In mountain landscapes, surface temperatures vary over short distances due to interacting influences of topography and overstory vegetation on local energy and water balances. At two study landscapes in the Sierra Nevada of California, characterized by foothill oak savanna at 276–481 m elevation and montane conifer forest at 1977–2135 m, we deployed 288 near-surface (5 cm above the surface) temperature sensors to sample site-scale (30 m) temperature variation related to hillslope orientation and vegetation structure and microsite-scale (2–10 m) variation related to microtopography and tree overstory. Daily near-surface maximum and minimum temperatures for the 2013 calendar year were related to topographic factors and vegetation overstory characterized using small footprint LiDAR imagery acquired by the National Ecological Observatory Network (NEON) Airborne Observation Platform (AOP). At both landscapes we recorded large site and microsite spatial variation in daily maximum temperatures, and less absolute variation in daily minimum temperatures. Generalized boosted regression trees were estimated to measure the influence of tree canopy density, understory solar radiation, cold-air drainage and pooling, ground cover and microtopography on daily maximum and minimum temperatures at site and microsite scales. Site-scale models based on indices of understory solar radiation and landscape position explained an average of 61–65% of daily variation in maximum temperature; site-scale models based on tree canopy density and landscape position explained 65–83% of variation in minimum temperatures. Models explained <15% of variation in microsite-scale maximum temperatures but within-site heterogeneity was significantly correlated with within-site heterogeneity in modeled understory radiation at both landscapes. Tree canopy density and slope explained 33% of microsite-scale variation in minimum temperatures at savanna sites. Our results demonstrate that it is feasible to model site-scale variation in daily surface temperature extremes and within-site heterogeneity in surface temperatures using LiDAR-derived variables, supporting efforts to understand cross-scale relationships between surface microclimates and regional climate change. Improved understanding of topographic and vegetative buffering of thermal microclimates across mountain landscapes is key to projecting microclimate heterogeneity and potential species’ range dynamics under future climate change.