Assessment of soil organic matter content using laboratory analysis can be costly and time consuming, so limiting how often land managers assess this important property. This work demonstrates an ability to estimate topsoil organic matter content from field observations alone and provides a method by which rapid and cost-effective assessments of soil organic matter status may be made. Models using environmental factors from the National Soil Inventory of Scotland (NSIS) dataset as inputs to a neural network model were used to predict loss on ignition (LOI). Two models, one for all soils and one for soils with small organic matter contents (LOI < 20%), were developed. It was found that the model developed for all soils produced reasonable predictive results across the entire LOI range (R2 = 0.877), although it was not as effective at predicting small LOI values (R2 = 0.354) as the small organic matter content model (R2 = 0.674). Both models were tested with imagery and data from samples outwith the NSIS dataset to validate the approach. Predictive results were less accurate than when using NSIS data. A discussion of possible improvements to make the model useful for field observations of soils is given.