数字农科院2.0

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

文献类型： 外文期刊

作者： Mingjuan Xie;Xiaofei Ma;Yuangang Wang;Chaofan Li;Haiyang Shi;Xiuliang Yuan;Olaf Hellwich;Chunbo Chen;Wenqiang Zhang;Chen Zhang;Qing Ling;Ruixiang Gao;Yu Zhang;Friday Uchenna Ochege;Amaury Frankl;Philippe De Maeyer;Nina Buchmann;Iris Feigenwinter;Jørgen E. Olesen;Radoslaw Juszczak;Adrien Jacotot;Aino Korrensalo;Andrea Pitacco;Andrej Varlagin;Ankit Shekhar;Annalea Lohila;Arnaud Carrara;Aurore Brut;Bart Kruijt;Benjamin Loubet;Bernard Heinesch;Bogdan Chojnicki;Carole Helfter;Caroline Vincke;Changliang Shao;Christian Bernhofer;Christian Brümmer;Christian Wille;Eeva Stiina Tuittila;Eiko Nemitz;Franco Meggio;Gang Dong;Gary Lanigan;Georg Niedrist;Georg Wohlfahrt;Guoyi Zhou;Ignacio Goded;Thomas Gruenwald;Janusz Olejnik;Joachim Jansen;Johan Neirynck;Juha Pekka Tuovinen;Junhui Zhang;Katja Klumpp;Kim Pilegaard;Ladislav Šigut;Leif Klemedtsson;Luca Tezza;Lukas Hörtnagl;Marek Urbaniak;Marilyn Roland;Marius Schmidt;Mark A. Sutton;Markus He

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期刊名称： Scientific Data

ISSN： 2052-4463

年卷期： 2023 年 10 卷 1 期

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收录情况： SCIE（2023版）

摘要： 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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