Articles | Volume 14, issue 5
https://doi.org/10.5194/essd-14-2315-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-14-2315-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
13 May 2022
Data description paper |
| 13 May 2022
| 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
School of Remote Sensing and Information Engineering, Wuhan
University, Wuhan 430079, China
Department of Geographical Sciences, University of Maryland, College
Park, MD 20742, USA
Bo Jiang
CORRESPONDING AUTHOR
Faculty of Geographical Science, Beijing Normal University, Beijing
100875, China
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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.
Land surface all-wave net radiation (Rn) is a key parameter in many land processes. Current...
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