Consistent retrieval methods to estimate land surface shortwave and longwave radiative flux components under clear-sky conditions

Shortwave (0.3–3 μm) and longwave (3–50 μm) surface radiative flux components have been widely used in numerical prediction, meteorology, hydrology, biomass estimation, surface energy circulation and climate change studies, etc. However, during past decades, these components were usually estimated independently using different methods, possibly causing inconsistent estimation biases due to different atmospheric parameters and algorithms, especially for net surface fluxes. Two methods have been proposed in this paper to simultaneously derive surface shortwave (or longwave) radiative flux components based on MODIS products using an artificial neural network (ANN). The validation results show that the maximum root-mean-square error for downward and net shortwave radiative fluxes is less than 45 W/m2, about 60 W/m2 for direct solar radiation and 25 W/m2 for all longwave fluxes, which are comparable or even better than existing algorithms, thus demonstrating their feasibility and efficacy. The ANN-based models are then applied over the Tibetan Plateau region and the characteristics of the surface radiative flux components over such areas are analyzed.

► Developed an ANN-based shortwave model for estimating SW radiative flux components. ► Developed an ANN-based longwave model for estimating LW radiative flux components. ► Better accuracies of fluxes can be achieved by using those newly developed methods. ► Proved feasibility to retrieve multi-components of flux from space consistently. ► Retrieved flux components reflect actual flux characteristics over Tibetan Plateau.