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.

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Cited articles

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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.