  Simulating soil reflectance spectra is invaluable for soil-plant radiative
modeling and training machine learning models, yet it is difficult as the
intricate relationships between soil structure and its constituents. To address
this, a fully data-driven soil optics generative model (SOGM) for simulation of
soil reflectance spectra based on soil property inputs was developed. The model
is trained on an extensive dataset comprising nearly 180,000 soil
spectra-property pairs from 17 datasets. It generates soil reflectance spectra
from text-based inputs describing soil properties and their values rather than
only numerical values and labels in binary vector format. The generative model
can simulate output spectra based on an incomplete set of input properties.
SOGM is based on the denoising diffusion probabilistic model (DDPM). Two
additional sub-models were also built to complement the SOGM: a spectral
padding model that can fill in the gaps for spectra shorter than the full
visible-near-infrared range (VIS-NIR; 400 to 2499 nm), and a wet soil spectra
model that can estimate the effects of water content on soil reflectance
spectra given the dry spectrum predicted by the SOGM. The SOGM was up-scaled by
coupling with the Helios 3D plant modeling software, which allowed for
generation of synthetic aerial images of simulated soil and plant scenes. It
can also be easily integrated with soil-plant radiation model used for remote
sensin research like PROSAIL. The testing results of the SOGM on new datasets
that not included in model training proved that the model can generate
reasonable soil reflectance spectra based on available property inputs. The
presented models are openly accessible on:
https://github.com/GEMINI-Breeding/SOGM_soil_spectra_simulation.
