Articles | Volume 14, issue 5
Earth Syst. Sci. Data, 14, 2315–2341, 2022
https://doi.org/10.5194/essd-14-2315-2022
Earth Syst. Sci. Data, 14, 2315–2341, 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 et al.

Viewed

Total article views: 2,034 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
1,615 377 42 2,034 88 18 31
  • HTML: 1,615
  • PDF: 377
  • XML: 42
  • Total: 2,034
  • Supplement: 88
  • BibTeX: 18
  • EndNote: 31
Views and downloads (calculated since 17 Sep 2021)
Cumulative views and downloads (calculated since 17 Sep 2021)

Viewed (geographical distribution)

Total article views: 1,730 (including HTML, PDF, and XML) Thereof 1,730 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 27 Jun 2022
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.