Articles | Volume 13, issue 11
Biogeosciences, 13, 3305–3317, 2016
https://doi.org/10.5194/bg-13-3305-2016
Biogeosciences, 13, 3305–3317, 2016
https://doi.org/10.5194/bg-13-3305-2016

Research article 06 Jun 2016

Research article | 06 Jun 2016

Modelling interannual variation in the spring and autumn land surface phenology of the European forest

Victor F. Rodriguez-Galiano et al.

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Cited articles

Archetti, M., Richardson, A. D., O'Keefe, J., and Delpierre, N.: Predicting Climate Change Impacts on the Amount and Duration of Autumn Colors in a New England Forest, PLoS ONE, 8, e57373, https://doi.org/10.1371/journal.pone.0057373, 2013.
Archibald, S., Roy, D. P., van Wilgen, B. W., and Scholes, R. J.: What limits fire? An examination of drivers of burnt area in Southern Africa, Glob. Change Biol., 15, 613–630, 2009.
Atkinson, P. M., Jeganathan, C., Dash, J., and Atzberger, C.: Inter-comparison of four models for smoothing satellite sensor time-series data to estimate vegetation phenology, Remote Sens. Environ., 123, 400–417, 2012.
Barriopedro, D., Fischer, E. M., Luterbacher, J., Trigo, R. M., and García-Herrera, R.: The Hot Summer of 2010: Redrawing the Temperature Record Map of Europe, Science, 332, 220–224, 2011.
Bicheron, P., Amberg, V., Bourg, L., Petit, D., Huc, M., Miras, B., Brockmann, C., Hagolle, O., Delwart, S., Ranera, F., Leroy, M., and Arino, O.: Geolocation Assessment of MERIS GlobCover Orthorectified Products, IEEE T. Geosci. Remote, 49, 2972–2982, 2011.
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Short summary
This research reveals new insights into the weather drivers of land surface phenology (LSP) across the entire European forest, while at the same time it establishes a new conceptual framework for modelling LSP. Specifically, a sophisticated machine learning regression method (RF) was introduced for LSP modelling across very large areas and across multiple years simultaneously. The RF models explained 81 and 62 % of the variance in the spring and autumn LSP interannual variation.
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