Land Surface Temperature (LST) has been identified by NASA and other international organizations as an important Earth System Data Record (ESDR). An ESDR is defined as a long-term, well calibrated and validated data set. Identifying uncertainties in LST products with coarse spatial resolutions (>10 km) such as those from hyperspectral infrared sounders is notoriously difficult due to the challenges of making reliable in situ measurements representative of the spatial scales of the output products. In this study we utilize a Radiance-based (R-based) LST method for estimating uncertainties in the Atmospheric Infrared Sounder (AIRS) v5 LST product. The R-based method provides estimates of the true LST using a radiative closure simulation without the need for in situ measurements, and requires input air temperature, relative humidity profiles and emissivity data. The R-based method was employed at three validation sites over the Namib Desert, Gran Desierto, and Redwood National Park for all AIRS observations from 2002 to 2010. Results showed daytime LST root-mean square errors (RMSE) of 2–3 K at the Namib and Desierto sites, and 1.5 K at the Redwood site. Nighttime LST RMSEs at the two desert sites were a factor of two less when compared to daytime results. Positive daytime LST biases were found at each site due to an underestimation of the daytime AIRS v5 longwave spectral emissivity, while the reverse occurred at nighttime. In the AIRS v6 product (release 2012), LST biases and RMSEs will be reduced significantly due to improved methodologies for the surface retrieval and emissivity first guess.