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RESEARCH PRODUCT
Understanding Climate Impacts on Vegetation with Gaussian Processes in Granger Causality
Miguel Morata-dolzDiego BuesoMaria PilesGustau Camps-vallssubject
FOS: Computer and information sciencesPhysics - Atmospheric and Oceanic PhysicsComputer Science - Machine LearningAtmospheric and Oceanic Physics (physics.ao-ph)FOS: Physical sciencesMachine Learning (cs.LG)description
Global warming is leading to unprecedented changes in our planet, with great societal, economical and environmental implications, especially with the growing demand of biofuels and food. Assessing the impact of climate on vegetation is of pressing need. We approached the attribution problem with a novel nonlinear Granger causal (GC) methodology and used a large data archive of remote sensing satellite products, environmental and climatic variables spatio-temporally gridded over more than 30 years. We generalize kernel Granger causality by considering the variables cross-relations explicitly in Hilbert spaces, and use the covariance in Gaussian processes. The method generalizes the linear and kernel GC methods, and comes with tighter bounds of performance based on Rademacher complexity. Spatially-explicit global Granger footprints of precipitation and soil moisture on vegetation greenness are identified more sharply than previous GC methods.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2020-12-06 |