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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; Vincke, Caroline; Shao, Changliang; Bernhofer, Christian; Brümmer, Christian; Wille, Christian; Tuittila, Eeva-Stiina; Nemitz, Eiko; Meggio, Franco; Dong, Gang; Lanigan, Gary; Niedrist, Georg; Wohlfahrt, Georg; Zhou, Guoyi; Goded, Ignacio; Gruenwald, Thomas; Olejnik, Janusz; Jansen, Joachim.
Afiliación
  • Xie M; State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, Xinjiang, 830011, China.
  • Ma X; Department of Geography, Ghent University, Ghent, 9000, Belgium.
  • Wang Y; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, 100049, China.
  • Li C; Sino-Belgian Joint Laboratory for Geo-Information, Urumqi, China.
  • Shi H; Sino-Belgian Joint Laboratory for Geo-Information, Ghent, Belgium.
  • Yuan X; State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, Xinjiang, 830011, China.
  • Hellwich O; State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, Xinjiang, 830011, China.
  • Chen C; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, 100049, China.
  • Zhang W; School of Geographical Sciences, Nanjing University of Information Science and Technology, Nanjing, 210044, China.
  • Zhang C; School of Earth Sciences and Engineering, Hohai University, Nanjing, 211100, China.
  • Ling Q; State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, Xinjiang, 830011, China.
  • Gao R; Department of Computer Vision & Remote Sensing, Technische Universität Berlin, 10587, Berlin, Germany.
  • Zhang Y; State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, Xinjiang, 830011, China.
  • Ochege FU; State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, Xinjiang, 830011, China.
  • Frankl A; Department of Geography, Ghent University, Ghent, 9000, Belgium.
  • De Maeyer P; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, 100049, China.
  • Buchmann N; Sino-Belgian Joint Laboratory for Geo-Information, Urumqi, China.
  • Feigenwinter I; Sino-Belgian Joint Laboratory for Geo-Information, Ghent, Belgium.
  • Olesen JE; State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, Xinjiang, 830011, China.
  • Juszczak R; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, 100049, China.
  • Jacotot A; State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, Xinjiang, 830011, China.
  • Korrensalo A; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, 100049, China.
  • Pitacco A; State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, Xinjiang, 830011, China.
  • Varlagin A; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, 100049, China.
  • Shekhar A; State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, Xinjiang, 830011, China.
  • Lohila A; Department of Geography, Ghent University, Ghent, 9000, Belgium.
  • Carrara A; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, 100049, China.
  • Brut A; Sino-Belgian Joint Laboratory for Geo-Information, Urumqi, China.
  • Kruijt B; Sino-Belgian Joint Laboratory for Geo-Information, Ghent, Belgium.
  • Loubet B; State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, Xinjiang, 830011, China.
  • Heinesch B; Department of Geography and Environmental Management, University of Port Harcourt, PMB 5323 Choba, East-West, Port Harcourt, Nigeria.
  • Chojnicki B; Department of Geography, Ghent University, Ghent, 9000, Belgium.
  • Helfter C; State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, Xinjiang, 830011, China.
  • Vincke C; Department of Geography, Ghent University, Ghent, 9000, Belgium.
  • Shao C; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, 100049, China.
  • Bernhofer C; Sino-Belgian Joint Laboratory for Geo-Information, Urumqi, China.
  • Brümmer C; Sino-Belgian Joint Laboratory for Geo-Information, Ghent, Belgium.
  • Wille C; Department of Environmental Systems Science, Institute of Agricultural Sciences, ETH Zürich, 8092, Zürich, Switzerland.
  • Tuittila ES; Department of Environmental Systems Science, Institute of Agricultural Sciences, ETH Zürich, 8092, Zürich, Switzerland.
  • Nemitz E; Department of Agroecology, Aarhus University, Tjele, Denmark.
  • Meggio F; Laboratory of Bioclimatology, Department of Ecology and Environmental Protection, Faculty of Environmental and Mechanical Engineering, Poznan University of Life Sciences, Piatkowska 94, 60-649, Poznan, Poland.
  • Dong G; Sol, Agro et hydrosystèmes, Spatialisation (SAS), UMR 1069, INRAE, Institut Agro, 35000, Rennes, France.
  • Lanigan G; Department of Environmental and Biological Sciences, University of Eastern Finland, Joensuu campus, P.O Box 111, Joensuu, FI-80101, Finland.
  • Niedrist G; Natural Resources Institute Finland, Joensuu, Yliopistokatu 6, FI-80130, Joensuu, Finland.
  • Wohlfahrt G; University of Padova - DAFNAE, Viale dell'Università 16, I-35020, Padova, Legnaro (PD), Italy.
  • Zhou G; A.N Severtsov Institute of Ecology and Evolution, Russian Academy of Sciences, 119071, Leninsky pr.33, Moscow, Russia.
  • Goded I; Department of Environmental Systems Science, Institute of Agricultural Sciences, ETH Zürich, 8092, Zürich, Switzerland.
  • Gruenwald T; Climate System Research, Finnish Meteorological Institute, P.O Box 503, FI-00101, Helsinki, Finland.
  • Olejnik J; University of Helsinki, Institute for Atmospheric and Earth System Research (INAR)/Physics, Faculty of Science, Helsinki, Finland.
  • Jansen J; Fundación CEAM, Parque Tecnológico, C/Charles R. Darwin, 14, Paterna, 46980, Spain.
Sci Data ; 10(1): 587, 2023 09 07.
Article en En | MEDLINE | ID: mdl-37679357
ABSTRACT
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.
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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Tipo de estudio: Clinical_trials Idioma: En Revista: Sci Data Año: 2023 Tipo del documento: Article País de afiliación: China
Texto completo
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Tipo de estudio: Clinical_trials Idioma: En Revista: Sci Data Año: 2023 Tipo del documento: Article País de afiliación: China