**Aaron Gerace \* , Tania Kleynhans, Rehman Eon and Matthew Montanaro**

Rochester Institute of Technology, 54 Lomb Memorial Drive, Rochester, NY 14624, USA; tkpci@rit.edu (T.K.); rse4949@rit.edu (R.E.); montanaro@cis.rit.edu (M.M.)

**\*** Correspondence: gerace@cis.rit.edu; Tel.: +1-585-475-4388

Received: 29 October 2019; Accepted: 6 January 2020; Published: 9 January 2020

**Abstract:** The split window technique has been used for over thirty years to derive surface temperatures of the Earth with image data collected from spaceborne sensors containing two thermal channels. The latest NASA/USGS Landsat satellites contain the Thermal Infrared Sensor (TIRS) instruments that acquire Earth data in two longwave infrared bands, as opposed to a single band with earlier Landsats. The United States Geological Survey (USGS) will soon begin releasing a surface temperature product for Landsats 4 through 8 based on the single spectral channel methodology. However, progress is being made toward developing and validating a more accurate and less computationally intensive surface temperature product based on the split window method for Landsat 8 and 9 datasets. This work presents the progress made towards developing an operational split window algorithm for TIRS. Specifically, details of how the generalized split window algorithm was tailored for the TIRS sensors are presented, along with geometric considerations that should be addressed to avoid spatial artifacts in the final surface temperature product. Validation studies indicate that the proposed algorithm is accurate to within 2 K when compared to land-based measurements and to within 1 K when compared to water-based measurements, highlighting the improved accuracy that may be achieved over the current single-channel methodology being used to derive surface temperature in the Landsat Collection 2 surface temperature product. Surface temperature products using the split window methodologies described here can be made available upon request for testing purposes.

**Keywords:** Landsat; land surface temperature; split window algorithm; TIRS; thermal
