Statewide landcover, ecosystem, and percentage canopy cover classifications for Texas were mapped at a spatial resolution of 10-meters. Classifications were run for 16 zones across the state corresponding to available cloud-free multitemporal Sentinel-2 satellite imagery for each zone. For each zone, RandomForest classifications were run using data stacks comprised of spectral bands from three dates (winter, early growing season, late growing season/leaf-off) of imagery, as well as multiple vegetation indices (NDVI, EVI2, MSAVI2). Over 50,000 training points were selected from ground trips and high-resolution aerial image surveys to run the entire pixel-based classification. The overlapping zones were merged using a feathering algorithm to produce a single statewide land-cover classification map. The landcover mapping results were further refined using multiple spatial masks (e.g., urban, water, crop) along with logical rulesets and ancillary data. To map ecological systems, the land-cover classification was then intersected with an enduring features dataset derived primarily from soil map-unit polygons (gSSURGO) and other geophysical variables. Additionally, we produced a statewide percentage canopy cover map at a 10-meter spatial resolution using multiple techniques. For the western 2/3 of the Texas, a nested machine learning approach was used (Sunde et al., 2020), and for the eastern 1/3 of the state, a combination of LiDAR derived training data and machine learning was used. This dataset includes four items: "TX_10m_landcover_2021.zip" - Statewide landcover classification for Texas (10-meter spatial resolution) "TX_10m_ecoclass_map_2021.zip" - Statewide ecological systems classification for Texas (10-meter spatial resolution) "TX_10m_canopy_cover_2021.zip" - Statewide percentage canopy cover map for Texas (10-meter spatial resolution) "TX_lu_ecoclass_key.xlsx" - Table containing keys for the mapped landcover, canopy, and ecological systems classes (To facilitate display of the datasets within ESRI software, .lyr files are included in the respective archive folders) This work was funded by the Texas A&M Forest Service.