenhouse Gases Observing Satellite (GOSAT) Level 4A product shows that monthly variations of these data were consistent in Siberia and East Asia; meanwhile, inconsistency was found in South Asia and Southeast Asia. Furthermore, differences in the land CO2 fluxes from SVR-NEE and GOSAT Level 4A were partially explained by accounting for the differences in the definition of land CO2 fluxes. These data-driven estimates can provide a new opportunity to assess CO2 fluxes in Asia and evaluate and constrain terrestrial ecosystem models.
650 7a NATURVETENSKAPx Geovetenskap och miljövetenskapx Klimatforskning0 (SwePub)105012 hsv//swe
650 7a NATURAL SCIENCESx Earth and Related Environmental Sciencesx Climate Research0 (SwePub)105012 hsv//eng
653 a Asia
653 a Data-driven model
653 a Eddy covariance data
653 a Remote sensing
653 a Terrestrial CO flux
653 a Upscaling
7001 a Ueyama, Masahitou Osaka Prefecture University4 aut
7001 a Kondo, Masayukiu Chiba University,Japan Agency for Marine-Earth Science and Technology4 aut
7001 a Saigusa, Nobukou National Institute for Environmental Studies of Japan4 aut
7001 a Kim, Joonu Seoul National University4 aut
7001 a Alberto, Ma Carmelitau International Rice Research Institute (IRRI)4 aut
7001 a Ardö, Jonasu Lund University,Lunds universitet,Institutionen för naturgeografi och ekosystemvetenskap,Naturvetenskapliga fakulteten,Dept of Physical Geography and Ecosystem Science,Faculty of Science4 aut0 (Swepub:lu)natg-jar
7001 a Euskirchen, Eugénie S.u University of Alaska Fairbanks4 aut
7001 a Kang, Minseoku Seoul National University4 aut
7001 a Hirano, Takashiu Hokkaido University4 aut
7001 a Joiner, Joannau NASA Goddard Space Flight Center4 aut
7001 a Kobayashi, Hidekiu Japan Agency for Marine-Earth Science and Technology4 aut
7001 a Marchesini, Luca Belelliu Far Eastern Federal University,Vrije Universiteit Amsterdam4 aut
7001 a Merbold, Lutzu International Livestock Research Institute Nairobi,ETH Zürich4 aut
7001 a Miyata, Akirau Institute for Agro-Environmental Sciences, NARO4 aut
7001 a Saitoh, Taku M.u Gifu University4 aut
7001 a Takagi, Kentarou Hokkaido University4 aut
7001 a Varlagin, Andreju Severtsov Institute of Ecology and Evolution, Russian Academy of Sciences4 aut
7001 a Bret-Harte, M. Syndoniau University of Alaska Fairbanks4 aut
7001 a Kitamura, Kenzou Forestry and Forest Products Research Institute4 aut
7001 a Kosugi, Yoshikou Kyoto University4 aut
7001 a Kotani, Ayumiu Nagoya University4 aut
7001 a Kumar, Kireetu G. B. Pant National Institute of Himalayan Environment and Sustainable Development4 aut
7001 a Li, Sheng Gongu Institute of Geographical Sciences and Natural Resources Research Chinese Academy of Sciences4 aut
7001 a Machimura, Takashiu Osaka University4 aut
7001 a Matsuura, Yojirou Forestry and Forest Products Research Institute4 aut
7001 a Mizoguchi, Yasukou Forestry and Forest Products Research Institute4 aut
7001 a Ohta, Takeshiu Nagoya University4 aut
7001 a Mukherjee, Sandipanu G. B. Pant National Institute of Himalayan Environment and Sustainable Development4 aut
7001 a Yanagi, Yujiu Japan Agency for Marine-Earth Science and Technology4 aut
7001 a Yasuda, Yukiou Forestry and Forest Products Research Institute4 aut
7001 a Zhang, Yipingu Chinese Academy of Sciences4 aut
7001 a Zhao, Fenghuau Institute of Geographical Sciences and Natural Resources Research Chinese Academy of Sciences4 aut
7102 a National Institute for Environmental Studies of Japanb Chiba University4 org
7730 t Journal of Geophysical Research - Biogeosciencesg 122:4, s. 767-795q 122:4<767-795x 2169-8953
8564 u http://dx.doi.org/10.1002/2016JG003640y FULLTEXT
8564 8u https://lup.lub.lu.se/record/d73171d4-11b1-4b85-a2dc-6225e5df3187
8564 8u https://doi.org/10.1002/2016JG003640

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