A fast neural network-based approach for joint mid-ir and far-ir surface spectral emissivity retrieval

Surface emissivity (epsilon) plays a critical role in Earth's radiation budget and climate. The Polar Radiant Energy in the Far-InfraRed Experiment (PREFIRE) and the Far-Infrared Outgoing Radiation Understanding and Monitoring (FORUM) satellite missions aim to estimate surface spectral emissivities in mid-IR and far-IR regions. This study presents a neural network (NN)-based surface spectral emissivity retrieval algorithm under clear-sky that offers comparable performance to optimal-estimation (OE)-based methods but reduces computation time by a factor of 10(5). The NN-based method has achieved a mean relative retrieval error (vertical bar Delta vertical bar epsilon) of 0.0028 with a standard deviation of 0.0013. Shapley values are employed to interpret the algorithm's results and assess the relative importance of input features. The results derived from the Shapley values analysis are in good agreement with the physical understanding. The study highlights the effectiveness of the proposed neural network approach for surface emissivity estimation in forthcoming satellite missions.