Emulation as an Accurate Alternative to Interpolation in Sampling Radiative Transfer Codes

TL;DR

This study demonstrates that emulation using statistical learning methods significantly outperforms traditional interpolation techniques in accuracy and speed for sampling radiative transfer models, offering a promising alternative.

Contribution

It introduces emulation as a more accurate and efficient alternative to interpolation for radiative transfer model sampling, validated through experiments with PROSAIL and MODTRAN.

Findings

Emulation methods outperform interpolation in spectral accuracy.

GPR emulation is up to ten times more accurate than best interpolation.

Emulation achieves comparable speed to faster interpolation methods.

Abstract

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 multi-dimensional LUT input variable space. However, the question arise whether common interpolation methods perform most accurate. As an alternative to interpolation, this work proposes to use emulation, i.e., approximating the RTM output by means of statistical learning. Two experiments were conducted to assess the accuracy in delivering spectral outputs using interpolation and emulation: (1) at canopy level, using PROSAIL; and (2) at top-of-atmosphere level, using MODTRAN. Various interpolation (nearest-neighbour, inverse distance weighting, piece-wice linear) and emulation (Gaussian process regression (GPR), kernel ridge regression, 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. 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.