Developing Algorithm for Operational GOES-R Land Surface Temperature Product

• Published in: IEEE transactions on geoscience and remote sensing 2009-03, Vol.47 (3), p.936-951
• Main Authors: Yunyue Yu, Tarpley, D., Privette, J.L., Goldberg, M.D., Rama Varma Raja, M.K., Vinnikov, K.Y., Hui Xu
• Format: Article
• Language: English
• Subjects: Accuracy; Advanced Baseline Imager (ABI); Algorithms; Applied geophysics; Atmosphere; Atmospheric modeling; Atmospherics; Computer simulation; Earth sciences; Earth, ocean, space; Emissivity; Exact sciences and technology; Geostationary Operational Environmental Satellite (GOES); Image retrieval; Image sensors; Information retrieval; Infrared image sensors; Internal geophysics; Land surface; Land surface temperature; land surface temperature (LST); Meteorological satellites; Satellite broadcasting; Satellites; Sensors; Spatial resolution; split window (SW); Studies; Temperature sensors
• ISSN: 0196-2892; 1558-0644
• DOI: 10.1109/TGRS.2008.2006180

The Geostationary Operational Environmental Satellite (GOES) program is developing the Advanced Baseline Imager (ABI), a new generation sensor to be carried onboard the GEOS-R satellite (launch expected in 2014). Compared to the current GOES Imager, ABI will have significant advantages for retrieving land surface temperature (LST) as well as providing qualitative and quantitative data for a wide range of applications. The infrared bands of the ABI sensor are designed to achieve a spatial resolution of 2 km at nadir and a noise equivalent temperature of 0.1 K. These improve the imager specifications and compare well with those of polar-orbiting sensors (e.g., Advanced Very High Resolution Radiometer and Moderate Resolution Imaging Spectroradiometer). In this paper, we discuss the development of a split window LST algorithm for the ABI sensor. First, we simulated ABI sensor data using the MODTRAN radiative transfer model and NOAA88 atmospheric profiles. To model land conditions, we developed emissivity data for 78 virtual surface types using the surface emissivity library from Snyder Using the simulation results, we performed regression analyses with the candidate LST algorithms. Algorithm coefficients were stratified for dry and moist atmospheres as well as for daytime and nighttime conditions. We estimated the accuracy and sensitivity of each algorithm for different sun-view geometries, emissivity errors, and atmospheric assessments. Finally, we evaluated the most promising algorithm using real data from the GOES-8 Imager and SURFace RADiation Network. The results indicate that the optimized LST algorithm meets the required accuracy (2.3 K) of the GOES-R mission.