Articles | Volume 15, issue 11
https://doi.org/10.5194/essd-15-4877-2023
https://doi.org/10.5194/essd-15-4877-2023
Data description paper
 | 
01 Nov 2023
Data description paper |  | 01 Nov 2023

Spatiotemporally consistent global dataset of the GIMMS leaf area index (GIMMS LAI4g) from 1982 to 2020

Sen Cao, Muyi Li, Zaichun Zhu, Zhe Wang, Junjun Zha, Weiqing Zhao, Zeyu Duanmu, Jiana Chen, Yaoyao Zheng, Yue Chen, Ranga B. Myneni, and Shilong Piao

Viewed

Total article views: 5,824 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
4,479 1,265 80 5,824 85 45 94
  • HTML: 4,479
  • PDF: 1,265
  • XML: 80
  • Total: 5,824
  • Supplement: 85
  • BibTeX: 45
  • EndNote: 94
Views and downloads (calculated since 24 Feb 2023)
Cumulative views and downloads (calculated since 24 Feb 2023)

Viewed (geographical distribution)

Total article views: 5,824 (including HTML, PDF, and XML) Thereof 5,628 with geography defined and 196 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 22 Apr 2024
Download
Short summary
The long-term global leaf area index (LAI) products are critical for characterizing vegetation dynamics under environmental changes. This study presents an updated GIMMS LAI product (GIMMS LAI4g; 1982−2020) based on PKU GIMMS NDVI and massive Landsat LAI samples. With higher accuracy than other LAI products, GIMMS LAI4g removes the effects of orbital drift and sensor degradation in AVHRR data. It has better temporal consistency before and after 2000 and a more reasonable global vegetation trend.
Altmetrics
Final-revised paper
Preprint