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NASA NTRS2022

JPSS-2 VIIRS Day-Night Band Pre-launch Radiometric Calibration and Performance

The first two flight models of the Visible Infrared Imaging Radiometer Suite (VIIRS) instrument continue to operate onboard the Suomi National Polar-orbiting Partnership (S-NPP) and NOAA-20 spacecrafts. The third flight model is set to be launched as one of a complement of instruments onboard the Joint Polar–orbiting Satellite System-2 (JPSS-2) satellite. In addition to its 14 reflective solar bands and 7 thermal emissive bands, VIIRS has a unique Day-Night Band (DNB). The DNB is a panchromatic imager based on CCD detectors, covering a spectral range of about 500 – 900 nm. The DNB is made up of three gain stages, allowing the sensor to operate over a large dynamic range (3×10(−9) - 0.02 W⁄cm(2)/sr). As part of VIIRS pre-launch ground testing, the DNB has been characterized to determine its functionality and performance under a space-like environment. The results are compared against the design specification and to previous VIIRS flight models when applicable. This paper presents a comprehensive summary of the VIIRS DNB prelaunch testing and the results of radiometric and spectral assessments. The expected impact to on-orbit operations and calibrated data products are also discussed.

Infrared Imaging Radiometer Suite (VIIRS)↗

NASA NTRSDate not supplied

Deriving Broadband Radiances from Geostationary Satellite Visible Radiance Channels for Use in the CERES SYN1deg Product

The NASA CERES project was designed to monitor the Earth’s radiation budget, both at the regional and global scale. The CERES SSF1deg single satellite product provides TOA flux observations from Aqua, Terra, NPP, and NOAA-20. The SYN1deg product incorporates observations from Terra, Aqua, and geostationary (GEO) satellites. The GEO observations are included in the SYN1deg product to infer the regional diurnal cycle between CERES observations. GEO satellites throughout the CERES record have different narrowband spectral response functions and do not directly observe radiances over the broadband spectra of CERES instruments. Hence the emphasis of this study is improving the process of deriving broadband radiances from the myriad GEO visible channel observations. We present a new method for converting the narrowband radiances observed from GEO operational satellites. This study relies on radiative transfer model hyper-spectral radiances for many cloud and surface conditions under all angular conditions observed by GEO sensors. First, the hyper-spectral radiances from a radiative transfer model must be convolved with the spectral response function of the visible imager for each GEO sensor in the CERES record. For each angular bin and surface type, the clear-sky and multiple optical depth and phase cloudy-sky narrowband and broadband reflectance pairs are linearly regressed. This study found that, no matter how the scene conditions are stratified, for example the all-sky, clear-sky, ice or water clouds, they all provide similar linear regressions. This includes ocean glint conditions, where the clear-sky reflectance exceeds cloudy conditions and yet the relationship was found to be linear in the given angular bin. This infers that no GEO retrieved cloud information is necessary in the narrowband to broadband conversion process, which relies only on visible channel reflectance. Over land, most of the GEO visible channels do not observe the NIR vegetation reflectance. We intend to use land adjustment factors developed using the TOA reflectance difference between the real-time MODIS .86um and the .64um channel. We validate this approach by using coincident observational data between CERES and MODIS aboard Aqua and Terra as well as GEO and CERES observations .

CERES↗

NASA NTRSDate not supplied

Developing a Spectral Correlation based Method for mitigating Angular Mismatch Effects in Intercalibration Using a Benchmark Hyperspectral Sensor

The CLARREO Pathfinder (CPF) mission will implement a state-of-the-art intercalibration method for transferring CPF’s in-orbit Système Internationale (SI)-traceable reference to the shortwave channel of the Clouds and the Earth’s Radiance Energy System (CERES) and the reflective solar bands of the Visible Infrared Imaging Radiometer Suite (VIIRS) aboard the NOAA-20 satellite platform with a targeted intercalibration methodology uncertainty of 0.3% (k=1). In order to achieve such a high intercalibration accuracy, the CPF intercalibration measurements to be scheduled need to closely match those from CERES and VIIRS in time, space, angles, and wavelength. Despite’s CPF’s two-axis pointing capability to match its boresight line of sight to that of a target sensor, there will be finite residual differences in angular samplings of the two instruments. The potential angular mismatch between CPF and the target sensors can introduce systematic errors in the intercalibration results, and therefore needs to be corrected using a rigorously designed algorithm. The CPF intercalibration team has been developing a correction method for mitigating the impact of these angular anisotropic effects in the CPF-VIIRS and CPF-CERES intercalibration. The method explores the spectral correlation relationship between the reflected solar radiances from the same surface target that would be measured by CPF at two adjacent angles. Our studies have shown that the spectral information based on CPF measurements can be used to accurately predict the spectral radiance or reflectance difference due to a given mismatch in the viewing and solar geometry. The hyper-spectral information can also be used to provide scene stratification without using any auxiliary data, which is critical to reduce the angular correction uncertainty. The angular correction relationship can be well established using simulated CPF-like top-of-atmosphere spectral radiances observed at all sorts of viewing geometry and solar angles and for different scenes. Intensive radiative transfer simulations have been completed using a state-of-art radiative transfer model developed by the CPF team members. The implementation of the algorithm on high-fidelity event simulation data and the characterization for the angular adjustment uncertainty will be presented.

