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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:
  • Kazuhito Ichii
  • Masahito Ueyama
  • Masayuki Kondo
  • Nobuko Saigusa
  • Joon Kim
  • Ma Carmelita Alberto
  • Jonas Ardö
  • Eugénie S. Euskirchen
  • Minseok Kang
  • Takashi Hirano
  • Joanna Joiner
  • Hideki Kobayashi
  • Luca Belelli Marchesini
  • Lutz Merbold
  • Akira Miyata
  • Taku M. Saitoh
  • Kentaro Takagi
  • Andrej Varlagin
  • M. Syndonia Bret-Harte
  • Kenzo Kitamura
  • Yoshiko Kosugi
  • Ayumi Kotani
  • Kireet Kumar
  • Sheng Gong Li
  • Takashi Machimura
  • Yojiro Matsuura
  • Yasuko Mizoguchi
  • Takeshi Ohta
  • Sandipan Mukherjee
  • Yuji Yanagi
  • Yukio Yasuda
  • Yiping Zhang
  • Fenghua Zhao
Publishing year: 2017-04-11
Language: English
Pages: 767-795
Publication/Series: Journal of Geophysical Research - Biogeosciences
Volume: 122
Issue: 4
Document type: Journal article
Publisher: American Geophysical Union (AGU)

Abstract english

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.

Keywords

  • Climate Research
  • Asia
  • Data-driven model
  • Eddy covariance data
  • Remote sensing
  • Terrestrial CO flux
  • Upscaling

Other

Published
  • ISSN: 2169-8953
E-mail: jonas [dot] ardo [at] nateko [dot] lu [dot] se

Professor

Dept of Physical Geography and Ecosystem Science

+46 46 222 40 31

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Teaching staff

Dept of Physical Geography and Ecosystem Science

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Department of Physical Geography and Ecosystem Science
Lund University
Sölvegatan 12
S-223 62 Lund
Sweden

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