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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; 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, Torsten; Maximov, Trofim; di Cella, Umberto Morra; Moderow, Uta; Li, Yingnian; He, Yongtao; Kosugi, Yoshiko; Luo, Geping.
I:
Scientific Data, Bind 10, 587, 2023.
Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
Harvard
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, FU, Frankl, A, De Maeyer, P, Buchmann, N, Feigenwinter, I, Olesen, JE, 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, ES, 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, JP, Zhang, J, Klumpp, K, Pilegaard, K, Šigut, L, Klemedtsson, L, Tezza, L, Hörtnagl, L, Urbaniak, M, Roland, M, Schmidt, M, Sutton, MA, Hehn, M, Saunders, M, Mauder, M, Aurela, M, Korkiakoski, M, Du, M, Vendrame, N, Kowalska, N, Leahy, PG, Alekseychik, P, Shi, P, Weslien, P, Chen, S, Fares, S
, Friborg, T, Tallec, T, Kato, T, Sachs, T, Maximov, T, di Cella, UM, 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, bind 10, 587.
https://doi.org/10.1038/s41597-023-02473-9APA
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., ... 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/s41597-023-02473-9Vancouver
Xie M, Ma X, Wang Y, Li C, Shi H, Yuan X o.a.
Monitoring of carbon-water fluxes at Eurasian meteorological stations using random forest and remote sensing.
Scientific Data. 2023;10. 587.
https://doi.org/10.1038/s41597-023-02473-9Author
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, Torsten ; Maximov, Trofim ; di Cella, Umberto Morra ; Moderow, Uta ; Li, Yingnian ; He, Yongtao ; Kosugi, Yoshiko ; Luo, Geping. / Monitoring of carbon-water fluxes at Eurasian meteorological stations using random forest and remote sensing. I: Scientific Data. 2023 ; Bind 10.
Bibtex
@article{e50abe1a9e814a7eb2ed015cf8375970,
title = "Monitoring of carbon-water fluxes at Eurasian meteorological stations using random forest and remote sensing",
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.",
author = "Mingjuan Xie and Xiaofei Ma and Yuangang Wang and Chaofan Li and Haiyang Shi and Xiuliang Yuan and Olaf Hellwich and Chunbo Chen and Wenqiang Zhang and Chen Zhang and Qing Ling and Ruixiang Gao and Yu Zhang and Ochege, {Friday Uchenna} and Amaury Frankl and {De Maeyer}, Philippe and Nina Buchmann and Iris Feigenwinter and Olesen, {J{\o}rgen E.} and Radoslaw Juszczak and Adrien Jacotot and Aino Korrensalo and Andrea Pitacco and Andrej Varlagin and Ankit Shekhar and Annalea Lohila and Arnaud Carrara and Aurore Brut and Bart Kruijt and Benjamin Loubet and Bernard Heinesch and Bogdan Chojnicki and Carole Helfter and Caroline Vincke and Changliang Shao and Christian Bernhofer and Christian Br{\"u}mmer and Christian Wille and Tuittila, {Eeva Stiina} and Eiko Nemitz and Franco Meggio and Gang Dong and Gary Lanigan and Georg Niedrist and Georg Wohlfahrt and Guoyi Zhou and Ignacio Goded and Thomas Gruenwald and Janusz Olejnik and Joachim Jansen and Johan Neirynck and Tuovinen, {Juha Pekka} and Junhui Zhang and Katja Klumpp and Kim Pilegaard and Ladislav {\v S}igut and Leif Klemedtsson and Luca Tezza and Lukas H{\"o}rtnagl and Marek Urbaniak and Marilyn Roland and Marius Schmidt and Sutton, {Mark A.