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Improved algorithm for determining the Visible Infrared Imaging Radiometer Suite Day/Night Band high-gain stage dark offset free from light contamination.
Appl Opt ; 58(6): 1400-1407, 2019 Feb 20.
Article em En | MEDLINE | ID: mdl-30874024
Dark offset is one of the key parameters for Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) high-gain stage (HGS) radiometric calibration, whose accuracy strongly impacts applications of DNB low-light detection for Earth observation at nighttime. Currently, DNB observation of the VIIRS onboard calibrator blackbody (OBCBB) view, together with its observation of deep space during the spacecraft pitch maneuver performed early in the mission, has been used to compute the HGS dark offset continuously. However, the relationship between the DNB OBCBB data and the Earth view (EV) data is unclear due to electronic timing differences between these two views. It is questionable whether the DNB OBCBB data can monitor the EV HGS dark offset change. Through comprehensive analysis of the DNB OBCBB data and EV data acquired from the monthly special acquisitions known as the VIIRS recommended operating procedures (VROPs), we have shown that the OBCBB data can only track the dark current component of the DNB HGS EV dark offset, instead of the total dark offset. The DNB observation of deep space during the spacecraft pitch maneuver was also contaminated by starlight. With such background, in this paper we propose an improved algorithm for determining the DNB HGS dark offset. By combined use of the DNB OBCBB data and the DNB VROP data, the generated DNB HGS dark offset is both free from light contamination and capable of tracking continuous drift. The improved algorithm could potentially improve the DNB radiometric performance at low radiance level. Our results provide a solid theoretical basis for dark offset calibration of the VIIRS DNB onboard Suomi National Polar-Orbiting Partnership satellite and the following Joint Polar Satellite System satellites.

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Revista: Appl Opt Ano de publicação: 2019 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Revista: Appl Opt Ano de publicação: 2019 Tipo de documento: Article