SCOPE-Based Emulators for Fast Generation of Synthetic Canopy Reflectance and Sun-Induced Fluorescence Spectra

Progress in advanced radiative transfer models (RTMs) led to an improved understanding
of reflectance (R) and sun-induced chlorophyll fluorescence (SIF) emission throughout the leaf and
canopy. Among advanced canopy RTMs that have been recently modified to deliver SIF spectral
outputs are the energy balance model SCOPE and the 3D models DART and FLIGHT. The downside
of these RTMs is that they are computationally expensive, which makes them impractical in routine
processing, such as scene generation and retrieval applications. To bypass their computational burden,
a computationally effective technique has been proposed by only using a limited number of model
runs, called emulation. The idea of emulation is approximating the original RTM by a surrogate
machine learning model with low computation time. However, a concern is whether the emulator
reaches sufficient accuracy. To this end, we analyzed key aspects of emulator development that may
impact the precision of emulating SCOPE-like R and SIF spectra, being: (1) type of machine learning,
(2) type of dimensionality reduction (DR) method, and (3) number of components and lookup table
(LUT) size. The machine learning family of Gaussian processes regression and neural networks
were found best suited to function as emulators. The classical principal component analysis (PCA)
remains a robust DR method, but the number of components needs to be optimized depending on
the complexity of the spectral data. Based on a small Latin hypercube sampling LUT of 500 samples
(70% used for training) covering a selection of SCOPE input variables, the best-performing emulators
can reconstruct any combination for the selected SCOPE input variables with relative errors along
the spectral range below 2% for R and 4% for SIF. That is sufficient for a precise reconstruction for
the large majority of possible combinations, and errors can be further reduced when increasing LUT
size for training. As a proof of concept, we imported the best-performing emulators into a newly
developed Automated Scene Generator Module (A-SGM) to generate a R and SIF synthetic scene of a
vegetated surface. Using emulators as alternative of SCOPE reduced the processing time from the
order of days to the order of minutes while preserving sufficient accuracy.