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
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...
| Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Formato: | Journal Article |
| Lenguaje: | Inglés |
| Publicado: |
Wiley
2017
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| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/82763 |
| _version_ | 1855531765746106368 |
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| author | Ichii, Kazuhito Ueyama, Masahito Masayuki Kondo Saigusa, Nobuko Kim, Joon Alberto, Ma. Carmelita R. Ardö, Jonas Euskirchen, Eugenie S. Minseok Kang Hirano, Takashi Joiner, Joanna Kobayashi, Hideki Belelli Marchesini, Luca Merbold, Lutz Miyata, Akira Saitoh, Taku M. Takagi, Kentaro Varlagin, Andrej Bret-Harte, Marion Syndonia Kenzo Kitamura Kosugi, Yoshiko Ayumi Kotani Kumar, K. Li, Shenggong Machimura, Takashi Yojiro Matsuura Yasuko Mizoguchi Takeshi Ohta Mukherjee, Sandipan Yuji Yanagi Yasuda, Yukio Yiping, Zhang Fenghua Zhao |
| author_browse | Alberto, Ma. Carmelita R. Ardö, Jonas Ayumi Kotani Belelli Marchesini, Luca Bret-Harte, Marion Syndonia Euskirchen, Eugenie S. Fenghua Zhao Hirano, Takashi Ichii, Kazuhito Joiner, Joanna Kenzo Kitamura Kim, Joon Kobayashi, Hideki Kosugi, Yoshiko Kumar, K. Li, Shenggong Machimura, Takashi Masayuki Kondo Merbold, Lutz Minseok Kang Miyata, Akira Mukherjee, Sandipan Saigusa, Nobuko Saitoh, Taku M. Takagi, Kentaro Takeshi Ohta Ueyama, Masahito Varlagin, Andrej Yasuda, Yukio Yasuko Mizoguchi Yiping, Zhang Yojiro Matsuura Yuji Yanagi |
| author_facet | Ichii, Kazuhito Ueyama, Masahito Masayuki Kondo Saigusa, Nobuko Kim, Joon Alberto, Ma. Carmelita R. Ardö, Jonas Euskirchen, Eugenie S. Minseok Kang Hirano, Takashi Joiner, Joanna Kobayashi, Hideki Belelli Marchesini, Luca Merbold, Lutz Miyata, Akira Saitoh, Taku M. Takagi, Kentaro Varlagin, Andrej Bret-Harte, Marion Syndonia Kenzo Kitamura Kosugi, Yoshiko Ayumi Kotani Kumar, K. Li, Shenggong Machimura, Takashi Yojiro Matsuura Yasuko Mizoguchi Takeshi Ohta Mukherjee, Sandipan Yuji Yanagi Yasuda, Yukio Yiping, Zhang Fenghua Zhao |
| author_sort | Ichii, Kazuhito |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | 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 8 days are reproduced (e.g., r2 = 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 (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. |
| format | Journal Article |
| id | CGSpace82763 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2017 |
| publishDateRange | 2017 |
| publishDateSort | 2017 |
| publisher | Wiley |
| publisherStr | Wiley |
| record_format | dspace |
| spelling | CGSpace827632025-03-11T12:14:31Z 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 Masayuki Kondo Saigusa, Nobuko Kim, Joon Alberto, Ma. Carmelita R. Ardö, Jonas Euskirchen, Eugenie S. Minseok Kang Hirano, Takashi Joiner, Joanna Kobayashi, Hideki Belelli Marchesini, Luca Merbold, Lutz Miyata, Akira Saitoh, Taku M. Takagi, Kentaro Varlagin, Andrej Bret-Harte, Marion Syndonia Kenzo Kitamura Kosugi, Yoshiko Ayumi Kotani Kumar, K. Li, Shenggong Machimura, Takashi Yojiro Matsuura Yasuko Mizoguchi Takeshi Ohta Mukherjee, Sandipan Yuji Yanagi Yasuda, Yukio Yiping, Zhang Fenghua Zhao data 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 8 days are reproduced (e.g., r2 = 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 (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. 2017-04 2017-07-13T10:03:50Z 2017-07-13T10:03:50Z Journal Article https://hdl.handle.net/10568/82763 en Open Access Wiley Ichii, K., et al. 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. |
| spellingShingle | data Ichii, Kazuhito Ueyama, Masahito Masayuki Kondo Saigusa, Nobuko Kim, Joon Alberto, Ma. Carmelita R. Ardö, Jonas Euskirchen, Eugenie S. Minseok Kang Hirano, Takashi Joiner, Joanna Kobayashi, Hideki Belelli Marchesini, Luca Merbold, Lutz Miyata, Akira Saitoh, Taku M. Takagi, Kentaro Varlagin, Andrej Bret-Harte, Marion Syndonia Kenzo Kitamura Kosugi, Yoshiko Ayumi Kotani Kumar, K. Li, Shenggong Machimura, Takashi Yojiro Matsuura Yasuko Mizoguchi Takeshi Ohta Mukherjee, Sandipan Yuji Yanagi Yasuda, Yukio Yiping, Zhang Fenghua Zhao 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 |
| 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 |
| title_full | 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 |
| title_fullStr | 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 |
| title_full_unstemmed | 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 |
| title_short | 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 |
| title_sort | 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 |
| topic | data |
| url | https://hdl.handle.net/10568/82763 |
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