Articles | Volume 13, issue 11
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, Manuel Sanchez-Castillo, Jadunandan Dash, Peter M. Atkinson, and Jose Ojeda-Zujar

Data sets

CM SAF Surface Radiation MVIRI Data Set 1.0 R. Posselt, R. Müller, R. Stöckli, and J. Trentmann

CM SAF Meteosat Surface Radiation Daylight Data Set 1.0 R. Müller and J. Trentmann

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.
Final-revised paper