On-orbit characterization of the VIIRS solar diffuser and solar diffuser screen.

Area of Science:

• Remote Sensing
• Instrument Calibration
• Optical Physics

Background:

• On-board calibration is crucial for accurate satellite data, especially for ocean color products.
• The Visible Infrared Imaging Radiometer Suite (VIIRS) relies on solar diffuser (SD) and its screen (SDS) for calibration.
• Degradation of the SD impacts data quality over time.

Purpose of the Study:

• To refine the on-board calibration of the VIIRS instrument by analyzing the bidirectional reflectance factors (BRF) of the solar diffuser (SD) and the vignetting function (VF) of the SD screen (SDS).
• To improve the accuracy and consistency of satellite ocean color remote sensing products through enhanced instrument characterization.
• To determine the absolute form of the BRF-VF product and the SDS VF using spacecraft yaw maneuver data.

Main Methods:

• Conducted 14 spacecraft yaw maneuvers for the Suomi National Polar-Orbiting Partnership satellite to gather data on VIIRS instrument response to solar angles.
• Combined yaw maneuver data with pre-launch measurements of SDS VF and SD BRF.
• Determined the absolute BRF-VF product and SDS VF for reflective solar bands (RSB) and the solar diffuser stability monitor (SDSM) detectors.

Main Results:

• The BRF-VF product for RSB was found to be independent of detector, gain status, and half-angle mirror side.
• Derived VFs showed agreement with pre-launch measurements and primarily depended on solar angles.
• Derived calibration coefficients (F-factors) demonstrated a significantly improved pattern, with residual seasonal fluctuations corrected by a solar azimuth angle-dependent function.

Conclusions:

• The refined calibration approach using BRF and VF significantly improves the accuracy of VIIRS instrument characterization.
• The results directly benefit satellite ocean color remote sensing by providing more reliable data.
• The study establishes a robust method for on-orbit calibration, reducing uncertainties in long-term data records.