DeepEmSat: Deep Emulation for Satellite Data Mining

The growing volume of Earth science data available from climate simulations and satellite remote sensing offers unprecedented opportunity for scientific insight, while also presenting computational challenges. Machine learning models have demonstrated potential as fast statistical models for expensive simulations and for extracting credible insights from complex datasets. One potential area of impact is atmospheric correction, where physics-based numerical models retrieve surface reflectance information from top of atmosphere observations. Here, we develop DeepEmSat: a deep learning emulator approach for atmospheric correction, and offer results to support the hypothesis that deep learning can make a contribution to the efficient processing of satellite images.