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; Neirynck, Johan; Tuovinen, Juha-Pekka; Zhang, Junhui; Klumpp, Katja; Pilegaard, Kim; Šigut, Ladislav; Klemedtsson, Leif; Tezza, Luca; Hörtnagl, Lukas; Urbaniak, Marek; Roland, Marilyn; Schmidt, Marius; Sutton, Mark A.; Hehn, Markus; Saunders, Matthew; Mauder, Matthias; Aurela, Mika; Korkiakoski, Mika; Du, Mingyuan; Vendrame, Nadia; Kowalska, Natalia; Leahy, Paul G.; Alekseychik, Pavel; Shi, Peili; Weslien, Per; Chen, Shiping; Fares, Silvano; Friborg, Thomas; Tallec, Tiphaine; Kato, Tomomichi; Sachs, T.; Maximov, Trofim; di Cella, Umberto Morra; Moderow, Uta; Li, Yingnian; He, Yongtao; Kosugi, Yoshiko; Luo, Geping

doi:10.1038/s41597-023-02473-9

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

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., Brümmer, C., Wille, C., Tuittila, E.-S., 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.-P., Zhang, J., Klumpp, K., Pilegaard, K., Šigut, L., Klemedtsson, L., Tezza, L., Hörtnagl, 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, N., Leahy, P. 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. (2023): Monitoring of carbon-water fluxes at Eurasian meteorological stations using random forest and remote sensing. - Scientific Data, 10, 587.
https://doi.org/10.1038/s41597-023-02473-9

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Xie, Mingjuan¹, Author
Ma, Xiaofei¹, Author
Wang, Yuangang¹, Author
Li, Chaofan¹, Author
Shi, Haiyang¹, Author
Yuan, Xiuliang¹, Author
Hellwich, Olaf¹, Author
Chen, Chunbo¹, Author
Zhang, Wenqiang¹, Author
Zhang, Chen¹, Author
Ling, Qing¹, Author
Gao, Ruixiang¹, Author
Zhang, Yu¹, Author
Ochege, Friday Uchenna¹, Author
Frankl, Amaury¹, Author
De Maeyer, Philippe¹, Author
Buchmann, Nina¹, Author
Feigenwinter, Iris¹, Author
Olesen, Jørgen E.¹, Author
Juszczak, Radoslaw¹, Author
Jacotot, Adrien¹, AuthorKorrensalo, Aino¹, AuthorPitacco, Andrea¹, AuthorVarlagin, Andrej¹, AuthorShekhar, Ankit¹, AuthorLohila, Annalea¹, AuthorCarrara, Arnaud¹, AuthorBrut, Aurore¹, AuthorKruijt, Bart¹, AuthorLoubet, Benjamin¹, AuthorHeinesch, Bernard¹, AuthorChojnicki, Bogdan¹, AuthorHelfter, Carole¹, AuthorVincke, Caroline¹, AuthorShao, Changliang¹, AuthorBernhofer, Christian¹, AuthorBrümmer, Christian¹, AuthorWille, Christian¹, AuthorTuittila, Eeva-Stiina¹, AuthorNemitz, Eiko¹, AuthorMeggio, Franco¹, AuthorDong, Gang¹, AuthorLanigan, Gary¹, AuthorNiedrist, Georg¹, AuthorWohlfahrt, Georg¹, AuthorZhou, Guoyi¹, AuthorGoded, Ignacio¹, AuthorGruenwald, Thomas¹, AuthorOlejnik, Janusz¹, AuthorJansen, Joachim¹, AuthorNeirynck, Johan¹, AuthorTuovinen, Juha-Pekka¹, AuthorZhang, Junhui¹, AuthorKlumpp, Katja¹, AuthorPilegaard, Kim¹, AuthorŠigut, Ladislav¹, AuthorKlemedtsson, Leif¹, AuthorTezza, Luca¹, AuthorHörtnagl, Lukas¹, AuthorUrbaniak, Marek¹, AuthorRoland, Marilyn¹, AuthorSchmidt, Marius¹, AuthorSutton, Mark A.¹, AuthorHehn, Markus¹, AuthorSaunders, Matthew¹, AuthorMauder, Matthias¹, AuthorAurela, Mika¹, AuthorKorkiakoski, Mika¹, AuthorDu, Mingyuan¹, AuthorVendrame, Nadia¹, AuthorKowalska, Natalia¹, AuthorLeahy, Paul G.¹, AuthorAlekseychik, Pavel¹, AuthorShi, Peili¹, AuthorWeslien, Per¹, AuthorChen, Shiping¹, AuthorFares, Silvano¹, AuthorFriborg, Thomas¹, AuthorTallec, Tiphaine¹, AuthorKato, Tomomichi¹, AuthorSachs, T.², Author Maximov, Trofim¹, Authordi Cella, Umberto Morra¹, AuthorModerow, Uta¹, AuthorLi, Yingnian¹, AuthorHe, Yongtao¹, AuthorKosugi, Yoshiko¹, AuthorLuo, Geping¹, Author more..

Affiliations:
1External Organizations, ou_persistent22
21.4 Remote Sensing, 1.0 Geodesy, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum, ou_146028

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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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Dates: Published Online: 2023-09-07Finally published : 2023

Publication Status: Finally published

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Identifiers: DOI: 10.1038/s41597-023-02473-9
GFZPOF: p4 T5 Future Landscapes
GFZPOFCCA: p4 CARF RemSens
OATYPE: Gold Open Access

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Title: Scientific Data

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Pages: - Volume / Issue: 10 Sequence Number: 587 Start / End Page: - Identifier: CoNE: https://gfzpublic.gfz-potsdam.de/cone/journals/resource/1911041
Publisher: Springer Nature