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

Victor F Rodriguez-Galiano,Jose Ojeda-Zujar,Jadunandan Dash,Peter M Atkinson,Manuel Sanchez-Castillo

doi:10.5194/bg-13-3305-2016

Abstract

Abstract. This research reveals new insights into the weather drivers of interannual variation in land surface phenology (LSP) across the entire European forest, while at the same time establishes a new conceptual framework for predictive modelling of LSP. Specifically, the random-forest (RF) method, a multivariate, spatially non-stationary and non-linear machine learning approach, was introduced for phenological modelling across very large areas and across multiple years simultaneously: the typical case for satellite-observed LSP. The RF model was fitted to the relation between LSP interannual variation and numerous climate predictor variables computed at biologically relevant rather than human-imposed temporal scales. In addition, the legacy effect of an advanced or delayed spring on autumn phenology was explored. The RF models explained 81 and 62 % of the variance in the spring and autumn LSP interannual variation, with relative errors of 10 and 20 %, respectively: a level of precision that has until now been unobtainable at the continental scale. Multivariate linear regression models explained only 36 and 25 %, respectively. It also allowed identification of the main drivers of the interannual variation in LSP through its estimation of variable importance. This research, thus, shows an alternative to the hitherto applied linear regression approaches for modelling LSP and paves the way for further scientific investigation based on machine learning methods.

Highlights

Vegetation phenology has emerged as an important focus for scientific research in the last few decades
Numerous models were built on the basis of different predictor combinations considering different temporal windows prior to the spring and autumn phenological events
We did not carry out an exhaustive analysis of the optimum growing degree days (GDDs) parametrization, our results showed a systematic pattern in spring models, presenting slightly larger pseudo-R2 for models which used 0 ◦C as a threshold for the computation of GDD

Summary

Introduction

Vegetation phenology has emerged as an important focus for scientific research in the last few decades. Different approaches have been devised for the study of vegetation phenology (Rafferty et al, 2013), the characterization and modelling of vegetation phenology at global or regional scales has been undertaken mainly through the use of longterm time series of satellite-sensor vegetation indices (termed land surface phenology, LSP, to reflect that satellite-observed phenology includes all land covers). Most studies of LSP analyse trends in phenological events across years (Delbart et al, 2008; Jeganathan et al, 2014; Jeong et al, 2011; Karlsen et al, 2007; Myneni et al, 1997), but more recent studies present process-based models to uncover cause–effect relationships between long-term trends in phenology and its key driving variables (Ivits et al, 2012; Maignan et al, 2008a, b; Stöckli et al, 2008, 2011; Yu et al, 2016; Zhou et al, 2001). Rodriguez-Galiano et al.: A new approach for modelling changes in phenology

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