Articles | Volume 14, issue 18
https://doi.org/10.5194/bg-14-4101-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/bg-14-4101-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
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 Alemohammad
Department of Earth and Environmental Engineering, Columbia
University, New York, 10027, USA
Columbia Water Center, Columbia University, New York, 10027, USA
Bin Fang
Department of Earth and Environmental Engineering, Columbia
University, New York, 10027, USA
Columbia Water Center, Columbia University, New York, 10027, USA
Alexandra G. Konings
Department of Earth System Science, Stanford University, Stanford,
94305, USA
Filipe Aires
Department of Earth and Environmental Engineering, Columbia
University, New York, 10027, USA
Observatoire de Paris, Paris, 75014, France
Julia K. Green
Department of Earth and Environmental Engineering, Columbia
University, New York, 10027, USA
Columbia Water Center, Columbia University, New York, 10027, USA
Jana Kolassa
Universities Space Research Association/NPP, Columbia, MD, 21046, USA
Global Modeling and Assimilation Office, NASA Goddard Spaceflight
Center, Greenbelt, MD, 20771, USA
Diego Miralles
Laboratory of Hydrology and Water Management, Ghent University, Ghent,
9000, Belgium
Catherine Prigent
Department of Earth and Environmental Engineering, Columbia
University, New York, 10027, USA
Global Modeling and Assimilation Office, NASA Goddard Spaceflight
Center, Greenbelt, MD, 20771, USA
Pierre Gentine
CORRESPONDING AUTHOR
Department of Earth and Environmental Engineering, Columbia
University, New York, 10027, USA
Columbia Water Center, Columbia University, New York, 10027, USA
Earth Institute, Columbia University, New York, 10027, USA
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124 citations as recorded by crossref.
- Evaluation of remotely sensed global evapotranspiration data from inland river basins Z. Liu https://doi.org/10.1002/hyp.14774
- Soil-Permittivity Estimation Under Grassland Using Machine-Learning and Polarimetric Decomposition Techniques R. Hansch et al. https://doi.org/10.1109/TGRS.2020.3010104
- An improved light use efficiency model by considering canopy nitrogen concentrations and multiple environmental factors H. Zhang et al. https://doi.org/10.1016/j.agrformet.2023.109359
- Artificial neural networks estimate evapotranspiration for Miscanthus × giganteus as effectively as empirical model but with fewer inputs G. Aslan-Sungur et al. https://doi.org/10.1007/s00704-025-05812-5
- Land–atmosphere interactions in the tropics – a review P. Gentine et al. https://doi.org/10.5194/hess-23-4171-2019
- Remote sensing of solar-induced chlorophyll fluorescence (SIF) in vegetation: 50 years of progress G. Mohammed et al. https://doi.org/10.1016/j.rse.2019.04.030
- Reconstructed Solar‐Induced Fluorescence: A Machine Learning Vegetation Product Based on MODIS Surface Reflectance to Reproduce GOME‐2 Solar‐Induced Fluorescence P. Gentine & S. Alemohammad https://doi.org/10.1002/2017GL076294
- Partitioning of Historical Precipitation Into Evaporation and Runoff Based on Hydrologic Dynamics Identified With Recent SMAP Satellite Measurements R. Akbar et al. https://doi.org/10.1029/2020WR027307
- Contrasting responses of vegetation productivity to intraseasonal rainfall in Earth system models B. Harris et al. https://doi.org/10.5194/esd-15-1019-2024
- Comprehensive analysis of methods for estimating actual paddy evapotranspiration—A review K. Behura et al. https://doi.org/10.3389/frwa.2025.1553732
- Artificial Neural Network Model of Soil Heat Flux over Multiple Land Covers in South America B. de Andrade et al. https://doi.org/10.3390/rs13122337
- A 1 km Global Carbon Flux Dataset Using In Situ Measurements and Deep Learning W. Shangguan et al. https://doi.org/10.3390/f14050913
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Short summary
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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