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

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Cited articles

Augustine, J. A., DeLuisi, J. J., and Long, C. N.: SURFRAD – A national surface radiation budget network for atmospheric research, B. Am. Meteorol. Soc., 81, 2341–2358, 2000. 
Ball, J. E., Anderson, D. T., and Chan, C. S.: Comprehensive survey of deep learning in remote sensing: theories, tools, and challenges for the community, J. Appl. Remote Sens., 11, 042609, https://doi.org/10.1117/1.JRS.11.042609, 2017. 
Barker, H. W. and Li, Z.: Interpreting shortwave albedo-transmittance plots: True or apparent anomalous absorption?, Geophys. Res. Lett., 24, 2023–2026, 1997. 
Betts, A. K., Zhao, M., Dirmeyer, P., and Beljaars, A.: Comparison of ERA40 and NCEP/DOE near-surface data sets with other ISLSCP-II data sets, J. Geophys. Res.-Atmos., 111, D22S04, https://doi.org/10.1029/2006JD007174, 2006. 
Bisht, G. and Bras, R. L.: Estimation of net radiation from the MODIS data under all sky conditions: Southern Great Plains case study, Remote Sens. Environ., 114, 1522–1534, 2010. 
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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.
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