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The Intercalibration of Geostationary Visible Imagers Using Operational...

Doelling, D. R., B. Scarino, D. Morstad, A. Gopalan, R. Bhatt, C. Lukashin, and P. Minnis (2013), The Intercalibration of Geostationary Visible Imagers Using Operational Hyperspectral SCIAMACHY Radiances, IEEE Trans. Geosci. Remote Sens., 51, 1245-1254, doi:10.1109/TGRS.2012.2227760.
Abstract: 

Spectral band differences between sensors can complicate the process of intercalibration of a visible sensor against a reference sensor. This can be best addressed by using a hyperspectral reference sensor whenever possible because they can be used to accurately mitigate the band differences. This paper demonstrates the feasibility of using operational Scanning Imaging Absorption Spectrometer for Atmospheric Cartography (SCIAMACHY) large-footprint hyperspectral radiances to calibrate geostationary Earth-observing (GEO) sensors. Near simultaneous nadir overpass measurements were used to compare the temporal calibration of SCIAMACHY with Aqua Moderate Resolution Imaging Spectroradiometer band radiances, which were found to be consistent to within 0.44% over seven years. An operational SCIAMACHY/GEO ray-matching technique was presented, along with enhancements to improve radiance pair sampling. These enhancements did not bias the underlying intercalibration and provided enough sampling to allow up to monthly monitoring of the GEO sensor degradation. The results of the SCIAMACHY/GEO intercalibration were compared with other operational four-year Meteosat-9 0.65-μm calibration coefficients and were found to be within 1% of the gain, and more importantly, it had one of the lowest temporal standard errors of all the methods. This is more than likely that the GEO spectral response function could be directly applied to the SCIAMACHY radiances, whereas the other operational methods inferred a spectral correction factor. This method allows the validation of the spectral corrections required by other methods.

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Research Program: 
Modeling Analysis and Prediction Program (MAP)
Radiation Science Program (RSP)
Mission: 
CLARREO