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.; Brummer, 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.; Sigut, L.; Klemedtsson, L.; Tezza, L.; Hortnagl, 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.

doi:10.1038/s41597-023-02473-9

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.

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.; Brummer, 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.; Sigut, L.; Klemedtsson, L.; Tezza, L.; Hortnagl, 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.. - In: SCIENTIFIC DATA. - ISSN 2052-4463. - 10:(2023), pp. 58701-58718. [10.1038/s41597-023-02473-9]

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.;Brummer, 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.;Sigut, L.;Klemedtsson, L.;Tezza, L.;Hortnagl, 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.

2023-01-01

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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	Anno di pubblicazione (Date of publication)
	
				2023
			
	Titolo del periodico (Journal title)
	
				SCIENTIFIC DATA
			
	DOI
	
				https://dx.doi.org/10.1038/s41597-023-02473-9
			
	Codice PubMed (PubMed Identifier)
	
				37679357
			
	Codice Scopus (Scopus identifier)
	
				2-s2.0-85170167023
			
	Codice WOS (WOS identifier)
	
				WOS:001065045600002
			
	Tutti gli autori
	
						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.; Frank...espandi
						
	Citazione
	
				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.; Brummer, 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.; Sigut, L.; Klemedtsson, L.; Tezza, L.; Hortnagl, 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.. - In: SCIENTIFIC DATA. - ISSN 2052-4463. - 10:(2023), pp. 58701-58718. [10.1038/s41597-023-02473-9]
			
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