Wan Wu↗

NASA NTRSDate not supplied

Expanding the Time Dimension of Hyperspectral Infrared Sounding Observations: Designing the NUCAPS-Forecast System

Low Earth orbit (LEO) hyper-spectral infrared (IR) sounders have significant yet untapped potential for characterizing thermodynamic environments of convective initiation and ongoing convection. While LEO soundings are of enormous value to weather forecasters, they cannot provide the temporal resolution needed to resolve the rapidly evolving thermodynamics of the convective environment. A novel methodology was developed to extend snapshots of LEO soundings forward in time up to six hours into the future and has been made available within the NOAA National Weather Service Advanced Weather Interactive Processing System (AWIPS) for testing in a quasi-operational setting. This methodology is based on parcel forward-trajectory calculations from the satellite observing time to recreate future soundings of temperature and moisture at regularly gridded intervals in space and time. The soundings are based on NOAA-Unique Combined Atmospheric Processing System (NUCAPS) retrievals from the Suomi NPP and NOAA-20 satellite platforms to allow for rapid near-real time product processing. Science code was transitioned to operational production in 2019 and the product, NUCAPS-Forecast, was tested at the NOAA Hazardous Weather Testbed Spring Experiment in 2019 and 2021. Forecaster feedback has led to improving the product and processing system to address product limitations and improve its applicability for pre-convective forecasting. This presentation highlights the design of the NUCAPS-Forecast system. Solutions were tested and implemented to remove excess moisture and conjoin forward-trajected parcels into vertical profiles for calculating grids of stability indices. Additionally, use cases that explore the tendencies of derived convective available potential energy (CAPE) and convective inhibition (CIN) were investigated. When evaluated against gridded, hourly accumulated rainfall obtained from the Multi-Radar Multi-Sensor (MRMS) observational data set areas with elevated CAPE (reduced CIN) are shown to be associated with areas of precipitation and increase confidence the LEO soundings advected forward in time physically represent atmospheric processes. These results suggest that adiabatic parcel advection of LEO satellite sounding snapshots forward in time exhibits skill in discriminating locations and times that are more likely to lead to convective initiation. Leveraging trajectory modeling to increase the spatial and temporal resolution of LEO IR sounding observations today demonstrates the value of these observations from the future constellation of JPSS satellites and GeoXO mission.

weather↗

NASA NTRS2022

Calibration Inter-Comparison of MODIS and VIIRS Reflective Solar Bands Using Lunar Observations

Multispectral band observations from Terra and Aqua MODIS, launched in December 1999 and May 2002, respectively, and from SNPP and NOAA-20 VIIRS, launched in November 2011 and October 2017, respectively, have continuously enabled a broad range of applications and studies of the Earth system and its changes via a set of geophysical and environmental parameters. The quality of MODIS and VIIRS science and environmental data products relies strongly on the calibration accuracy and stability of individual sensors, as well as their calibration consistency, especially for the data products generated using observations from sensors across different platforms. Both MODIS and VIIRS instruments carry a similar set of on-board calibrators for their on-orbit calibration. Besides, lunar observations are regularly scheduled and implemented in support of their reflective solar bands (RSB) calibration, especially their long-term stability monitoring. In this paper, we provide an overview of MODIS and VIIRS solar and lunar calibration methodologies applied for the RSB on-orbit calibration, and describe the approach developed for their calibration inter-comparisons using lunar observations, including corrections for the effects caused by differences in the relative spectral response and adopted solar spectra between individual sensors. The MODIS and VIIRS calibration inter-comparison results derived from their regularly scheduled lunar observations are presented and discussed, including associated uncertainties and a comparison with those derived using the Earth-view targets. Also discussed are remaining challenges in lunar calibration and inter-comparison for the Earth-observing sensors, as well as on-going efforts for future improvements.

