Monitoring of carbon-water fluxes at Eurasian meteorological stations using random forest and remote sensing

Xie, Mingjuan; Ma, Xiaofei; Wang, Yuangang; Li, Chaofan; Shi, Haiyang; Yuan, Xiuliang; Hellwich, Olaf; Chen, Chunbo; Zhang, Wenqiang; Zhang, Chen; Ling, Qing; Gao, Ruixiang; Zhang, Yu; Ochege, Friday Uchenna; Frankl, Amaury; De Maeyer, Philippe; Buchmann, Nina; Feigenwinter, Iris; Olesen, Jørgen E.; Juszczak, Radoslaw; Jacotot, Adrien; Korrensalo, Aino; Pitacco, Andrea; Varlagin, Andrej; Shekhar, Ankit; Lohila, Annalea; Carrara, Arnaud; Brut, Aurore; Kruijt, Bart; Loubet, Benjamin; Heinesch, Bernard; Chojnicki, Bogdan; Helfter, Carole ORCID: https://orcid.org/0000-0001-5773-4652; Vincke, Caroline; Shao, Changliang; Bernhofer, Christian; Brümmer, Christian; Wille, Christian; Tuittila, Eeva-Stiina; Nemitz, Eiko ORCID: https://orcid.org/0000-0002-1765-6298; Meggio, Franco; Dong, Gang; Lanigan, Gary; Niedrist, Georg; Wohlfahrt, Georg; Zhou, Guoyi; Goded, Ignacio; Gruenwald, Thomas; Olejnik, Janusz; Jansen, Joachim; Neirynck, Johan; Tuovinen, Juha-Pekka; Zhang, Junhui; Klumpp, Katja; Pilegaard, Kim; Šigut, Ladislav; Klemedtsson, Leif; Tezza, Luca; Hörtnagl, Lukas; Urbaniak, Marek; Roland, Marilyn; Schmidt, Marius; Sutton, Mark A. ORCID: https://orcid.org/0000-0002-1342-2072; Hehn, Markus; Saunders, Matthew; Mauder, Matthias; Aurela, Mika; Korkiakoski, Mika; Du, Mingyuan; Vendrame, Nadia; Kowalska, Natalia; Leahy, Paul G.; Alekseychik, Pavel; Shi, Peili; Weslien, Per; Chen, Shiping; Fares, Silvano; Friborg, Thomas; Tallec, Tiphaine; Kato, Tomomichi; Sachs, Torsten; Maximov, Trofim; di Cella, Umberto Morra; Moderow, Uta; Li, Yingnian; He, Yongtao; Kosugi, Yoshiko; Luo, Geping. 2023 Monitoring of carbon-water fluxes at Eurasian meteorological stations using random forest and remote sensing. Scientific Data, 10 (1), 587. 18, pp. https://doi.org/10.1038/s41597-023-02473-9

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Abstract/Summary

Simulating the carbon-water fluxes at more widely distributed meteorological stations based on the sparsely and unevenly distributed eddy covariance flux stations is needed to accurately understand the carbon-water cycle of terrestrial ecosystems. We established a new framework consisting of machine learning, determination coefficient (R2), Euclidean distance, and remote sensing (RS), to simulate the daily net ecosystem carbon dioxide exchange (NEE) and water flux (WF) of the Eurasian meteorological stations using a random forest model or/and RS. The daily NEE and WF datasets with RS-based information (NEE-RS and WF-RS) for 3774 and 4427 meteorological stations during 2002–2020 were produced, respectively. And the daily NEE and WF datasets without RS-based information (NEE-WRS and WF-WRS) for 4667 and 6763 meteorological stations during 1983–2018 were generated, respectively. For each meteorological station, the carbon-water fluxes meet accuracy requirements and have quasi-observational properties. These four carbon-water flux datasets have great potential to improve the assessments of the ecosystem carbon-water dynamics.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	https://doi.org/10.1038/s41597-023-02473-9
UKCEH and CEH Sections/Science Areas:	Atmospheric Chemistry and Effects (Science Area 2017-)
ISSN:	2052-4463
Additional Information. Not used in RCUK Gateway to Research.:	Open Access paper - full text available via Official URL link.
NORA Subject Terms:	Ecology and Environment Hydrology
Related URLs:	Other Dataset Dataset Dataset Dataset Dataset
Date made live:	09 Nov 2023 13:19 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/536054

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