Imaging spectroscopy has the potential to enable measurement of soil properties in intact soil profiles at spatial scales previously not possible. There are unique challenges associated with imaging spectroscopy compared to point spectroscopy. Particularly, signal noise and the influence of non-compositional effects on the spectra. One way to manage these effects is by using wavelet transforms, which are a signal processing technique. Combining wavelet transform processed spectra with machine learning techniques can be used to improve predictions of soil organic carbon throughout the soil profile with imaging spectroscopy. In one study, intact soil cores were analyzed using a SisuROCK automated hyperspectral imaging system in a laboratory setting collecting shortwave infrared reflectance data. Predictive models were then built for soil organic carbon using a combination of wavelet analysis and Bayesian Regularized Neural Nets. The combination of wavelets, machine learning and imaging spectroscopy enabled mapping of soil organic carbon throughout the profile, and identification of the magnitude and depths that rotational treatments were having an effect. This webinar is based on the following publication: Sorenson, P. T., Quideau, S. A., Rivard, B., & Dyck, M. (2020). Distribution mapping of soil profile carbon and nitrogen with laboratory imaging spectroscopy. Geoderma, 359, 113982 https://doi.org/10.1016/j.geoderma.2019.113982.