Launched in November 2017, the National Oceanic and Atmospheric Administration-20 (NOAA-20) Visible Infrared Imaging Radiometer Suite (VIIRS) has successfully operated over five years and produced high-quality sensor data records (SDRs), which have significantly contributed to the Earth’s environmental and climate change studies. The VIIRS instrument collects data in the reflective solar bands (RSBs) from bands M1 to M11 and I1 to I3 with two spatial resolutions of 375 m for imaging ($I$) bands and 750 m for moderate resolution ($M$) bands covering a wavelength range from 401 to 2284 nm. For the RSBs on-orbit radiometric calibration, VIIRS primarily uses solar diffuser (SD) observations along with alternative long-term lunar calibrations and deep convective cloud (DCC) trends. The NOAA VIIRS SDR team observed upward long-term trends after three years in the lunar calibration coefficients (called lunar F-factors) compared to the initial on-orbit SD F-factors. These long-term lunar trend changes were validated with the DCC observation results and the operational radiometric calibration coefficient (called F-PREDICTED) lookup table (LUT) was updated in November of 2021, which was proportional to observed radiance. After five years of on-orbit operations, the performance of the current operational F-PREDICTED LUT was evaluated in comparison with the long-term DCC trends. After application of the LUT, the results showed excellent on-orbit radiometric calibration stability providing confidence for the user communities of NOAA-20 VIIRS SDR products.

The Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the National Oceanic and Atmospheric Administration-21 (NOAA-21) satellite was launch on November 10, 2022, following the successful operations of the VIIRS onboard the Suomi National Polar-orbiting Partnership (S-NPP) and NOAA-20 satellites. This article presents NOAA-21 VIIRS thermal emissive bands (TEBs) early on-orbit calibration performance, including instrument temperature telemetry trending, TEB gains, noise, and calibration stability as well as biases in the NOAA operational sensor data records (SDR). Different from S-NPP and NOAA-20, NOAA-21 VIIRS TEBs have experienced distinct on-orbit gain changes during its early mission, caused by detector responsivity degradations, mid-mission outgassing (MMOG), and the cold focal plane assembly setpoint temperature switch. The calibration stability and biases of NOAA-21 VIIRS TEB SDRs were analyzed by intercomparing with co-located Cross-Track Infrared Sounder (CrIS) observed and gap-filled spectra. NOAA-21 TEB SDRs have been stable, except for small changes caused by known VIIRS or CrIS instrument setting or calibration updates. VIIRS longwave infrared (LWIR) bands agree with CrIS within 0.1 K; M13 agrees with CrIS ~0.13 K. Calibration parameters derived from the spacecraft pitch maneuver data were applied for the first time in the operational processing, and LWIR scan angle and scene temperature-dependent biases were effectively reduced after that update. Similar to S-NPP and NOAA-20 VIIRS, NOAA-21 VIIRS TEBs also exhibit calibration anomalies during the blackbody warm-up/cool-down (WUCD) tests. NOAA-21 WUCD bias correction coefficients were developed and deployed to the operations for supporting sea surface temperature (SST) applications.

The Joint Polar Satellite System-2 (JPSS-2) was successfully launched on November 10, 2022 and renamed as NOAA-21 after it reached its final polar orbit. Twenty-five days after launch, on December 5, 2022, the NOAA-21 Visible Infrared Imaging Radiometer Suite (VIIRS) started collecting science data. After intensive analysis, calibration and validation of VIIRS science raw data record (RDR), telemetry RDR, and sensor data record (SDR) radiometric and geolocation data products, the NOAA-21 VIIRS SDRs reached Beta maturity on Feb. 23, 2023 and provisional maturity on March 30, 2023. This paper provides an update on the NOAA-21 VIIRS SDR availability, post-launch calibration and validation activities, and SDR quality assessments.

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 Bands (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 the METHOD2021 and METHOD2019 on TEB SDRs are generally comparable. The METHOD2019 is simpler, while the 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.

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).

In the NOAA operational processing, the Thermal Emissive Band (TEB) data from the VIIRS onboard the NOAA-20 and S- NPP satellites are not calibrated if all scans in a granule are flagged as lunar intrusion into space view (SV). As a result, more than 100 NOAA-20 and S-NPP VIIRS single-gain TEB granules are un-calibrated each year. For M13 (dual-gain fire detection band), the number of un -calibrated granules due to lunar intrusion is doubled because of an additional bug in the operational processing software. This study presents a Lowest N Algorithm for calibrating VIIRS TEB during lunar intrusions. It takes advantage of the fact that the extent of the full moon image is smaller than the field of view of VIIRS SV. Moreover, the bug that affects M13 calibration was also fixed. A VIIRS SDR algorithm code change package has been implemented in the NOAA operational processing since March 30,2021.

