A data-driven model of the state of the African biosphere Martin Jung, Eric Thomas.

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Presentation transcript:

A data-driven model of the state of the African biosphere Martin Jung, Eric Thomas

Meteorology Land use Biosphere- atmosphere exchange (FLUXNET) Biosphere- atmosphere exchange (FLUXNET) Vegetation state from satellites Empirical ‘upscaling’ of FLUXNET Pinty et al 2011 Explanatory (X) variablesTarget (Y) variable

Meteorology (7 x 4) Land use (8) Soil (10) Remotely sensed fAPAR Remotely sensed fAPAR Mean annual Mean seasonal cycle Anomalies Raw Random forests Empirical modelling of phenology Explanatory (X) variablesTarget (Y) variable

Approach Training with all 48 variables Time period: Prediction of fAPAR for Evaluation of predicted fAPAR Case Study Africa

Results

Forecasting of anomalies Results

Improving modelling of anomalies Meteorology (7 x x 24 ) Land use (8) Soil (10) Remotely sensed fAPAR Remotely sensed fAPAR Mean annual Mean seasonal cycle Anomalies Raw Random forests Lag Cumulativ e Lag Lag Cumulativ e Lag Fire fAPAR Mean season al cycle Variance of anomalies Variable selection based on Genetic Algorithm Back to methods …

Forecasting of anomalies improved oldnew New Results

Potential applications -In ‘early warning systems’ by using seasonal weather forecasts -In empirical ‘upscaling’ of FLUXNET data beyond satellite era -In Land Surface Models Development of a fully data-driven phenology model

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