An urban air temperature model is presented using GOES-16 land surface temperature. The
Automated Surface Observing System (ASOS) serves as ground truth air temperature for
calibration and testing of the model. The National Land Cover Database (NLCD) is used to
calculate a weighted distribution of 20 land classifications for each satellite pixel surrounding a
nearby ASOS station. A time-match algorithm aligns the ground and satellite measurements
within 5-minutes of one another, and the resulting matched LST and air temperature are
compared over nine months to investigate their cross-correlation. A model is constructed by
fitting their difference using a gaussian profile. Landcover, latitude, longitude, local time, and
elevation are inputted into an artificial regressive neural network to fit each unique GOES-16
pixel. Over 100 urban stations and satellite pixels throughout the continental U.S. are used to
construct the diurnal gaussian model and approximate air temperature. Early statistics indicate
favorable results, competing with other studies with more complicated and intensive
calculations. The presentation of this model is intended to simplify the calculation of air
temperature from satellite LST and create a successful model that performs well in urban
environments. The improvement of urban air temperature calculations will also result in
improved satellite land surface products such as relative humidity and heat index.