Abstract
Surface upward longwave radiation (SULR) is a critical component in the calculation of the Earth’s surface radiation budget. Multiple clear-sky SULR estimation methods have been developed for high-spatial resolution satellite observations. Here, we comprehensively evaluated six SULR estimation methods, including the temperature-emissivity physical methods with the input of the MYD11/MYD21 (TE-MYD11/TE-MYD21), the hybrid methods with top-of-atmosphere (TOA) linear/nonlinear/artificial neural network regressions (TOA-LIN/TOA-NLIN/TOA-ANN), and the hybrid method with bottom-of-atmosphere (BOA) linear regression (BOA-LIN). The recently released MYD21 product and the BOA-LIN—a newly developed method that considers the spatial heterogeneity of the atmosphere—is used initially to estimate SULR. In addition, the four hybrid methods were compared with simulated datasets. All the six methods were evaluated using the Moderate Resolution Imaging Spectroradiometer (MODIS) products and the Surface Radiation Budget Network (SURFRAD) in situ measurements. Simulation analysis shows that the BOA-LIN is the best one among four hybrid methods with accurate atmospheric profiles as input. Comparison of all the six methods shows that the TE-MYD21 performed the best, with a root mean square error (RMSE) and mean bias error (MBE) of 14.0 and −0.2 W/m2, respectively. The RMSE of BOA-LIN, TOA-NLIN, TOA-LIN, TOA-ANN, and TE-MYD11 are equal to 15.2, 16.1, 17.2, 21.2, and 18.5 W/m2, respectively. TE-MYD21 has a much better accuracy than the TE-MYD11 over barren surfaces (NDVI < 0.3) and a similar accuracy over non-barren surfaces (NDVI ≥ 0.3). BOA-LIN is more stable over varying water vapor conditions, compared to other hybrid methods. We conclude that this study provides a valuable reference for choosing the suitable estimation method in the SULR product generation.
Highlights
The Earth’s surface radiation budget (SRB) is a driving factor in the exchange of energy between the atmosphere and oceans/land [1]
The evaluation results for the four hybrid methods (TOA-LIN, TOA-NLIN, TOA-Artificial Neural Network (ANN), and BOA and linear regression (BOA-LIN)) based on the simulation datasets are presented
The scatterplots of the TOA hybrid methods show that the bias increases with an increase of surface upward longwave radiation (SULR) values, while the BOA-LIN method has a relatively stable bias distribution at all SULR range
Summary
The Earth’s surface radiation budget (SRB) is a driving factor in the exchange of energy between the atmosphere and oceans/land [1]. The surface upward longwave radiation (SULR; the total surface upward radiative flux in the 4 to 100 μm spectral domain) is one of the four SRB components, and is the dominant component at night, high latitude, and during most times of the year in polar regions [3,4]. Broadband satellite sensors which can directly measure the earth’s radiation up to 100 μm (e.g., Clouds and the Earth’s Radiant Energy System (CERES) [7,8]) are used for the generation of surface radiative flux products. Several methods have been developed for instantaneous SULR estimation under clear-sky conditions from these fine spatial resolution satellite observations. The SULR estimation methods proposed for multispectral sensors can be grouped into two categories
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