Send to

Choose Destination
Biogeosciences. 2017;14(18):4101-4124. doi: 10.5194/bg-14-4101-2017. Epub 2017 Sep 20.

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.

Author information

Department of Earth and Environmental Engineering, Columbia University, New York, 10027, USA.
Columbia Water Center, Columbia University, New York, 10027, USA.
Department of Earth System Science, Stanford University, Stanford, 94305, USA.
Observatoire de Paris, Paris, 75014, France.
Universities Space Research Association/NPP, Columbia, MD, 21046, USA.
Global Modeling and Assimilation Office, NASA Goddard Spaceflight Center, Greenbelt, MD, 20771, USA.
Department of Earth Sciences, VU University Amsterdam, Amsterdam, 1081HV, The Netherlands.
Laboratory of Hydrology and Water Management, Ghent University, Ghent, B-9000, Belgium.
Earth Institute, Columbia University, New York, 10027, USA.


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 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 on 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 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. Analysing WECANN retrievals across three extreme drought and heatwave events demonstrates the capability of the retrievals in capturing the extent of these events. Uncertainty estimates of the retrievals are analysed and the inter-annual variability in average global and regional fluxes show the impact of distinct climatic events - such as the 2015 El Niño - on surface turbulent fluxes and GPP.

Supplemental Content

Full text links

Icon for PubMed Central
Loading ...
Support Center