Digital soil mapping (DSM) provides a cost-effective approach for characterizing the spatial variation in soil properties which contributes to inconsistent productivity. This study utilized Random Forest (RF) models to facilitate DSM of apparent soil electrical conductivity (ECa), estimated cation exchange capacity (CEC), and soil organic matter (SOM) in agricultural fields across the Lower Mississippi Alluvial Valley. The RF models were trained and tested using in situ collected ECa, CEC, and SOM data, paired with a bare soil composite of Landsat 9 imagery. Field data and imagery were collected during the study period of 2019 through 2023. Models ranged from fair to moderate in accuracy (R2 from 0.27 to 0.68). The contrasting performance between CEC/SOM and ECa models is likely due to the dynamic nature of soil properties. Accordingly, models could have benefitted from covariates such as soil moisture, topography, and climatic factors, or higher spectral resolution imagery, such as hyperspectral.