Suomi National Polar‐Orbiting Partnership (S‐NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) reflective bands are currently calibrated via weekly updates to look‐up tables (LUTs) utilized by operational ground processing in the Joint Polar Satellite System Interface Data Processing Segment (IDPS). The parameters in these LUTs must be predicted ahead 2 weeks and cannot adequately track the dynamically varying response characteristics of the instrument. As a result, spurious “predict‐ahead” calibration errors of the order of 0.1% or greater are routinely introduced into the calibrated reflectances and radiances produced by IDPS in sensor data records (SDRs). Spurious calibration errors of this magnitude adversely impact the quality of downstream environmental data records (EDRs) derived from VIIRS SDRs such as Ocean Color/Chlorophyll and cause increased striping and band‐to‐band radiometric calibration uncertainty of SDR products. A novel algorithm that fully automates reflective band calibration has been developed for implementation in IDPS in late 2013. Automating the reflective solar band (RSB) calibration is extremely challenging and represents a significant advancement over the manner in which RSB calibration has traditionally been performed in heritage instruments such as the Moderate Resolution Imaging Spectroradiometer. The automated algorithm applies calibration data almost immediately after their acquisition by the instrument from views of space and on‐onboard calibration sources, thereby eliminating the predict‐ahead errors associated with the current offline calibration process. This new algorithm, when implemented, will significantly improve the quality of VIIRS reflective band SDRs and consequently the quality of EDRs produced from these SDRs.
The GOES-R Flight Project has developed an Image Navigation and Registration (INR) Performance Assessment Tool Set (IPATS) for measuring Advanced Baseline Imager (ABI) and Geostationary Lightning Mapper (GLM) INR performance metrics in the post-launch period for performance evaluation and long term monitoring. For ABI, these metrics are the 3-sigma errors in navigation (NAV), channel-to-channel registration (CCR), frame-to-frame registration (FFR), swath-to-swath registration (SSR), and within frame registration (WIFR) for the Level 1B image products. For GLM, the single metric of interest is the 3-sigma error in the navigation of background images (GLM NAV) used by the system to navigate lightning strikes. 3-sigma errors are estimates of the 99.73 rd percentile of the errors accumulated over a 24 hour data collection period. IPATS utilizes a modular algorithmic design to allow user selection of data processing sequences optimized for generation of each INR metric. This novel modular approach minimizes duplication of common processing elements, thereby maximizing code efficiency and speed. Fast processing is essential given the large number of sub-image registrations required to generate INR metrics for the many images produced over a 24 hour evaluation period. Another aspect of the IPATS design that vastly reduces execution time is the off-line propagation of Landsat based truth images to the fixed grid coordinates system for each of the three GOES-R satellite locations, operational East and West and initial checkout locations. This paper describes the algorithmic design and implementation of IPATS and provides preliminary test results.
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