Regional land surface temperature (LST) maps derived from remote sensing data are most available to cities to assess and respond to heat. Yet, LST only captures one dimension of urban climate. This study investigates the extent to which remote sensing derived estimates of LST are a proxy for multiple climate variables at hyper-local scales (<10s of meters). We compare remotely sensed estimates of LST (RS-LST) to field and simulated LST, MRT, and air temperature (AT), in a neighborhood in Tucson, Arizona, USA. We find that LST, MRT, and ST follow different diurnal trends masked by RS-LST. We also find that three-dimensional urban design is a better predictor of MRT than two-dimensional land cover and albedo – a known determinant of RS-LST. Shade is a better predictor of both simulated LST and MRT than RS-LST. We conclude that RS-LST is not adequate for guiding heat mitigation at hyper-local scales in cities.