Indices derived from remotely-sensed imagery are commonly used to predict soil properties with digital soil mapping (DSM) techniques. The use of images from single dates or a small number of dates is most common for DSM; however, selection of the appropriate images is complicated by temporal variability in land surface spectral properties. We argue that hyper-temporal remote sensing (RS) (i.e., hundreds of images) can provide novel insights into soil spatial variability by quantifying the temporal response of land surface spectral properties. This temporal response provides a spectral ‘fingerprint’ of the soil-vegetation relationship which is directly related to a range of soil properties. To evaluate the hyper-temporal RS approach, this study first reviewed and synthesized, within the context of temporal variability, previous research that has used RS imagery for DSM. From this analysis we developed a conceptual framework for understanding the soil-vegetation-climate relationship discernable with hyper-temporal RS, which encapsulates both intra- and inter-annual variability. Finally, we demonstrate the utility of this approach by presenting a case study in a semiarid landscape of southeastern Arizona, USA; where surface soil texture and coarse fragment classes were predicted using a 29 year time series of normalized difference vegetation index (NDVI) from Landsat TM data and modeled using support vector machine (SVM) classification. Results from the case study show that SVM classification using hyper-temporal RS imagery was effective in modeling both soil texture and coarse fragment classes, where both variables were 60% percent correctly classified (PCC). In this semiarid ecosystem, variability in precipitation at short time scales (i.e., <6 weeks) was the dominant driver of vegetation spectral variability, with maximum variability corresponding to short-term transitions between climatic states. These short-term transitions between climatic states corresponded to the RS scenes with the highest variable importance in our SMV models, confirming the importance of spectral variability in predicting soil texture and coarse fragment classes. Results from the case study demonstrate the efficacy of the hyper-temporal RS approach in predicting soil properties and highlights how hyper-temporal RS can improve current methods of soil mapping efforts through its ability to characterize subtle changes in RS spectra relating to variation in soil properties.