DSpace Unitus DSpace

Unitus DSpace >
Dipartimento per l’Innovazione dei sistemi biologici, agroalimentari e forestali >
DIBAF - Archivio della produzione scientifica >

Please use this identifier to cite or link to this item: http://hdl.handle.net/2067/2925

Title: Predicting carbon dioxide and energy fluxes across global FLUXNET sites with regression algorithms
Authors: Tramontana, Gianluca
Jung, Martin
Schwalm, Christopher R.
Ichii, Kazuhito
Camps-Valls, Gustau
Ráduly, Botond
Reichstein, Markus
Arain, M. Altaf
Cescatti, Alessandro
Kiely, Gerard
Merbold, Lutz
Serrano-Ortiz, Penelope
Sickert, Sven
Wolf, Sebastian
Papale, Dario
Keywords: Machine learning
Carbon fluxes
Energy fluxes
Remote sensing
Issue Date: 2016
Publisher: Copernicus Publications
Citation: Tramontana, G. et al. 2016. Predicting carbon dioxide and energy fluxes across global FLUXNET sites with regression algorithms. "Biogeosciences" 13 (14): 4291–4313
Abstract: Spatio-temporal fields of land–atmosphere fluxes derived from data-driven models can complement simulations by process-based land surface models. While a number of strategies for empirical models with eddy-covariance flux data have been applied, a systematic intercomparison of these methods has been missing so far. In this study, we performed a cross-validation experiment for predicting carbon dioxide, latent heat, sensible heat and net radiation fluxes across different ecosystem types with 11 m
DOI: 10.5194/bg-13-4291-2016
URI: http://hdl.handle.net/2067/2925
ISSN: 1726-4170 (print)
1726-4189 (online)
Appears in Collections:DIBAF - Archivio della produzione scientifica

Files in This Item:

File Description SizeFormat
gtramontana.pdf2.4 MBAdobe PDFView/Open

This item is protected by original copyright

Recommend this item

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.


Valid XHTML 1.0! Unitus DSpace  © 2005 Università degli Studi della Tuscia - Feedback