Defining site potential for an area establishes its possible long-term vegetation growth productivity in a relatively undisturbed state, providing a realistic reference point for ecosystem performance. Modeling and mapping site potential helps to measure and identify naturally occurring variations on the landscape as opposed to variations caused by land management activities or disturbances (Rigge et al. 2020). We integrated remotely sensed data (250-m enhanced Moderate Resolution Imaging Spectroradiometer (eMODIS) Normalized Difference Vegetation Index (NDVI) (https://earthexplorer.usgs.gov/)) with land cover, biogeophysical (i.e., soils, topography) and climate data into regression-tree software (Cubist?). We integrated these data to create a snapshot of spatially explicit predictions of shrub and grass site potential in the Upper Colorado River Basin (UCRB) in the western U.S. The predictions are limited to pixels classified by the 2016 National Land Cover Database (NLCD) as shrub or grassland/herbaceous (https://www.mrlc.gov). The site potential datasets will be incorporated as independent variables into mapping models that delineate statistically significant deviations between actual NDVI values and modeled NDVI values in the UCRB. Rigge, MB., Homer, C., Shi, H., Wylie, BK. 2020. Departures of rangeland fractional component cover and land cover from landsat-based ecological potential in Wyoming, USA. Rangeland Ecology & Management. DOI: 10.1016/j.rama.2020.03.009.