Articles | Volume 21, issue 2
https://doi.org/10.5194/bg-21-473-2024
https://doi.org/10.5194/bg-21-473-2024
Reviews and syntheses
 | 
25 Jan 2024
Reviews and syntheses |  | 25 Jan 2024

Reviews and syntheses: Remotely sensed optical time series for monitoring vegetation productivity

Lammert Kooistra, Katja Berger, Benjamin Brede, Lukas Valentin Graf, Helge Aasen, Jean-Louis Roujean, Miriam Machwitz, Martin Schlerf, Clement Atzberger, Egor Prikaziuk, Dessislava Ganeva, Enrico Tomelleri, Holly Croft, Pablo Reyes Muñoz, Virginia Garcia Millan, Roshanak Darvishzadeh, Gerbrand Koren, Ittai Herrmann, Offer Rozenstein, Santiago Belda, Miina Rautiainen, Stein Rune Karlsen, Cláudio Figueira Silva, Sofia Cerasoli, Jon Pierre, Emine Tanır Kayıkçı, Andrej Halabuk, Esra Tunc Gormus, Frank Fluit, Zhanzhang Cai, Marlena Kycko, Thomas Udelhoven, and Jochem Verrelst

Viewed

Total article views: 4,358 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
3,162 1,130 66 4,358 58 55
  • HTML: 3,162
  • PDF: 1,130
  • XML: 66
  • Total: 4,358
  • BibTeX: 58
  • EndNote: 55
Views and downloads (calculated since 19 Jun 2023)
Cumulative views and downloads (calculated since 19 Jun 2023)

Viewed (geographical distribution)

Total article views: 4,358 (including HTML, PDF, and XML) Thereof 4,206 with geography defined and 152 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 17 Jul 2024
Short summary
We reviewed optical remote sensing time series (TS) studies for monitoring vegetation productivity across ecosystems. Methods were categorized into trend analysis, land surface phenology, and assimilation into statistical or dynamic vegetation models. Due to progress in machine learning, TS processing methods will diversify, while modelling strategies will advance towards holistic processing. We propose integrating methods into a digital twin to improve the understanding of vegetation dynamics.
Altmetrics
Final-revised paper
Preprint