Ranking drivers of global carbon and energy fluxes over land

Gustau Camps-Valls, Martin Jung, Kazuhito Ichii, Dario Papale, Gianluca Tramontana, Paul Bodesheim, Christopher R Schwalm, Jakob Zscheischler, Miguel Mahecha, Markus Reichstein

Research output: Chapter in Book/Report/Conference proceedingConference contribution

10 Scopus citations

Abstract

The accurate estimation of carbon and heat fluxes at global scale is paramount for future policy decisions in the context of global climate change. This paper analyzes the relative relevance of potential remote sensing and meteorological drivers of global carbon and energy fluxes over land. The study is done in an indirect way via upscaling both Gross Primary Production (GPP) and latent energy (LE) using Gaussian Process regression (GPR). In summary, GPR is successfully compared to multivariate linear regression (RMSE gain of +4.17% in GPP and +7.63% in LE) and kernel ridge regression (+2.91% in GPP and +3.07% in LE). The best GP models are then studied in terms of explanatory power based on the analysis of the lengthscales of the anisotropic covariance function, sensitivity maps of the predictive mean, and the robustness to distortions in the input variables. It is concluded that GPP is predominantly mediated by several vegetation indices and land surface temperature (LST), while LE is mostly driven by LST, global radiation and vegetation indices.

Original languageEnglish (US)
Title of host publicationInternational Geoscience and Remote Sensing Symposium (IGARSS)
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages4416-4419
Number of pages4
Volume2015-November
ISBN (Print)9781479979295
DOIs
StatePublished - Nov 10 2015
Externally publishedYes
EventIEEE International Geoscience and Remote Sensing Symposium, IGARSS 2015 - Milan, Italy
Duration: Jul 26 2015Jul 31 2015

Other

OtherIEEE International Geoscience and Remote Sensing Symposium, IGARSS 2015
CountryItaly
CityMilan
Period7/26/157/31/15

Keywords

  • carbon
  • energy
  • feature ranking
  • Gaussian process
  • global monitoring
  • GPP
  • regression

ASJC Scopus subject areas

  • Earth and Planetary Sciences(all)
  • Computer Science Applications

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