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Article Dans Une Revue (Data Paper) Scientific Data Année : 2023

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

Mingjuan Xie (1, 2, 3) , Xiaofei Ma (1) , Yuangang Wang (2, 1) , Chaofan Li (4) , Haiyang Shi (5) , Xiuliang Yuan (1) , Olaf Hellwich (6) , Chunbo Chen (1) , Wenqiang Zhang (1, 2, 3) , Chen Zhang (1, 2) , Qing Ling (1, 2) , Ruixiang Gao (1, 2) , Yu Zhang (1, 2, 3) , Friday Uchenna Ochege (1, 7) , Amaury Frankl (3) , Philippe de Maeyer (3, 1, 2) , Nina Buchmann (8) , Iris Feigenwinter (8) , Jørgen E Olesen (9) , Radoslaw Juszczak (10) , Adrien Jacotot (11) , Aino Korrensalo (12, 13) , Andrea Pitacco (14) , Andrej Varlagin (15) , Ankit Shekhar (8) , Annalea Lohila (16, 17) , Arnaud Carrara (18) , Aurore Brut (19) , Bart Kruijt (20) , Benjamin Loubet (21) , Bernard Heinesch (22) , Bogdan Chojnicki (10) , Carole Helfter (23) , Caroline Vincke (24) , Changliang Shao (25) , Christian Bernhofer (26) , Christian Brümmer (27) , Christian Wille (28) , Eeva-Stiina Tuittila (12) , Eiko Nemitz (23) , Franco Meggio (14) , Gang Dong (29) , Gary Lanigan (30) , Georg Niedrist (31) , Georg Wohlfahrt (32) , Guoyi Zhou (4) , Ignacio Goded (33) , Thomas Gruenwald (26) , Janusz Olejnik (10) , Joachim Jansen (34) , Johan Neirynck (35) , Juha-Pekka Tuovinen (16) , Junhui Zhang (36) , Katja Klumpp (37) , Kim Pilegaard (38) , Ladislav Šigut (39) , Leif Klemedtsson (40) , Luca Tezza (14) , Lukas Hörtnagl (8) , Marek Urbaniak (34) , Marilyn Roland (41) , Marius Schmidt (42) , Mark A Sutton (23) , Markus Hehn (26) , Matthew Saunders (43) , Matthias Mauder (26) , Mika Aurela (16) , Mika Korkiakoski (16) , Mingyuan Du (44) , Nadia Vendrame (45) , Natalia Kowalska (39) , Paul G Leahy (46) , Pavel Alekseychik (13) , Peili Shi (1) , Per Weslien (40) , Shiping Chen (1) , Silvano Fares (47) , Thomas Friborg (48) , Tiphaine Tallec (19) , Tomomichi Kato (49) , Torsten Sachs (28) , Trofim Maximov (50) , Umberto Morra Di Cella (51) , Uta Moderow (26) , Yingnian Li (1) , Yongtao He (1) , Yoshiko Kosugi (52) , Geping Luo (1, 2)
1 CAS - Chinese Academy of Sciences [Beijing]
2 UCAS - University of Chinese Academy of Sciences [Beijing]
3 UGENT - Universiteit Gent = Ghent University
4 NUIST - Nanjing University of Information Science and Technology
5 Hohai University
6 TU - Technical University of Berlin / Technische Universität Berlin
7 University of Port Harcourt
8 Institute of Agricultural Sciences [Zürich]
9 Aarhus University [Aarhus]
10 PULS - Poznan University of Life Sciences (Uniwersytet Przyrodniczy w Poznaniu)
11 SAS - Sol Agro et hydrosystème Spatialisation
12 University of Eastern Finland
13 LUKE - Natural Resources Institute Finland
14 Unipd - Università degli Studi di Padova = University of Padua
15 A.N. Severtsov Institute of Ecology and Evolution
16 FMI - Finnish Meteorological Institute
17 Helsingin yliopisto = Helsingfors universitet = University of Helsinki
18 CEAM - Centre d'Estudis Ambientals del Mediterrani = Centre for Mediterranean Environmental Studies
19 CESBIO - Centre d'études spatiales de la biosphère
20 WUR - Wageningen University and Research [Wageningen]
21 ECOSYS - Ecologie fonctionnelle et écotoxicologie des agroécosystèmes
22 Gembloux Agro-Bio Tech [Gembloux]
23 UK Centre for Ecology & Hydrology
24 UCL - Université Catholique de Louvain = Catholic University of Louvain
25 CAAS - Chinese Academy of Agricultural Sciences
26 TU Dresden - Technische Universität Dresden = Dresden University of Technology
27 Thünen Institute
28 GFZ - German Research Centre for Geosciences - Helmholtz-Centre Potsdam
29 SXU - Shanxi University
30 Teagasc - Teagasc - The Agriculture and Food Development Authority
31 Institute for Alpine Environment
32 Universität Innsbruck [Innsbruck]
33 JRC - European Commission - Joint Research Centre [Ispra]
34 Uppsala University
35 INBO - Research Institute for Nature and Forest
36 Qufu Normal University
37 UREP - Unité Mixte de Recherche sur l'Ecosystème Prairial - UMR
38 DTU - Danmarks Tekniske Universitet = Technical University of Denmark
39 CAS - Czech Academy of Sciences [Prague]
40 GU - Göteborgs Universitet = University of Gothenburg
41 UA - University of Antwerp
42 FZJ - Forschungszentrum Jülich GmbH | Centre de recherche de Jülich | Jülich Research Centre
43 Trinity College Dublin
44 NARO - National Agriculture and Food Research Organization
45 UNITN - Università degli Studi di Trento = University of Trento
46 UCC - University College Cork
47 CNR - National Research Council of Italy | Consiglio Nazionale delle Ricerche
48 UCPH - University of Copenhagen = Københavns Universitet
49 Hokkaido University [Sapporo, Japan]
50 SB RAS - Siberian Branch of the Russian Academy of Sciences
51 ARPA - Aosta Valley Regional Environmental Protection Agency
52 Kyoto University
Xiaofei Ma
Haiyang Shi
Amaury Frankl
Nina Buchmann
Jørgen E Olesen
Adrien Jacotot
Carole Helfter
Christian Brümmer
Eeva-Stiina Tuittila
Gang Dong
  • Fonction : Auteur
Georg Niedrist
Georg Wohlfahrt
Joachim Jansen
Johan Neirynck
Juha-Pekka Tuovinen
Ladislav Šigut
Marek Urbaniak
Marilyn Roland
Matthew Saunders
Mika Aurela
Mika Korkiakoski
Natalia Kowalska
Paul G Leahy
Peili Shi
Tomomichi Kato
Yingnian Li
Yoshiko Kosugi
  • Fonction : Auteur

Résumé

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 2), 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.

Domaines

Météorologie
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Dates et versions

hal-04232608 , version 1 (08-10-2023)

Licence

Paternité

Identifiants

Citer

Mingjuan Xie, Xiaofei Ma, Yuangang Wang, Chaofan Li, Haiyang Shi, et al.. Monitoring of carbon-water fluxes at Eurasian meteorological stations using random forest and remote sensing. Scientific Data , 2023, 10 (1), pp.587. ⟨10.1038/s41597-023-02473-9⟩. ⟨hal-04232608⟩
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