Articles | Volume 14, issue 18
https://doi.org/10.5194/bg-14-4101-2017
https://doi.org/10.5194/bg-14-4101-2017
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, Bin Fang, Alexandra G. Konings, Filipe Aires, Julia K. Green, Jana Kolassa, Diego Miralles, Catherine Prigent, and Pierre Gentine

Viewed

Total article views: 6,888 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
4,488 2,257 143 6,888 561 96 156
  • HTML: 4,488
  • PDF: 2,257
  • XML: 143
  • Total: 6,888
  • Supplement: 561
  • BibTeX: 96
  • EndNote: 156
Views and downloads (calculated since 18 Nov 2016)
Cumulative views and downloads (calculated since 18 Nov 2016)

Viewed (geographical distribution)

Total article views: 6,888 (including HTML, PDF, and XML) Thereof 6,540 with geography defined and 348 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Discussed (preprint)

Latest update: 18 Jun 2024
Download
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.
Altmetrics
Final-revised paper
Preprint