Approximating Experimental Vegetation Spectroscopy Data through Emulation

The collection of field data (biophysical variables with associated spectral data) are an essential part of the development and validation of imaging spectroscopy vegetation products. Yet, their quality can only be assessed in the subsequent analysis, and often it appears that there is a wish for extra data to fill up gaps. In an attempt to generate such additional data, we propose to exploit emulation, i.e. variables-based reconstruction of spectral data through statistical learning. We evaluated emulation against classical interpolation techniques using an experimental field dataset with associated airborne hyperspectral HyMap reflectance spectra to produce HyMap-like spectra for any combination of input variables. Results indicate that: (1) emulation produces reflectance spectra more accurately than interpolation when validating against a split part of the field dataset (8% vs 12% errors), (2) emulation produces spectral data multiple times (tens to hundreds) faster than interpolation, and (3) emulation enables meaningful extrapolation outputs. Consequently, this technique opens various new analysis opportunities, e.g., emulators not only allow to produce large experimental-like datasets in a fraction of a second, but they also can be implemented into computationally intensive processing routines to speed up processing, such as global sensitivity analysis or inversion schemes.