Global Estimates for High-Spatial-Resolution Clear-Sky Land Surface Upwelling Longwave Radiation from MODIS Data

Abstract

Surface upwelling longwave radiation (LWUP) is a vital component in calculating the Earth's surface radiation budget. Under the general framework of the hybrid method, we developed linear and dynamic learning neural network (DLNN) models for estimating the global 1-km instantaneous clear-sky LWUP from the top-of-atmosphere radiance of Moderate Resolution Imaging Spectroradiometer thermal infrared channels 29, 31, and 32. Extensive radiative transfer simulations were conducted to produce a large number of representative samples, from which the linear model and DLNN model were derived. These two hybrid models were evaluated using ground measurements collected at 19 sites from three networks (SURFRAD, ASRCOP, and GAME-AAN). According to the validation results, the linear model was more accurate than the DLNN model, with a bias and root-mean-square error (RMSE) of -0.31 W/m2 and 19.92 W/m2 obtained by averaging the mean bias and RMSE for the three networks. Additionally, the computational efficiency of the linear model was much higher than that of the DLNN model. We also compared our linear model to a hybrid method developed by a previous study and found ours to perform better.