This dataset release provides historical (2016 - 2022) estimates of fractional cover for exotic annual grass (EAG) species and a native perennial bunch grass in the arid and semi-arid rangelands of the western United States. The dataset includes four fractional cover maps per year, accompanied by corresponding confidence maps, for a group of 16 species of EAGs, cheatgrass (Bromus tectorum); medusahead (Taeniatherum caput-medusae); and Sandberg bluegrass (Poa secunda). The data were generated using a combination of field observations from Bureau of Land Management (BLM) Assessment, Inventory, and Monitoring (AIM) plots; remotely sensed data from the Harmonized Landsat and Sentinel-2 (HLS) product (specifically Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI)), and various environmental, vegetation, remotely sensed, and geophysical drivers. Additionally, artificial intelligence and machine learning techniques were employed in the data generation process. It should be noted that the training of regression-tree models and the development of historical maps (2016-2020) utilized a total of 17,536 AIM plots from years 2016 – 2019. For the creation of 2021 maps, 19,415 AIM plots from years 2016 - 2021 were employed, and 2022 maps, 28,901 AIM plots from 2016-2022 were used. In the case of 2016 – 2020 maps, areas above 2250-m elevation and pixels classified other than grassland/herbaceous or shrub (likely rangelands) were masked based on the 2016 National Land Cover Dataset (NLCD). For the 2021 and 2022 maps, areas above 2350-m elevation and pixels classified as other than grassland/herbaceous by the 2019 NLCD were masked. The seed source variable from the Rangeland Analysis Platform (RAP) [Jones et al., 2018]) was used as one of the drivers for modeling of 2016 – 2020 maps but was not utilized for modeling of 2021 and 2022 maps. Additionally, HLS NDWI were not used for 2021 and 2022 maps. All other predictor variables are identical for all sets of maps. For details, please check data quality information section.