Journal cover Journal topic
Biogeosciences An interactive open-access journal of the European Geosciences Union
Journal topic
Volume 14, issue 18
Biogeosciences, 14, 4101-4124, 2017
https://doi.org/10.5194/bg-14-4101-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
Biogeosciences, 14, 4101-4124, 2017
https://doi.org/10.5194/bg-14-4101-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 20 Sep 2017

Research article | 20 Sep 2017

Water, Energy, and Carbon with Artificial Neural Networks (WECANN): a statistically based estimate of global surface turbulent fluxes and gross primary productivity using solar-induced fluorescence

Seyed Hamed Alemohammad1,2, Bin Fang1,2, Alexandra G. Konings3, Filipe Aires1,4, Julia K. Green1,2, Jana Kolassa5,6, Diego Miralles7, Catherine Prigent1,6, and Pierre Gentine1,2,8 Seyed Hamed Alemohammad et al.
  • 1Department of Earth and Environmental Engineering, Columbia University, New York, 10027, USA
  • 2Columbia Water Center, Columbia University, New York, 10027, USA
  • 3Department of Earth System Science, Stanford University, Stanford, 94305, USA
  • 4Observatoire de Paris, Paris, 75014, France
  • 5Universities Space Research Association/NPP, Columbia, MD, 21046, USA
  • 6Global Modeling and Assimilation Office, NASA Goddard Spaceflight Center, Greenbelt, MD, 20771, USA
  • 7Laboratory of Hydrology and Water Management, Ghent University, Ghent, 9000, Belgium
  • 8Earth Institute, Columbia University, New York, 10027, USA

Abstract. A new global estimate of surface turbulent fluxes, latent heat flux (LE) and sensible heat flux (H), and gross primary production (GPP) is developed using a machine learning approach informed by novel remotely sensed solar-induced fluorescence (SIF) and other radiative and meteorological variables. This is the first study to jointly retrieve LE, H, and GPP using SIF observations. The approach uses an artificial neural network (ANN) with a target dataset generated from three independent data sources, weighted based on a triple collocation (TC) algorithm. The new retrieval, named Water, Energy, and Carbon with Artificial Neural Networks (WECANN), provides estimates of LE, H, and GPP from 2007 to 2015 at 1° × 1° spatial resolution and at monthly time resolution. The quality of ANN training is assessed using the target data, and the WECANN retrievals are evaluated using eddy covariance tower estimates from the FLUXNET network across various climates and conditions. When compared to eddy covariance estimates, WECANN typically outperforms other products, particularly for sensible and latent heat fluxes. Analyzing WECANN retrievals across three extreme drought and heat wave events demonstrates the capability of the retrievals to capture the extent of these events. Uncertainty estimates of the retrievals are analyzed and the interannual variability in average global and regional fluxes shows the impact of distinct climatic events – such as the 2015 El Niño – on surface turbulent fluxes and GPP.

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Water, Energy, and Carbon with Artificial Neural Networks (WECANN) is a statistically based estimate of global surface latent and sensible heat fluxes and gross primary productivity. The retrieval uses six remotely sensed observations as input, including the solar-induced fluorescence. WECANN provides estimates on a 1° × 1° geographic grid and on a monthly time scale and outperforms other global products in capturing the seasonality of the fluxes when compared to eddy covariance tower data.
Water, Energy, and Carbon with Artificial Neural Networks (WECANN) is a statistically based...
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