|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|
|Ichii, Kazuhito; Ueyama, Masahito; Kondo, Masayuki; Saigusa, Nobuko; Kim, Joon; Carmelita Alberto, Ma.; Ardoe, Jonas; Euskirchen, Eugenie S.; Kang, Minseok; Hirano, Takashi; Joiner, Joanna; Kobayashi, Hideki; Marchesini, Luca Belelli; Merbold, Lutz; Miyata, Akira; Saitoh, Taku M.; Takagi, Kentaro; Varlagin, Andrej; Bret-Harte, M. Syndonia; Kitamura, Kenzo; Kosugi, Yoshiko; Kotani, Ayumi; Kumar, Kireet; Li, Sheng-Gong; Machimura, Takashi; Matsuura, Yojiro; Mizoguchi, Yasuko; Ohta, Takeshi; Mukherjee, Sandipan; Yanagi, Yuji; Yasuda, Yukio; Zhang, Yiping; Zhao, Fenghua
|Source Publication||JOURNAL OF GEOPHYSICAL RESEARCH-BIOGEOSCIENCES
|Abstract||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., r(2)=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 (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 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.|
|Keyword||Terrestrial Co2 Flux
Eddy Covariance Data
Ichii, Kazuhito,Ueyama, Masahito,Kondo, Masayuki,et al. 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[J]. JOURNAL OF GEOPHYSICAL RESEARCH-BIOGEOSCIENCES,2017,122(4):767-795.
Ichii, Kazuhito.,Ueyama, Masahito.,Kondo, Masayuki.,Saigusa, Nobuko.,Kim, Joon.,...&Zhao, Fenghua.(2017).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.JOURNAL OF GEOPHYSICAL RESEARCH-BIOGEOSCIENCES,122(4),767-795.
Ichii, Kazuhito,et al."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".JOURNAL OF GEOPHYSICAL RESEARCH-BIOGEOSCIENCES 122.4(2017):767-795.
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