Articles | Volume 14, issue 5
https://doi.org/10.5194/essd-14-2315-2022
https://doi.org/10.5194/essd-14-2315-2022
Data description paper
 | 
13 May 2022
Data description paper |  | 13 May 2022

A global long-term (1981–2019) daily land surface radiation budget product from AVHRR satellite data using a residual convolutional neural network

Jianglei Xu, Shunlin Liang, and Bo Jiang

Viewed

Total article views: 4,455 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
3,348 985 122 4,455 278 82 95
  • HTML: 3,348
  • PDF: 985
  • XML: 122
  • Total: 4,455
  • Supplement: 278
  • BibTeX: 82
  • EndNote: 95
Views and downloads (calculated since 17 Sep 2021)
Cumulative views and downloads (calculated since 17 Sep 2021)

Viewed (geographical distribution)

Total article views: 4,455 (including HTML, PDF, and XML) Thereof 4,034 with geography defined and 421 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 02 Nov 2024
Download
Short summary
Land surface all-wave net radiation (Rn) is a key parameter in many land processes. Current products have drawbacks of coarse resolutions, large uncertainty, and short time spans. A deep learning method was used to obtain global surface Rn. A long-term Rn product was generated from 1981 to 2019 using AVHRR data. The product has the highest accuracy and a reasonable spatiotemporal variation compared to three other products. Our product will play an important role in long-term climate change.
Altmetrics
Final-revised paper
Preprint