  Diffuse reflectance spectroscopy is a powerful technique to predict soil
properties. It can be used in situ to provide data inexpensively and rapidly
compared to the standard laboratory measurements. Because most spectral data
bases contain air-dried samples scanned in the laboratory, field spectra
acquired in situ are either absent or rare in calibration data sets. However,
when models are calibrated on air-dried spectra, prediction using field spectra
are often inaccurate. We propose a framework to calibrate partial least squares
models when field spectra are rare using synthetic minority oversampling
technique (SMOTE). We calibrated a model to predict soil organic carbon content
using air-dried spectra spiked with synthetic field spectra. The root
mean-squared error of prediction decreased from 6.18 to 2.12 mg g$^{-1}$ and
$R^2$ increased from $-$0.53 to 0.82 compared to the model calibrated on
air-dried spectra only.
