Preprints
https://doi.org/10.5194/essd-2021-250
https://doi.org/10.5194/essd-2021-250

  17 Sep 2021

17 Sep 2021

Review status: this preprint is currently under review for the journal ESSD.

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

Jianglei Xu1, Shunlin Liang2, and Bo Jiang3 Jianglei Xu et al.
  • 1School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
  • 2Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA
  • 3Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China

Abstract. The surface radiation budget, also known as all-wave net radiation (Rn), is a key parameter for various land surface processes including hydrological, ecological, agricultural, and biogeochemical processes. Satellite data can be effectively used to estimate Rn, but existing satellite products have coarse spatial resolutions and limited temporal coverage. In this study, a point-surface matching estimation (PSME) method is proposed to estimate surface Rn using a residual convolutional neural network (RCNN) integrating spatially adjacent information to improve the accuracy of retrievals. A global high-resolution (0.05°) long-term (1981–2019) Rn product was subsequently generated from Advanced Very High-Resolution Radiometer (AVHRR) data. Specifically, the RCNN was employed to establish a nonlinear relationship between globally distributed ground measurements from 537 sites and AVHRR top of atmosphere (TOA) observations. Extended triplet collocation (ETC) technology was applied to address the spatial scale mismatch issue resulting from the low spatial support of ground measurements within the AVHRR footprint by selecting reliable sites for model training. The overall independent validation results show that the generated AVHRR Rn product is highly accurate, with R2, root-mean-square error (RMSE), and bias of 0.84, 26.66 Wm−2 (31.66 %), and 1.59 Wm−2 (1.89 %), respectively. Inter-comparisons with three other Rn products, i.e., the 5 km Global Land Surface Satellite (GLASS), the 1° Clouds and the Earth's Radiant Energy System (CERES), and the 0.5° × 0.625° Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA2), illustrate that our AVHRR Rn retrievals have the best accuracy under all of the considered surface and atmospheric conditions, especially thick cloud or hazy conditions. The spatiotemporal analyses of these four Rn datasets indicate that the AVHRR Rn product reasonably replicates the spatial pattern and temporal evolution trends of Rn observations. This dataset is freely available at https://doi.org/10.5281/zenodo.5509854 for 1981–2019 (Xu et al., 2021).

Jianglei Xu et al.

Status: open (until 26 Nov 2021)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • EC1: 'Comment on essd-2021-250', Christof Lorenz, 17 Sep 2021 reply
    • AC1: 'Reply on EC1', Xu Jianglei, 10 Oct 2021 reply

Jianglei Xu et al.

Data sets

Daily surface all-wave net radiation over global land (1981—2019) from AVHRR data Xu Jianglei; Liang Shunlin; Jiang Bo https://doi.org/10.5281/zenodo.5509854

Jianglei Xu et al.

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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 the coarse resolution, large uncertainty, and short time span. A deep learning method was used to obtain global surface Rn. A long-term Rn product was generated from 1981–2019 using AVHRR data. The product has the highest accuracy and a reasonable spatiotemporal variation compared to the other three products. Our product will play an important role in long-term climate change.