The Geostationary Operational Environmental Satellite (GOES) program is developing a new generation sensor, the
Advanced Baseline Imager (ABI), to be carried on the GOES-R satellite to be lunched in approximately in 2014.
Compared to the current GOES imager, ABI will have significant advantages for measuring land surface temperature as
well as to providing qualitative and quantitative data for a wide range of applications. Specifically, spatial resolution of
the ABI sensor is 2 km, and the infrared window noise equivalent temperature is 0.1 K, which are very close to the polarorbiting
satellite sensors such as AVHRR. Most importantly, ABI observes the full disk every five minutes, which not
only provides more cloud-free measurements but also makes daily temperature variation analysis possible. In this study
we developed split window algorithms for the LST measurement from the ABI sensor. We generated the ABI sensor
data using MODTRAN radiative transfer model and NOAA88 atmospheric profiles and ran regression analyses for the
LST algorithm development. The algorithms are developed by optimizing existing split window LST algorithms and
adding a path length correction term to minimize the retrieval errors due to difference atmospheric path absorption from
nadir view to the edge-of-scan. The algorithm coefficients are stratified for dry and moist atmospheric conditions, as well
as for the daytime and nighttime. The algorithm sensitivity to land surface emissivity uncertainty is analyzed to ensure
the algorithm performance.