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

Modelling interannual variation in the spring and autumn land surface phenology of the European forest
Modelling interannual variation in the spring and autumn land surface phenology of the European forest
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.
1726-4170
3305-3317
Rodriguez Galiano, Victor F.
88495556-2795-456d-b972-31ca79fe4a71
Sanchez-Castillo, Manuel
1ceee7a7-1219-45a6-9660-32893fa54892
Dash, Jadunandan
51468afb-3d56-4d3a-aace-736b63e9fac8
Atkinson, Peter
96e96579-56fe-424d-a21c-17b6eed13b0b
Ojeda-Zujar, Jose
8660961d-b658-4c2f-a27f-0dc9699fb367
Rodriguez Galiano, Victor F.
88495556-2795-456d-b972-31ca79fe4a71
Sanchez-Castillo, Manuel
1ceee7a7-1219-45a6-9660-32893fa54892
Dash, Jadunandan
51468afb-3d56-4d3a-aace-736b63e9fac8
Atkinson, Peter
96e96579-56fe-424d-a21c-17b6eed13b0b
Ojeda-Zujar, Jose
8660961d-b658-4c2f-a27f-0dc9699fb367

Rodriguez Galiano, Victor F., Sanchez-Castillo, Manuel, Dash, Jadunandan, Atkinson, Peter and Ojeda-Zujar, Jose (2016) Modelling interannual variation in the spring and autumn land surface phenology of the European forest. Biogeosciences, 13, 3305-3317. (doi:10.5194/bg-13-3305-2016).

Record type: Article

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.

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Accepted/In Press date: 16 May 2016
e-pub ahead of print date: 6 June 2016
Organisations: Global Env Change & Earth Observation

Identifiers

Local EPrints ID: 396536
URI: http://eprints.soton.ac.uk/id/eprint/396536
ISSN: 1726-4170
PURE UUID: a3d1ffbc-a932-4c10-8588-55995b99851b
ORCID for Jadunandan Dash: ORCID iD orcid.org/0000-0002-5444-2109
ORCID for Peter Atkinson: ORCID iD orcid.org/0000-0002-5489-6880

Catalogue record

Date deposited: 10 Jun 2016 12:36
Last modified: 07 Oct 2020 03:42

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Contributors

Author: Victor F. Rodriguez Galiano
Author: Manuel Sanchez-Castillo
Author: Jadunandan Dash ORCID iD
Author: Peter Atkinson ORCID iD
Author: Jose Ojeda-Zujar

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