Background Canadian fire management agencies track drought conditions using the Drought Code (DC) in the Canadian Forest Fire Danger Rating System. The DC represents deep organic layer moisture.Aims To determine if electronic soil moisture probes and land surface model estimates of soil moisture content can be used to supplement and/or improve our understanding of drought in fire danger rating.Methods We carried out field studies in the provinces of Alberta and Ontario. We installed in situ soil moisture probes at two different depths in seven forest plots, from the surface through the organic layers, and in some cases into the mineral soil.Results Our results indicated that the simple DC model predicted the moisture content of the deeper organic layers (10–18 cm depths) well, even compared with the more sophisticated land surface model.Conclusions Electronic moisture probes can be used to supplement the DC. Land surface model estimates of moisture content consistently underpredicted organic layer moisture content.Implications Calibration and validation of the land surface model to organic soils in addition to mineral soils is necessary for future use in fire danger prediction.

• Maps of organic layer thickness and fuel load were developed using machine learning. • Tree species was the most important variable in the final random forest model. • Error in our final models was close to the natural variability we expected to find. • The resultant maps will help improve fuel consumption models. Forest organic layers are important soil carbon pools that can, in the absence of disturbance, accumulate to great depths, especially in lowland areas. Across the Canadian boreal forest, fire is the primary disturbance agent, often limiting organic layer accumulation through the direct consumption of these fuels. Organic layer thickness (OLT) and fuel load (OLFL) are common physical attributes used to characterize these layers, especially for wildland fire science. Understanding the drivers and spatial distribution of these attributes is important to improve predictions of fire behaviour, emissions and effects models. We developed maps of OLT and OLFL using machine learning approaches (weighted K-nearest neighbour and random forests) for the forested region of the province of Alberta, Canada (538, 058 km 2 ). The random forests approach was found to be the best approach to model the spatial distribution of these forest floor attributes. A databased of 3, 237 OLT and 594 OLFL plots were used to train the models. The error in our final model, particularly for OLT (5 cm), was relatively close to the variability we would expect to find naturally (3 cm). The dominant tree species was the most important covariate in the models. Age, solar radiation, spatial location, climate variables and surficial geology were also important drivers, although their level of importance varied between tree species and depended on the modelling method that was used.