Yu, YY, Privette, JL, Pinheiro, AC (2005). Analysis of the NPOESS VIIRS land surface temperature algorithm using MODIS data. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 43(10), 2340-2350.
The Visible Infrared Imaging Radiometer Suite (VIIRS) will replace the National Oceanic and Atmospheric Administration's (NOAA) Advanced Very High Resolution Radiometer (AVHRR) and the National Aeronautics and Space Administration's (NASA) Moderate Resolution Imaging Spectrometer (MODIS) as the nation's wide-swath multispectral sensor following the launch of the. National Polar Orbiting Environmental Sensor Suite (NPOESS) Preparatory Project: (NPP) in about 2008. Through the follow-on NPOESS program, VIIRS data will be the primary source of systematic land remote sensing products until about 2022. Together, the AVHRR/MODIS/VIIRS satellite series will provide a critical long-term data record for Earth studies. In the NPP/NPOESS program, the product. algorithms and production environments are developed and managed by private industry. This is a significant change from the current NOAA and NASA programs, and NPOESS algorithm deviations from successful heritage approaches (e.g., Earth Observation System). warrant comprehensive independent testing. The current baseline VIIRS land surface temperature (LST) algorithm represents one such deviation. In the present study, we evaluated the VIIRS LST by adapting it for use with 60 scenes of MODIS Level 1b radiance data. Algorithm coefficients were derived from MODTRAN4 radiative transfer model simulations. Using the validated MODIS LST (MYD11_L2) product as a reference, we found that precision errors in the VIIRS dual split window (DSW) algorithm (the current main approach) significantly exceed those of the VIIRS split window (SW) algorithm (the current backup approach) in both daytime and nighttime conditions. Performance of both is better for nighttime cases than for daytime cases with all surface types. We attribute the larger errors in the DSW approach to its use of short middle infrared wavelengths which, compared to thermal infrared wavelengths, exhibit greater variability in surface emissivity and susceptibility to solar contamination. We conclude that a traditional SW algorithm, such as the current VIIRS backup algorithm, would provide superior performance to the DSW approach. An SW approach would also provide more seamless continuity with heritage products. Although this is not an NPOESS requirement, it is a key objective for multimission climate data records and Earth system data records.