Abstract
The first Visible Infrared Imaging Radiometer Suite (VIIRS) instrument onboard the Suomi National Polar-orbiting Partnership (S-NPP) satellite has been collecting Earth observations since 2011. Despite challenges created by ongoing telescope throughput degradation and uneven solar diffuser (SD) degradation, the on-orbit radiometric calibration of the reflective solar bands (RSBs) has been successfully carried out utilizing observations of the sun-lit SD panel. Recently, an increase of striping in S-NPP VIIRS Sensor Data Record (SDR) RSB imagery has been observed, especially for the short-wavelength bands M1 to M4. This observation is also consistent with an increase, during multiple years of on-orbit operation, in the divergence of the on-orbit SD-based calibration factors (F-factors) among the different detectors for those bands. To reduce the observed striping, the NOAA STAR VIIRS SDR Cal/Val team utilized measurements over Deep Convective Clouds (DCC) to develop correction factors that can be applied during the operational automated processing of the S-NPP VIIRS solar calibration measurements in IDPS (RSBAutoCal). In this study, we perform analysis to quantify striping in S-NPP VIIRS M1-M4 imagery using a detector-level cumulative histogram approach over multiple selected cases of homogenous land and ocean targets. We also quantitatively evaluate the impact of DCC-based striping correction by performing striping comparisons using operational (without striping correction) and reprocessed (with striping correction) data. Furthermore, we examine the dependence of the striping correction performance on the detector gain stage involved. The results suggest that the applied corrections can effectively reduce striping for the majority of cases, especially in M1 and M2, where a striping reduction on the order of similar to 2% can be achieved.