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New data‐driven estimation of terrestrial CO 2 fluxes in Asia using a standardized database of eddy covariance measurements, remote sensing data, and support vector regression
Author(s) -
Ichii Kazuhito,
Ueyama Masahito,
Kondo Masayuki,
Saigusa Nobuko,
Kim Joon,
Alberto Ma. Carmelita,
Ardö Jonas,
Euskirchen Eugénie S.,
Kang Minseok,
Hirano Takashi,
Joiner Joanna,
Kobayashi Hideki,
Marchesini Luca Belelli,
Merbold Lutz,
Miyata Akira,
Saitoh Taku M.,
Takagi Kentaro,
Varlagin Andrej,
BretHarte M. Syndonia,
Kitamura Kenzo,
Kosugi Yoshiko,
Kotani Ayumi,
Kumar Kireet,
Li ShengGong,
Machimura Takashi,
Matsuura Yojiro,
Mizoguchi Yasuko,
Ohta Takeshi,
Mukherjee Sandipan,
Yanagi Yuji,
Yasuda Yukio,
Zhang Yiping,
Zhao Fenghua
Publication year - 2017
Publication title -
journal of geophysical research: biogeosciences
Language(s) - English
Resource type - Journals
eISSN - 2169-8961
pISSN - 2169-8953
DOI - 10.1002/2016jg003640
Subject(s) - eddy covariance , environmental science , satellite , greenhouse gas , primary production , vegetation (pathology) , atmosphere (unit) , climatology , remote sensing , atmospheric sciences , meteorology , geography , ecosystem , geology , ecology , medicine , pathology , engineering , biology , aerospace engineering , oceanography
The lack of a standardized database of eddy covariance observations has been an obstacle for data‐driven estimation of terrestrial CO 2 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 CO 2 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 CO 2 fluxes shows that although performance varies in different vegetation and climate classifications, GPP and NEE at 8 days are reproduced (e.g., r 2 = 0.73 and 0.42 for 8 day 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 ( r 2 = 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 CO 2 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 CO 2 fluxes from SVR‐NEE and GOSAT Level 4A were partially explained by accounting for the differences in the definition of land CO 2 fluxes. These data‐driven estimates can provide a new opportunity to assess CO 2 fluxes in Asia and evaluate and constrain terrestrial ecosystem models.