Počet záznamů: 1
Monitoring of carbon-water fluxes at Eurasian meteorological stations using random forest and remote sensing
- 1.
SYSNO ASEP 0576230 Document Type J - Journal Article R&D Document Type Journal Article Subsidiary J Článek ve WOS Title Monitoring of carbon-water fluxes at Eurasian meteorological stations using random forest and remote sensing Author(s) Xie, M. (CN)
Ma, X. (CN)
Wang, Y. (CN)
Li, C. (CN)
Shi, H. (CN)
Yuan, X. (CN)
Hellwich, O. (DE)
Chen, C. (CN)
Zhang, W. (CN)
Zhang, C. (CN)
Ling, Q. (CN)
Gao, R. (CN)
Zhang, Y. (CN)
Ochege, F. U. (CN)
Frankl, A. (BE)
De Maeyer, P. (BE)
Buchmann, N. (CH)
Feigenwinter, I. (CH)
Olesen, J.E. (DK)
Juszczak, R. (PL)
Jacotot, A. (FR)
Korrensalo, A. (FI)
Pitacco, A. (IT)
Varlagin, A. (RU)
Shekhar, A. (CH)
Lohila, A. (FI)
Carrara, A. (ES)
Brut, A. (FR)
Kruijt, B. (NL)
Loubet, B. (FR)
Heinesch, B. (BE)
Chojnicki, B. (PL)
Helfter, C. (GB)
Vincke, C. (BE)
Shao, C. (CN)
Bernhofer, C. (DE)
Bruemmer, C. (DE)
Wille, C. (DE)
Tuittila, E. (FI)
Nemitz, E. (GB)
Meggio, F. (IT)
Dong, G. (CN)
Lanigan, G. (IE)
Niedrist, G. (IT)
Wohlfahrt, G. (AT)
Zhou, G. (CN)
Goded, I. (IT)
Gruenwald, T. (DE)
Olejnik, J. (PL)
Jansen, J. (SE)
Neirynck, J. (BE)
Tuovinen, J. (FI)
Zhang, J. (CN)
Klumpp, K. (FR)
Pilegaard, K. (DK)
Šigut, Ladislav (UEK-B) RID, ORCID, SAI
Klemedtsson, L. (SE)
Tezza, L. (IT)
Hoertnagl, L. (CH)
Urbaniak, M. (PL)
Roland, M. (BE)
Schmidt, M. (DE)
Sutton, M.A. (GB)
Hehn, M. (DE)
Saunders, M. (IE)
Mauder, M. (DE)
Aurela, M. (FI)
Korkiakoski, M. (FI)
Du, M. (JP)
Vendrame, N. (IT)
Kowalska, Natalia (UEK-B) SAI, ORCID, RID
Leahy, Paul G. (IE)
Alekseychik, P. (FI)
Shi, P. (CN)
Weslien, P. (SE)
Chen, S. (CN)
Fares, S. (IT)
Friborg, T. (DK)
Tallec, T. (FR)
Kato, T. (JP)
Sachs, T. (DE)
Maximov, T. (RU)
di Cella, U. M. (IT)
Moderow, U. (DE)
Li, Y. (CN)
He, Y. (CN)
Kosugi, Y. (JP)
Luo, G. (CN)Number of authors 88 Article number 587 Source Title Scientific Data. - : Nature Publishing Group - ISSN 2052-4463
Roč. 10, č. 1 (2023)Number of pages 18 s. Language eng - English Country GB - United Kingdom Keywords ecology ; hydrology ; models ; site ; river-basin ; upscaling evapotranspiration Subject RIV DG - Athmosphere Sciences, Meteorology OECD category Meteorology and atmospheric sciences Research Infrastructure CzeCOS IV - 90248 - Ústav výzkumu globální změny AV ČR, v. v. i. Method of publishing Open access Institutional support UEK-B - RVO:86652079 UT WOS 001065045600002 EID SCOPUS 85170167023 DOI https://doi.org/10.1038/s41597-023-02473-9 Annotation 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. Workplace Global Change Research Institute Contact Nikola Šviková, svikova.n@czechglobe.cz, Tel.: 511 192 268 Year of Publishing 2024 Electronic address https://www.nature.com/articles/s41597-023-02473-9
Počet záznamů: 1