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J. Imaging 2016, 2(1), 9; doi:10.3390/jimaging2010009
Abstract: The Suomi National Polar-orbiting (NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Day-Night Band (DNB) measures visible and near-infrared light extending over seven orders of magnitude of dynamic range. This makes radiometric calibration difficult. We have observed that DNB imagery has striping, banding and other nonuniformities—day or night. We identified the causes as stray light, nonlinearity, detector crosstalk, hysteresis and mirror-side variation. We found that these affect both Earth-view and calibration signals. These present an obstacle to interpretation by users of DNB products. Because of the nonlinearity we chose the histogram matching destriping technique which we found is successful for daytime, twilight and nighttime scenes. Because of the very large dynamic range of the DNB, we needed to add special processes to the histogram matching to destripe all scenes, especially imagery in the twilight regions where scene illumination changes rapidly over short distances. We show that destriping aids image analysts, and makes it possible for advanced automated cloud typing algorithms. Manual or automatic identification of other features, including polar ice and gravity waves in the upper atmosphere are also discussed. In consideration of the large volume of data produced 24 h a day by the VIIRS DNB, we present methods for reducing processing time.
1.1. VIIRS Design
1.2. On-Orbit DNB Calibration Process
1.2.1. Original DNB Calibration Process
1.2.2. DNB RSB Automatic Calibration Process
2. Materials and Methods
2.1. Source of Data
2.3. Software Language and Libraries
2.4. Histogram Matching Method Destriping Algorithm Description
- Offset correction for night scenes similar to what is described in the On-Orbit DNB Calibration section above.
- Gain correction for moonlit scenes using the Moment Matching (MM) technique.
- Complete correction over the entire dynamic range using the Histogram Matching Method (HMM).
2.5. Investigative Methodology
- We conducted a thorough survey of DNB imagery to discover all conditions where striping occurs. We have collected data from many days taken over the span of the S-NPP mission, and viewed all images taken over a 24-h period, including daytime and nighttime. We specifically chose dates near the phases of the moon—new moon, first quarter, full moon, last quarter and some between these.
- In order to observe features in twilight imagery striping we used the Near Constant Contrast algorithm to produce imagery from DNB radiance that minimizes the contrast due to changes in twilight illumination. This algorithm divides the DNB radiance by a factor that is dependent on the computed direct or indirect solar and lunar irradiance. The solar and lunar illumination levels are estimated in the twilight region using a fit to the twilight radiances during new moon . The NCC images here are not from the official version, but use a similar technique to minimize twilight contrast. These NCC images were plotted on a gray scale for evaluation.
- Along with the radiance plots, we evaluated the associated geolocation to understand the solar or lunar illumination conditions associated with a particular striping artifact. This provides clues as to the root cause of the artifact.
- We collected and analyzed several days of on-board calibrator data, and in some cases calibrated the counts into radiances using nominal linear calibration coefficients. We produced plots of this cal sector data as a function of time, ASN and/or detector number. In some cases we simulated the calibration process by computing gain ratios and offsets. The on-board calibrator data reveals uncertainties in the calibration process that can lead to striping.
- We applied the HMM algorithms to SDR data where striping was found and compared the uncorrected images with the corrected images plotted over the same radiance range. We thus evaluated the performance of the destriping algorithm. We also plotted the correction tables used because the detector-to-detector variation seen in the plots are a characterization of the level of striping produced by the DNB.
3.1. Survey of DNB Imagery
3.1.1. Observations for Daytime Scenes
3.1.2. Observations for Twilight Scenes
3.1.3. Observations for Nighttime Scenes
3.2. Evaluating Causes of Striping and Nonuniformities
3.2.1. Stray Light in the Cross-Calibration
3.2.2. Residual Uncorrected Stray Light in the Earth-View
- The correction uses “dark” new moon scenes to characterize the background stray light, but in reality these scenes are not completely dark, since they contain variable amounts of nightglow. The nightglow is estimated from areas where there is no stray light, but since nightglow is not uniform over the scene  this variation leads to over/under-correction.
- The correction look-up tables are constructed as a function of solar zenith angle. In reality, stray light intensity is also a function of solar azimuth angle, and this changes seasonally over the year. This azimuthal dependence is handled empirically; via monthly updates of look-up tables but for times between these synoptic updates the changes in actual stray light cause over/under-correction. This can be somewhat alleviated by taking advantage of the year-to-year 9-days shift in the lunar cycle and using tables created from the previous years. This assumes, however, that the stray light does not change long-term from year to year for other reasons.
- The stray light cannot be characterized for scenes with twilight illumination, so correction in these areas are estimated either through extrapolation  or by setting it to a constant based on the solar zenith angle . Neither of these estimates are perfect, and so this results in residual over/under-correction.
- Changes in offset between the time the correction tables are made and when they are applied can cause additional striping due to drifts in offset described previously. This is because there is striping in the signal used to estimate airglow, and these offset errors become a part of the correction tables. When the offsets drift, their effect remains in the tables and it creates additional striping in the stray light corrected scene.
3.2.3. Electronic Effects—Hysteresis and Crosstalk
3.2.4. Detector Nonlinearity
3.2.5. Half-Angle Mirror Side Differences
3.3. Histogram Matching Method Results
4.1. Changing the DNB Calibration Process to Reduce Striping
4.2. Histogram Matching Method Computational Considerations
- Collecting the ensemble of pixel radiances, filtering the ensemble, producing histograms, and computing correction tables from the histograms.
- Applying the correction tables to the DNB radiances.
Conflicts of Interest
Aggregation Sequence Number
Comprehensive Large Array-data Stewardship System
Histogram Matching Method
Interface Data Processing Segment
Moderate-Resolution Imaging Spectroradiometer
Response Versus Scan
Reflective Solar Band
Senor Data Record
Science Investigator-led Processing System
Suomi National Polar-orbiting Partnership
VIIRS Science Support Team
Visible Infrared Imaging Radiometer Suite
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