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• W4200375863 abstract "<strong class=journal-contentHeaderColor>Abstract.</strong> The surface radiation budget, also known as all-wave net radiation (<span class=inline-formula><i>R</i>_n</span>), is a key parameter for various land surface processes including hydrological, ecological, agricultural, and biogeochemical processes. Satellite data can be effectively used to estimate <span class=inline-formula><i>R</i>_n</span>, 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 <span class=inline-formula><i>R</i>_n</span> using a residual convolutional neural network (RCNN) integrating spatially adjacent information to improve the accuracy of retrievals. A global high-resolution (0.05<span class=inline-formula>^∘</span>), long-term (1981–2019), and daily mean <span class=inline-formula><i>R</i>_n</span> 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 522 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 <span class=inline-formula><i>R</i>_n</span> product is highly accurate, with <span class=inline-formula><i>R</i>²</span>, root-mean-square error (RMSE), and bias of 0.84, 26.77 W m<span class=inline-formula>^−2</span> (31.54 %), and 1.16 W m<span class=inline-formula>^−2</span> (1.37 %), respectively. Inter-comparisons with three other <span class=inline-formula><i>R</i>_n</span> products, i.e., the 5 km Global Land Surface Satellite (GLASS); the 1<span class=inline-formula>^∘</span> Clouds and the Earth's Radiant Energy System (CERES); and the 0.5<span class=inline-formula>^∘</span> <span class=inline-formula>×</span> 0.625<span class=inline-formula>^∘</span> Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2), illustrate that our AVHRR <span class=inline-formula><i>R</i>_n</span> retrievals have the best accuracy under most of the considered surface and atmospheric conditions, especially thick-cloud or hazy conditions. However, the performance of the model needs to be further improved for the snow/ice cover surface. The spatiotemporal analyses of these four <span class=inline-formula><i>R</i>_n</span> datasets indicate that the AVHRR <span class=inline-formula><i>R</i>_n</span> product reasonably replicates the spatial pattern and temporal evolution trends of <span class=inline-formula><i>R</i>_n</span> observations. The long-term record (1981–2019) of the AVHRR <span class=inline-formula><i>R</i>_n</span> product shows its value in climate change studies. This dataset is freely available at <a href=https://doi.org/10.5281/zenodo.5546316>https://doi.org/10.5281/zenodo.5546316</a> for 1981–2019 (Xu et al., 2021)." @default.
• W4200375863 created "2021-12-31" @default.
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• W4200375863 date "2021-11-29" @default.
• W4200375863 modified "2023-10-15" @default.
• W4200375863 title "Reply on RC1" @default.
• W4200375863 doi "https://doi.org/10.5194/essd-2021-250-ac2" @default.
• W4200375863 hasPublicationYear "2021" @default.
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