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).
Highlights
Net radiation (Rn), which characterizes the surface radiation budget, is the difference between downward and upward radiation across the shortwave (0.3–3.0 μm) and longwave (3.0–100 μm) spectra
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
No site was considered reliable for some observation networks/programs, namely ChinaFlux, Greenland Climate Network (GCNET), GAME.ANN, HiWATER, IMAU-Ktransect, and LBA-ECO
Summary
Net radiation (Rn), which characterizes the surface radiation budget, is the difference between downward and upward radiation across the shortwave (0.3–3.0 μm) and longwave (3.0–100 μm) spectra. Historical Rn and surface radiative components have been measured at ground meteorological stations These ground-based measurements are widely used to study spatiotemporal variations in regional surface radiation and to evaluate gridded products (Jia et al, 2018; Zhang et al, 2020; Zhang et al, 2015). The high cost of maintaining radiation radiometers means that stations are sparely distributed, severely hindering our ability to study and understand the spatiotemporal variability of surface Rn at global scale. The look-up-table (LUT) and parameterization methods are two typical physical schemes that are widely used to estimate surface radiation from satellite data. To address the low computational efficiency of the radiative transfer model (RTM), the LUT method was proposed to estimate the surface radiation from satellite top of atmosphere (TOA) observations, which combines the advantages of RTM-
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