Computationally expensive radiative transfer models (RTMs) are widely used to realistically reproduce the light interaction with the earth surface and atmosphere. Because these models take long processing time, the common practice is to first generate a sparse look-up table (LUT) and then make use of interpolation methods to sample the multidimensional LUT input variable space. However, the question arise whether common interpolation methodsperform most accurate. As an alternative to interpolation, this paper proposes to use emulation, i.e., approximating the RTM output by means of the statistical learning. Two experiments were conducted to assess the accuracy in delivering spectral outputs using interpolation and emulation: at canopy level, using PROSAIL; and at top-of-atmosphere level, using MODTRAN. Various interpolation (nearest-neighbor, inverse distance weighting, and piece-wice linear) and emulation [Gaussian process regression (GPR), kernel ridge regression, and neural networks] methods were evaluated against a dense reference LUT. In all experiments, the emulation methods clearly produced more accurate output spectra than classical interpolation methods. The GPR emulation performed up to ten times more accurately than the best performing interpolation method, and this with a speed that is competitive with the faster interpolation methods. It is concluded that emulation can function as a fast and more accurate alternative to commonly used interpolation methods for reconstructing RTM spectral data.
Keywords: Emulation; interpolation; look-up tables (LUT); machine learning; peformance simulators; processing speed; radiative transfer models (RTMs).