This paper presents NOAA-20 and S-NPP Visible Infrared Imaging Radiometer Suite (VIIRS) Reflective Solar Bands (RSB) and Thermal Emissive Bands (TEB) Sensor Data Records (SDR) performance and recent improvements to support user communities. Results for NOAA-20 VIIRS and in 2019 are emphasized. NOAA-20 VIIRS geolocation errors are within ±100 m, comparable to S-NPP; RSBs have been stable after achieved validated maturity status, except that small upward trends were observed; TEBs agree with co-located Cross-track Infrared Sounder (CrIS) observations within ~0.1 K at nadir, more stable (relative to CrIS) compared to S-NPP TEBs in 2019. NOAA-20 RSBs continue bias ~2-4.5% lower than S-NPP. Three major improvements to VIIRS SDRs, including the new operational M6 saturation rollover flagging method, the operational TEB warm-up/cool-down bias correction, and the latest results for correcting NOAA-20 TEB scan angle/scene temperature dependent biases, are also presented to address users' concerns.

The NOAA-20 (N-20) VIIRS has successfully operated for more than two years since its launch in November 2017. Shortly after completing its initial instrument check-outs and post-launch testing (PLT) activities, the N-20 VIIRS sensor data records (SDR) achieved the beta, provisional, and validated maturity status in January, February, and April 2018, respectively. In this paper, we briefly describe the instrument on-orbit operation and calibration activities, provide an overall assessment of its on-orbit performance, and discuss the methodologies developed to maintain and improve sensor calibration and data quality. As illustrated in this paper, the N-20 VIIRS continues to perform with excellent stability, allowing high-quality environmental data records (EDR) to be generated from its well-calibrated SDR.

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 μm. 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.

Mathematical methods of information theory appear to provide a useful language to describe how stimuli are encoded in activities of signaling effectors. Exploring the information-theoretic perspective, however, remains conceptually, experimentally and computationally challenging. Specifically, existing computational tools enable efficient analysis of relatively simple systems, usually with one input and output only. Moreover, their robust and readily applicable implementations are missing. Here, we propose a novel algorithm, SLEMI-statistical learning based estimation of mutual information, to analyze signaling systems with high-dimensional outputs and a large number of input values. Our approach is efficient in terms of computational time as well as sample size needed for accurate estimation. Analysis of the NF-κB single-cell signaling responses to TNF-α reveals that NF-κB signaling dynamics improves discrimination of high concentrations of TNF-α with a relatively modest impact on discrimination of low concentrations. Provided R-package allows the approach to be used by computational biologists with only elementary knowledge of information theory.

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.

The Visible Infrared Imaging Radiometer Suite (VIIRS) onboard Suomi-NPP is equipped with a Day/Night Band (DNB), a major advancement in nighttime imaging capability. DNB data quality has been largely improved through several calibration updates since early launch. The reprocessed VIIRS DNB sensor data record (SDR) data at the NOAA Center for Satellite Applications and Research (STAR) accommodate all updates, such as DNB modulated relative spectral responses (RSRs), monthly DNB offset and gain ratio, straylight correction, and geolocation terrain correction since launch. In addition, one of the major improvements is in the DNB offset computing using the pitch maneuver-based deep space view that is free of airglow. The reprocessed DNB data results in radiometrically consistent calibrated SDR that allows the user community to use high-quality DNB data in long-term applications. Until the end of 2016, DNB high gain stage (HGS) operational calibration involved estimation of dark offsets using the new moon-based nighttime measurements over the Pacific Ocean. However, the faint emission from airglow during the new moon nights led to an overestimation of the dark offset by an amount equivalent to that of the airglow. This directly impacts the calibrated data by underestimating the calibrated radiance values. The impact is prominent at low light radiance, which can also lead to negative radiances. This paper quantifies the reprocessing led improvements in the calibrated VIIRS DNB data, analyzes the impact of airglow on operational calibrated radiance product, and explains how the absolute accuracy for low light radiance has been largely improved for the entire reprocessed DNB archive.

Since launch in November 2018, the VIIRS on-board the NOAA-20 (or JPSS-1) satellite has completed its initial intensive on-orbit check-outs and several key calibration and validation activities scheduled to help evaluate sensor at launch performance. This paper provides a brief overview of NOAA-20 VIIRS on-orbit operation and calibration activities, presents early results derived from its on-board calibrators and lunar observations, and discusses potential improvements and future effort to assure sensor data product quality.

