A synergic study on estimating surface downward shortwave radiation from satellite data

Abstract

Surface downward shortwave radiation (DSR) is a fundamental variable in determining the Earth's radiation balance and is essential in many applications. Considerable efforts have been devoted to algorithm development, product generation, and validation. However, few studies have focused on comparing retrieval approaches, examining their strengths and weaknesses, and identifying the most suitable scenarios for each approach. In this study, we implemented and evaluated five representative DSR retrieval algorithms, including the forward parameterization approach, two physical inversion methods (look-up table (LUT) and optimization), and two statistical inversion methods (direct estimation and neural networks). We then proposed an algorithm-integration framework that combined the results of these DSR retrieval methods to further improve DSR estimation accuracy and consistency. To validate the DSR retrievals, we used in-situ data collected at 25 stations of the Baseline Surface Radiation Network (BSRN) over one year. Validation revealed that forward parameterization consistently performed best, with an overall root mean square error (RMSE) of 91.7 W/m2 or a relative RMSE of 16.9%, although it generated the fewest valid retrievals. For an identical data set, the LUT approach generated results comparable to those of parameterization. The neural network-based algorithm-integration approach reduced the RMSE by 11.0 W/m2 or the relative RMSE by 2.0%, compared to the best individual retrieval algorithm. Our analysis demonstrates that algorithm integration is a promising way to obtain DSR data that are superior to estimates from any individual retrieval algorithm.