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Radiometric consistency between GOES-16 ABI and VIIRS on Suomi NPP and NOAA-20
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2020
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Source: Journal of Applied Remote Sensing 14(3), 032407 (29 May 2020)
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Journal Title:Journal of Applied Remote Sensing
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NOAA Program & Office:
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Description:NOAA has both geostationary (GEO) and polar orbiting (LEO) satellites that are procured through different vendors. For long-term environmental studies, it is essential to establish consistent radiometric calibration among satellite instruments irrespective of the satellite platforms. The Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership (S-NPP) and NOAA-20 satellites has been providing critical weather and climate-related data. With the launch of NOAA-20 in November 2017, the global coverage of VIIRS has doubled. With more than 7 years in space, S-NPP VIIRS has been rigorously calibrated and validated. We aim to quantify the radiometric consistency between the two VIIRS instruments through double differencing using GOES-16 Advanced Baseline Imager (ABI) as a reference instrument. We also provide insight into the temporal radiometric consistency between VIIRS and ABI. Since no direct simultaneous nadir overpasses (SNOs) exist between SNPP and NOAA-20 for intercomparison, GEO-LEO intercalibration is performed using SNOs between ABI and VIIRS instruments. We show that the NOAA-20 VIIRS measured top-of-atmosphere (TOA) reflectance is consistently lower than that of the S-NPP, mostly on the order of 2% to 3%, consistent with the past studies. GOES-16 ABI-observed reflectance is higher than both VIIRS instruments. SNOs over the all-sky tropical ocean are analyzed to quantify the bias for both NOAA-20 and S-NPP VIIRS relative to ABI. The impact on bias due to spectral differences is accounted for using spectral band adjustment factors estimated using hyperspectral measurements from SCIAMACHY. Uncertainties exist mainly due to calibration uncertainties in VIIRS and ABI, the time difference between the VIIRS and ABI observations, differences in cloud contamination, spectral response function differences, and lack of in-situ hyperspectral data to account for the spectral bias.
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Source:Journal of Applied Remote Sensing 14(3), 032407 (29 May 2020)
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