Wildland fires are increasing in both severity and intensity leading to severe and lasting biogeochemical effects on soil. The CZU lightning complex started on August 16th, 2020, and burned 86,509 acres causing severe social and ecological damage. To better understand the impact of fire on soil properties at the landscape scale, we created a digital soil mapping model with the inclusion of remotely sensed burn severity covariates. We combined a raster-stack of environmental covariates with rasters for fire severity and soil samples, to disentangle the relative contribution of fire to the spatial distribution of soil properties in the recently burned Little Creek watershed in Santa Cruz, Ca. Soils were sampled via a conditional Latin hypercube sampling design and analyzed for soil health and soil Fe/Al-oxide mineralogy. To ascertain the relative contribution of remotely sensed fire severity covariates and standard digital soil mapping covariates (e.g. SCORPAN factors) to explain the variance in post-fire soil properties, we deployed multi-linear regression and random forest modeling. We report that remotely sensed indicators of fire severity explained the variance of Ntotal, Caex, pH, oxalate extractable P, NO3-, and NH4+ in both the MLR and RF models at the watershed scale. The inclusion of rasters of fire effects improved the description of target soil property variance, in concert with more traditional raster-based proxies for the soil forming factors, indicating that fire helps explain the spatial variability of these soil properties in recently burned post-fire landscapes. Furthermore, we report that an increase in remotely sensed fire severity led to an increase in sorbed P (as measured via oxalate extractable P), suggesting a potentially unreported change to post-fire soil P dynamics. Results inform remotely sensed assessment of fire induced changes to soil properties at the landscape scale.