} and Markus Hehn and Matthew Saunders and Matthias Mauder and Mika Aurela and Mika Korkiakoski and Mingyuan Du and Nadia Vendrame and Natalia Kowalska and Leahy, {Paul G.} and Pavel Alekseychik and Peili Shi and Per Weslien and Shiping Chen and Silvano Fares and Thomas Friborg and Tiphaine Tallec and Tomomichi Kato and Torsten Sachs and Trofim Maximov and {di Cella}, {Umberto Morra} and Uta Moderow and Yingnian Li and Yongtao He and Yoshiko Kosugi and Geping Luo",
note = "Publisher Copyright: {\textcopyright} 2023, Springer Nature Limited.",
year = "2023",
doi = "10.1038/s41597-023-02473-9",
language = "English",
volume = "10",
journal = "Scientific data",
issn = "2052-4463",
publisher = "nature publishing group",
}
RIS
TY - JOUR
T1 - Monitoring of carbon-water fluxes at Eurasian meteorological stations using random forest and remote sensing
AU - Xie, Mingjuan
AU - Ma, Xiaofei
AU - Wang, Yuangang
AU - Li, Chaofan
AU - Shi, Haiyang
AU - Yuan, Xiuliang
AU - Hellwich, Olaf
AU - Chen, Chunbo
AU - Zhang, Wenqiang
AU - Zhang, Chen
AU - Ling, Qing
AU - Gao, Ruixiang
AU - Zhang, Yu
AU - Ochege, Friday Uchenna
AU - Frankl, Amaury
AU - De Maeyer, Philippe
AU - Buchmann, Nina
AU - Feigenwinter, Iris
AU - Olesen, Jørgen E.
AU - Juszczak, Radoslaw
AU - Jacotot, Adrien
AU - Korrensalo, Aino
AU - Pitacco, Andrea
AU - Varlagin, Andrej
AU - Shekhar, Ankit
AU - Lohila, Annalea
AU - Carrara, Arnaud
AU - Brut, Aurore
AU - Kruijt, Bart
AU - Loubet, Benjamin
AU - Heinesch, Bernard
AU - Chojnicki, Bogdan
AU - Helfter, Carole
AU - Vincke, Caroline
AU - Shao, Changliang
AU - Bernhofer, Christian
AU - Brümmer, Christian
AU - Wille, Christian
AU - Tuittila, Eeva Stiina
AU - Nemitz, Eiko
AU - Meggio, Franco
AU - Dong, Gang
AU - Lanigan, Gary
AU - Niedrist, Georg
AU - Wohlfahrt, Georg
AU - Zhou, Guoyi
AU - Goded, Ignacio
AU - Gruenwald, Thomas
AU - Olejnik, Janusz
AU - Jansen, Joachim
AU - Neirynck, Johan
AU - Tuovinen, Juha Pekka
AU - Zhang, Junhui
AU - Klumpp, Katja
AU - Pilegaard, Kim
AU - Šigut, Ladislav
AU - Klemedtsson, Leif
AU - Tezza, Luca
AU - Hörtnagl, Lukas
AU - Urbaniak, Marek
AU - Roland, Marilyn
AU - Schmidt, Marius
AU - Sutton, Mark A.
AU - Hehn, Markus
AU - Saunders, Matthew
AU - Mauder, Matthias
AU - Aurela, Mika
AU - Korkiakoski, Mika
AU - Du, Mingyuan
AU - Vendrame, Nadia
AU - Kowalska, Natalia
AU - Leahy, Paul G.
AU - Alekseychik, Pavel
AU - Shi, Peili
AU - Weslien, Per
AU - Chen, Shiping
AU - Fares, Silvano
AU - Friborg, Thomas
AU - Tallec, Tiphaine
AU - Kato, Tomomichi
AU - Sachs, Torsten
AU - Maximov, Trofim
AU - di Cella, Umberto Morra
AU - Moderow, Uta
AU - Li, Yingnian
AU - He, Yongtao
AU - Kosugi, Yoshiko
AU - Luo, Geping
N1 - Publisher Copyright:
© 2023, Springer Nature Limited.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
U2 - 10.1038/s41597-023-02473-9
DO - 10.1038/s41597-023-02473-9
M3 - Journal article
C2 - 37679357
AN - SCOPUS:85170167023
VL - 10
JO - Scientific data
JF - Scientific data
SN - 2052-4463
M1 - 587
ER -
