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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; [...]

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

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  • Media type: E-Article
  • Title: Monitoring of carbon-water fluxes at Eurasian meteorological stations using random forest and remote sensing
  • Contributor: 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; [...]
  • imprint: Springer Science and Business Media LLC, 2023
  • Published in: Scientific Data
  • Language: English
  • DOI: 10.1038/s41597-023-02473-9
  • ISSN: 2052-4463
  • Keywords: Library and Information Sciences ; Statistics, Probability and Uncertainty ; Computer Science Applications ; Education ; Information Systems ; Statistics and Probability
  • Origination:
  • Footnote:
  • Description: <jats:title>Abstract</jats:title><jats:p>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 (R<jats:sup>2</jats:sup>), 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.</jats:p>
  • Access State: Open Access