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title: 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
author: 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
Issued Date: 2017
Keyword: terrestrial CO2 flux;  data-driven model;  eddy covariance data;  remote sensing;  Asia;  upscaling
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.
Department: 其他
Related URLs: 10.1002/2016JG003640
Source: JOURNAL OF GEOPHYSICAL RESEARCH-BIOGEOSCIENCES
Content Type: 期刊论文
URI: http://ir.xtbg.org.cn/handle/353005/10762
Language: 英语
Appears in Collections:全球变化研究组_期刊论文

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全文许可: Creative Commons 署名-非商业性使用-相同方式共享 3.0

Recommended Citation:
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.

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