0576230 - ÚVGZ 2024 RIV GB eng J - Journal Article
Xie, M. - Ma, X. - Wang, Y. - Li, C. - Shi, H. - Yuan, X. - Hellwich, O. - Chen, C. - Zhang, W. - Zhang, C. - Ling, Q. - Gao, R. - Zhang, Y. - Ochege, F. U. - Frankl, A. - De Maeyer, P. - Buchmann, N. - Feigenwinter, I. - Olesen, J.E. - Juszczak, R. - Jacotot, A. - Korrensalo, A. - Pitacco, A. - Varlagin, A. - Shekhar, A. - Lohila, A. - Carrara, A. - Brut, A. - Kruijt, B. - Loubet, B. - Heinesch, B. - Chojnicki, B. - Helfter, C. - Vincke, C. - Shao, C. - Bernhofer, C. - Bruemmer, C. - Wille, C. - Tuittila, E. - Nemitz, E. - Meggio, F. - Dong, G. - Lanigan, G. - Niedrist, G. - Wohlfahrt, G. - Zhou, G. - Goded, I. - Gruenwald, T. - Olejnik, J. - Jansen, J. - Neirynck, J. - Tuovinen, J. - Zhang, J. - Klumpp, K. - Pilegaard, K. - Šigut, Ladislav - Klemedtsson, L. - Tezza, L. - Hoertnagl, L. - Urbaniak, M. - Roland, M. - Schmidt, M. - Sutton, M.A. - Hehn, M. - Saunders, M. - Mauder, M. - Aurela, M. - Korkiakoski, M. - Du, M. - Vendrame, N. - Kowalska, Natalia - Leahy, Paul G. - Alekseychik, P. - Shi, P. - Weslien, P. - Chen, S. - Fares, S. - Friborg, T. - Tallec, T. - Kato, T. - Sachs, T. - Maximov, T. - di Cella, U. M. - Moderow, U. - Li, Y. - He, Y. - Kosugi, Y. - Luo, G.
Monitoring of carbon-water fluxes at Eurasian meteorological stations using random forest and remote sensing.
Scientific Data. Roč. 10, č. 1 (2023), č. článku 587. E-ISSN 2052-4463
Research Infrastructure: CzeCOS IV - 90248
Institutional support: RVO:86652079
Keywords : ecology * hydrology * models * site * river-basin * upscaling evapotranspiration
OECD category: Meteorology and atmospheric sciences
Impact factor: 9.8, year: 2022
Method of publishing: Open access
https://www.nature.com/articles/s41597-023-02473-9

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.
Permanent Link: https://hdl.handle.net/11104/0345810