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.

Data sets

CM SAF Surface Radiation MVIRI Data Set 1.0 R. Posselt, R. Müller, R. Stöckli, and J. Trentmann https://doi.org/10.5676/EUM_SAF_CM/RAD_MVIRI/V001

CM SAF Meteosat Surface Radiation Daylight Data Set 1.0 R. Müller and J. Trentmann https://doi.org/10.5676/EUM_SAF_CM/DAL_MVIRI_SEVIRI/V001

Download
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.
Altmetrics
Final-revised paper
Preprint