Suomi NPP VIIRS DNB and RSB M Bands Detector-To-Detector and HAM Side Calibration Differences Assessment Using a Homogenous Ground Target

Near-nadir observations of the Libya 4 site from the S-NPP VIIRS Day-Night Band (DNB) and Moderate resolution Bands (M bands) are used to assess the detector calibration stability and half-angle mirror (HAM) side differences. Almost seven years of Sensor Data Records products are extracted from the Libya 4 site center over an area of 3232 pixels. The mean values of the radiance from individual detectors per HAM side are computed separately. The comparison of the normalized radiance between detectors indicates that the detector calibration differences are wavelength dependent and the differences have been slowly increasing with time for short wavelength bands, especially for M1-M4. The maximum annual average differences between DNB detectors are 0.77% in 2017 at HAM-A. For the M bands, the maximum detector differences in 2017 are 1.7% for M1, 1.8% for M2, 1.3% for M3, 1.2% for M4, 0.67% for M5, 0.75% for M7, 0.57% for M8, 13% for M9, 0.63% for M10, and 0.66% for M11. The average HAM side A to B difference in 2017 are 0.00% for DNB, 0.22% for M1, 0.17% for M2, 0.15% for M3, 0.09% for M4, -0.07% for M5, 0.02% for M7, 0.01% for M8, 1.4% for M9, 0.01% for M10, and 0.03% for M11. Results for M6 are not available due to the signal saturation and M9 results are not accurate because of the low reflectance from the desert site and the strong atmospheric absorption in this channel. The results in this study help scientists better understand each detectors performance and HAM side characteristics. Additionally, they provide evidence and motivation for future VIIRS calibration improvements

Assessing the Effects of Suomi NPP VIIRS M15/M16 Detector Radiometric Stability and Relative Spectral Response Variation on Striping

Modern satellite radiometers have many detectors with different relative spectral response (RSR). Effect of RSR differences on striping and the root cause of striping in sensor data record (SDR) radiance and brightness temperature products have not been well studied. A previous study used MODTRAN radiative transfer model (RTM) to analyze striping. In this study, we make efforts to find the possible root causes of striping. Line-by-Line RTM (LBLRTM) is used to evaluate the effect of RSR difference on striping and the atmospheric dependency for VIIRS bands M15 and M16. The results show that previous study using MODTRAN is repeatable: the striping is related to the difference between band-averaged and detector-level RSR, and the BT difference has some atmospheric dependency. We also analyzed VIIRS earth view (EV) data with several striping index methods. Since the EV data is complex, we further analyze the onboard calibration data. Analysis of Variance (ANOVA) test shows that the noise along track direction is the major reason for striping. We also found evidence of correlation between solar diffuser (SD) and blackbody (BB) for detector 1 in M15. Digital Count Restoration (DCR) and detector instability are possibly related to the striping in SD and EV data, but further analysis is needed. These findings can potentially lead to further SDR processing improvements

Assessing the Effects of Suomi NPP VIIRS M15/M16 Detector Radiometric Stability and Relative Spectral Response Variation on Striping

Modern satellite radiometers have many detectors with different relative spectral response (RSR). Effect of RSR differences on striping and the root cause of striping in sensor data record (SDR) radiance and brightness temperature products have not been well studied. A previous study used MODTRAN radiative transfer model (RTM) to analyze striping. In this study, we make efforts to find the possible root causes of striping. Line-by-Line RTM (LBLRTM) is used to evaluate the effect of RSR difference on striping and the atmospheric dependency for VIIRS bands M15 and M16. The results show that previous study using MODTRAN is repeatable: the striping is related to the difference between band-averaged and detector-level RSR, and the BT difference has some atmospheric dependency. We also analyzed VIIRS earth view (EV) data with several striping index methods. Since the EV data is complex, we further analyze the onboard calibration data. Analysis of Variance (ANOVA) test shows that the noise along track direction is the major reason for striping. We also found evidence of correlation between solar diffuser (SD) and blackbody (BB) for detector 1 in M15. Digital Count Restoration (DCR) and detector instability are possibly related to the striping in SD and EV data, but further analysis is needed. These findings can potentially lead to further SDR processing improvements