Articles | Volume 15, issue 19
https://doi.org/10.5194/bg-15-5779-2018
https://doi.org/10.5194/bg-15-5779-2018
Research article
 | 
02 Oct 2018
Research article |  | 02 Oct 2018

A global spatially contiguous solar-induced fluorescence (CSIF) dataset using neural networks

Yao Zhang, Joanna Joiner, Seyed Hamed Alemohammad, Sha Zhou, and Pierre Gentine

Viewed

Total article views: 15,564 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
11,681 3,718 165 15,564 598 181 265
  • HTML: 11,681
  • PDF: 3,718
  • XML: 165
  • Total: 15,564
  • Supplement: 598
  • BibTeX: 181
  • EndNote: 265
Views and downloads (calculated since 22 Jun 2018)
Cumulative views and downloads (calculated since 22 Jun 2018)

Viewed (geographical distribution)

Total article views: 15,564 (including HTML, PDF, and XML) Thereof 14,599 with geography defined and 965 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 13 Nov 2025
Download
Short summary
Using satellite reflectance measurements and a machine learning algorithm, we generated a new solar-induced chlorophyll fluorescence (SIF) dataset that is closely linked to plant photosynthesis. This new dataset has higher spatial and temporal resolutions, and lower uncertainty compared to the existing satellite retrievals. We also demonstrated its application in monitoring drought and improving the understanding of the SIF–photosynthesis relationship.
Share
Altmetrics
Final-revised paper
Preprint