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The Visible Infrared Imaging Radiometer Suite (VIIRS) is onboard the Suomi National Polar-orbiting Partnership (S-NPP) and the National Oceanic and Atmospheric Administration-20 (NOAA-20) satellites. This study presents a method for estimating VIIRS thermal emissive band (TEB) response versus scan (RVS) and calibration offsets simultaneously using on-orbit pitch maneuver data (METHOD2021). Raw Earth view (EV) RVS is estimated using an existing method (METHOD2019), with prelaunch RVS and calibration offsets as first guesses. Errors in the calibration offset are derived based on a prelaunch data-based assumption. Moreover, RVS and calibration offsets are optimized iteratively using the updated calibration coefficients until results converge. Compared to the METHOD2019, the METHOD2021-derived RVS is not affected by errors in the calibration offsets. Evaluation results using independent co-located Cross-track Infrared Sounder (CrIS) observations indicate that the METHOD2021 could effectively mitigate NOAA-20 scan angle- and scene temperature-dependent biases in M15, M16, and I5, as well as the cold bias in S-NPP M15. S-NPP TEB striping at cold scene temperatures can also be significantly reduced. Furthermore, NOAA-20 I5 and M15 measurements become more in family with S-NPP VIIRS and other radiometers at extremely low temperatures. Our analysis indicates that the impacts of METHOD2021 and METHOD2019 on TEB sensor data records (SDRs) are generally comparable. The METHOD2019 is simpler, while METHOD2021 better explains the root causes of the VIIRS TEB scan angle- and scene temperature-dependent biases. Both can be used for improving VIIRS TEB on-orbit calibration, especially at cold scenes. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicle (UAV) offers an unprecedented observing potential with ultrahigh spatial and temporal resolutions and high flexibility. However, it remains difficult to solve the radiometric inconsistency of multiflight UAV imagery. This study proposed a relative radiometric correction method for multiflight UAV images based on concurrent satellite imagery (MACA) consisting of two steps, i.e., cross-sensor spectral fitting (CSF) and fine-resolution spectral calibration (FSC). In CSF, relationships of multiflight UAV reflectance and the concurrent satellite reflectance were established for generating the fine-resolution reference imagery. Subsequently, a relative radiometric correction model was constructed in the FSC step to correct multiflight UAV imagery. The performance of MACA was evaluated using multiflight UAV datasets acquired on six cropland sites and a concurrent Sentinel-2 image. Compared with four typical or state-of-the-art relative correction methods, the correction using MACA yielded better consistency between UAV and Sentinel-2 data, regardless of individual spectral bands ($\text{R}^{2} =0.79$ –0.86, root mean square error (RMSE) $=0.004$ –0.019) or vegetation indices (VIs) ($\text{R}^{2} =0.80$ –0.86, RMSE $=0.024$ –0.054). Moreover, the prediction of plant nitrogen accumulation (PNA) based on the MACA-corrected UAV data had the highest accuracy and showed the spatial variation most significantly within and between fields for all sites. The results demonstrated that MACA was more robust in reducing spectral mismatch across sensors and eliminating the subjective error of pseudo-invariant features (PIFs) selection. MACA has the potential to be used to cross-calibrate multisensor data into a consistent standard, which will benefit multisensor synergies. [ABSTRACT FROM AUTHOR]

This article presents an improved radiance limit verification and saturation rollover flagging method for the Visible Infrared Imaging Radiometer Suite (VIIRS) reflective solar band (RSB) and thermal emissive band (TEB) sensor data records (SDRs). Platform (satellite)-dependent radiance limits are introduced to account for the different radiometric characteristics of the VIIRS onboard the Suomi National Polar-Orbiting Partnership (S-NPP) and the National Oceanic and Atmospheric Administration-20 (NOAA-20) satellites. Two reference band-based tests are added for better saturation rollover flagging. Evaluation results using NOAA-20 and S-NPP reprocessed and on-orbit SDRs indicate that saturation rollover flagging can be significantly improved. To date, the improved scheme of saturation rollover flagging is applied only to NOAA-20 and S-NPP M6. The application of this scheme to other RSB and TEB bands can be achieved by updating the Quality Assurance lookup table. Moreover, the issue of radiance and brightness temperature mismatch for NOAA-20 TEBs is also resolved. A VIIRS SDR algorithm code change for the improved method has been implemented in the NOAA operational processing for NOAA-20 and S-NPP since March 25, 2019. It can also be applied to the VIIRS onboard the future Joint Polar Satellite System satellites (J2–J4). [ABSTRACT FROM AUTHOR]

The National Oceanic and Atmospheric Administration (NOAA)-20 Visible Infrared Imaging Radiometer Suite (VIIRS) was launched on November 18, 2017, and it has been operational for more than two years and follows the first Joint Polar Satellite System (JPSS) series of the Suomi National Polar-orbiting Partnership (S-NPP) mission. VIIRS has 14 reflective solar bands (RSBs) covering a spectral range of 0.41– $2.3~\mu \text{m}$. The primary source of RSB calibration is the solar diffuser (SD), and the time-dependent SD degradation is monitored by the SD stability monitor (SDSM). The initial instability of the SD degradation (H-factor) was resolved by updating SDSM sun screen transmittance function combining yaw maneuver data and on-orbit SDSM data sets. After the H-factor improvements, the VIIRS RSB calibration coefficients (F-factors) are updated and applied to the operational Sensor Data Record (SDR) product generation. To validate the SD F-factors, the lunar F-factors are calculated by using a lunar irradiance model and comparing the trend differences between them. Over the two years of operation, decreasing trends have been calculated with the SD F-factors, whereas constant lunar F-factors were observed in bands M1–M4. With these discrepancies, the operational F-factors remained unchanged since April 2018 because the deep convective cloud (DCC) and cross-calibration comparison results did not show any further degradations in these bands. All the possible radiometric calibration sources, such as SD and lunar F-factors, DCC trends, and cross-calibration results, are monitored, compared, and applied by the NOAA VIIRS SDR science team for the best quality of the VIIRS SDR product. [ABSTRACT FROM AUTHOR]

The Suomi National Polar-orbiting Partnership Program Visible Infrared Imaging Radiometer Suite (VIIRS) thermal emissive bands (TEB) have been performing well during nominal operations since launch. However, small but persistent calibration anomalies are observed in all TEBs during the quarterly blackbody (BB) warm-up/cool-down (WUCD) events. As a result, the time series of daytime sea surface temperature (SST) (derived from bands M15–M16) show warm spikes on the order of 0.25 K. This paper suggests that VIIRS TEB WUCD biases are band dependent, with daily-averaged biases about −0.04 and 0.05 K for I4 and I5, and −0.05, −0.05, 0.11, 0.09, and 0.05 K for M12–M16, respectively. Two correction methods—Ltrace and WUCD-C—have been implemented and evaluated using colocated observations from the Cross-track Infrared Sounder (CrIS), radiative transfer simulations, and SST retrievals. Also an error in the National Oceanic and Atmospheric Administration operational processing was identified and fixed. Both correction methods effectively minimize WUCD-induced SST anomalies. The Ltrace method works well for I5, M12, and M14–M16, with residual biases about 0.01 K. The WUCD-C method, on the other hand, performs well to correct WUCD biases in all TEBs, with residual biases also about 0.01 K. However, it introduces warm biases relative to CrIS at cold scene temperatures, which requires further study. Applying nonequal BB thermistor weights improves calibration at BB temperature set points, but its impact on daily-averaged WUCD biases is small. The proposed methodologies may also be applied to the VIIRS onboard the follow-on Joint Polar Satellite System satellites. [ABSTRACT FROM AUTHOR]