This paper presents the recent progress in the cal/val of the Visible Infrared Imaging Radiometer Suite (VIIRS) on Suomi National Polar Orbiting Partnership (NPP) since launch, and Joint Polar Satellite System (JPSS) J1 which will be launched in 2017. The Suomi NPP VIIRS instrument continues to perform well with a very stable calibration according to extensive comparisons with other instruments and at vicarious validation sites. The calibration accuracy for most bands meet the specification and user needs, although additional efforts are being made to meet more stringent needs such as ocean color applications. The VIIRS Sensor Data Records (SDR) have been operationally used at the Alaska National Weather Service, and have been used to generate a large number of environmental data records ranging from aerosols, fire, to ocean color, sea surface temperature, vegetation, and many others. However, since many updates have been made to the operational processing of VIIRS since launch, inconsistency exists in the long term data records. To address this issue, the VIIRS SDR team is preparing for the reprocessing of the VIIRS historical data using the new automated calibration module RSBAutocal, with the latest calibration coefficients. The reprocessed data will be more consistent over the entire history of VIIRS with all known corrections. At the same time, significant efforts are devoted to the prelaunch calibration of the JPSS J1 VIIRS. While in general the J1 VIIRS performance meets specifications, there are performance waivers and as a result, mitigations have to be developed. This includes the implementation of a new aggregation scheme to address the nonlinear response of the VIIRS Day/Night Band (DNB). Other waivers include the larger than expected polarization sensitivity which will impact the ocean color bands and additional corrections will be needed postlaunch. In addition, the team has also been performing a trade study for adding a water vapor band to future VIIRS. Many of these efforts have been documented in the VIIRS special issue in the remote sensing open access journal which is being finalized to reach out to a broader user community.

The first VIIRS instrument has successfully operated for more than 4 years on-board the Suomi-National Polar-orbiting Partnership (S-NPP) spacecraft. The sensor data records (SDR) derived from VIIRS on-orbit observations have been used to produce many environment data records (EDR), enabling a wide range of applications by the users from operational and research community. This paper provides an overview of instrument operations and its calibration activities, and presents an update of its radiometric performance, in terms of on-orbit changes in sensor spectral band responses and noise characterization. It also describes the effort made to improve sensor calibration, and the strategies developed in support of producing consistent SDR and, consequently, the EDR with improved quality.

Radiometric calibration coefficients for the VIIRS reflective solar bands have been reprocessed from the beginning of the Suomi NPP mission until present. An automated calibration procedure, implemented in the JPSS operational data production system, was applied to reprocess onboard solar calibration data and solar diffuser degradation measurements. The latest processing parameters from the operational system were used to include corrected solar vectors, optimized directional dependence of attenuation screens transmittance and solar diffuser reflectance, updated pre-launch calibration coefficients without an offset term, and optimized Robust Holt-Winters filter parameters. The parameters were consistently used to generate a complete set of the radiometric calibration coefficients for the entire duration of the Suomi NPP mission. The reprocessing has also demonstrated that the automated calibration procedure can be successfully applied to all solar measurements acquired from the beginning of the mission until the full deployment of the automated procedure in the operational processing system.

In this study, two methods were used to evaluate radiometric calibration of the VIIRS sensor data records: (1) imaging of radiometrically stable Earth's surfaces and (2) SNO (Simultaneous Nadir Overpass) observations by VIIRS and other satellite instruments. Measurements acquired by VIIRS at the stable Dome C calibration site in Antarctica confirm that a faster-than-expected degradation of the radiometric response occurs for selected spectral bands in the visible and near-infrared region, with other bands remaining stable. SNO comparisons with MODIS show that the implemented regular updates of the radiometric calibration coefficients have stabilized the calibration and that the VIIRS and MODIS measurements are in agreement.

The launch of the SNPP with VIIRS signifies a new era of continued operational global earth observations with moderate resolutions. In the calibration/validation of VIIRS, it is essential to ensure that its measurements are consistent with those from previous instruments such as MODIS and AVHRR because longterm study of the earth relies on consistent satellite measurements with decadal data sets. This study focuses on the use of an extension of the traditional simultaneous nadir overpass (SNO) approach for performance evaluation and intercomparison of the VIIRS instrument with MODIS and AVHRR. Unlike SNO approach which is limited to polar regions only, the extended SNO (SNOx) provides an opportunity to compare satellite instruments at low latitudes over wide dynamic ranges. In addition, a new Geolocation Matching Method (GSM) method is developed to match a full VIIRS scene to MODIS. This tool uses an optimized algorithm with reduced run time and has been primarily used to analyze the intersatellite radiometric consistency and geolocation accuracy.

One example of an application of EOS science data is the computation of aerodynamic roughness for momentum and its ultimate application to meteorological and atmospheric transport modeling. Currently, data products derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) and other sensors such as Landsat and ASTER are being used to estimate vegetation and urban (non-vegetated) aerodynamic roughness fields at local to regional scales. The goal is to incorporate those satellite-based roughnesses into both numerical meteorological models and atmospheric transport and dispersion models, to improve understanding and forecasting of wind and pressure fields, as well as plume dispersion. Once achieved, the results should be of practical benefit to society in the areas of improved weather and climate forecasting and better prediction of the dispersion of pollutants and other hazardous releases to the atmosphere.