These data were compiled for the creation of a continuous, high-resolution transboundary land cover map of the Sonoran and Mojave Desert ecoregion within Bird Conservation Region 33 (BCR 33). Objective(s) of our study were to, 1) develop a machine learning algorithm trained to classify vegetation land cover using remote sensing spectral data and phenology metrics from 2013-2021, over the Sonoran and Mojave Deserts BCR 33; 2) calibrate, validate, and refine the final machine learning derived vegetation map using a collection of openly sourced remote sensing and ground-based ancillary data, images, and limited fieldwork; and 3) harmonize a new transboundary classification system by expanding existing land cover mapping resources from the United States portion of BCR 33 into Mexico. These data represent the final land cover maps produced using a Random Forest Classifier (RF), with additional ancillary labels for urban and agriculture areas. These data were created within BCR33 which spans an extent from Nevada, in the United States to Sinaloa, in Mexico for the time period from April 2013 to December 2021. These data were created by researchers at the University of Arizona, Vegetation Index and Phenology Lab who collected, processed, and analyzed all data and developed the random forest model used to produce the final continuous, high-resolution transboundary land cover map. These data can be used to guide land management and conservation decisions within the Sonoran and Mojave Desert ecoregion within BCR 33.