Multispectral band↗

NASA NTRSDate not supplied

CPF-VIIRS Intercalibration Methodology

NASA’s Climate Absolute Radiance and Refractivity Observatory (CLARREO) Pathfinder (CPF) mission will carry a high-accuracy reflected solar (RS) spectrometer to measure the Earth-reflected solar radiation from International Space Station with an SI-traceable radiometric uncertainty of 0.3% (1-sigma). The CPF measurements will serve as an on-orbit reference for intercalibrating other spaceflight RS instruments. The CPF intercalibration team has developed a state-of-the-art approach to calibrate the reflective solar bands (RSB) of the NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) sensor against the CPF benchmark measurements with an aimed intercalibration methodology uncertainty of 0.3%. This presentation will highlight the CPF mission overview and CPF-VIIRS intercalibration approach, as well as other potential outcomes of the CPF-VIIRS intercalibration study that may benefit the broader remote sensing community.

Raj Bhatt↗

NASA NTRS2023

A Nowcasting Approach for Low Earth Orbit Hyperspectral Infrared Soundings Within the Convective Environment

North America↗

NASA NTRSDate not supplied

Novel Hourly-Resolved Global Cloud Property Composite from Operational Satellites Imagers

Numerous applications in satellite remote sensing of surface properties, atmospheric state, composition, and radiation require accurate knowledge of the location and characteristics of clouds. Operational satellite imager radiances are valuable for cloud detection and for deriving many different physical parameters that can be used for a variety of weather, aviation, and energy applications. The NASA Satellite ClOud and Radiation Property retrieval System (SatCORPS) applies a suite of algorithms to meteorological satellite data to provide cloud properties, radiative fluxes and other parameters on a global scale. This paper describes a new global high-resolution dataset of cloud properties made available for community use that is constructed from analyses of a constellation of meteorological satellite imagers. Data taken from Meteosat-8/9 and -11, Himawari-8/9, GOES-16, and -17/-18, Aqua, Terra, Suomi-NPP and NOAA-20 are processed and composited on a 3-km grid to provide hourly global coverage. A historical multi-year dataset is available to serve various needs including modeling challenges related to cloud evaluations and parameterizations. Near real-time data products are also currently available between 60N and 60S. Efforts are underway to operationalize polar orbiting satellite cloud detection methods for low-latency applications over polar regions. The cloud detection and retrieval algorithms have been developed over many years to support NASA weather and climate programs such as the Clouds and Earth’s Radiant Energy System (CERES). To improve the utility of the data products, machine learning and other innovative methods are applied in various ways to help minimize data product uncertainties under the most challenging conditions and to improve their consistency at all times of day. A brief description of the methods highlighting the unique aspects of the SatCORPS data products will be presented along with information on their status and availability.

SatCORPS↗

NASA NTRSDate not supplied

The Impact of Pixel Size on the Characterization of Deep Convective Clouds for Calibration

The NASA CERES project provides the scientific community the observed TOA SW and LW fluxes for climate monitoring and climate model validation. CERES utilizes hourly geostationary imager derived broadband fluxes, which rely on the channel radiances and associated cloud retrievals, are used to estimate the broadband fluxes between CERES observations. This requires stable and consistent cross-platform imager visible channel calibration. The CERES project utilizes deep convective clouds (DCC) as an invariant Earth target to both monitor the stability of sensors and for radiometric scaling. GSICS, an international collaboration, is also evaluating and implementing the DCC invariant target calibration methodology to provide consistent calibration coefficients across geostationary imagers anchored to the AquaMODIS or the NOAA-20 VIIRS calibration reference. Tropical DCC are the brightest, coldest, most Lambertian, top of the atmosphere Earth targets. The DCC invariant target calibration methodology relies on a large ensemble of tropical D CC-identified pixel-level reflectances, which are aggregated as probability density functions (PDF). By assuming the monthly PDF shape is otherwise consistent in time excepting shifts in reflectance caused by changes in the sensor calibration, the imager stability is monitored. Radiometric scaling is accomplished by ratioing the sensor pair DCC PDF reflectance values. The success of the DCC methodology relies on consistent PDF distributions. The goal of this study is to determine the impact of pixel resolution on the DCC reflectance distribution. Single SNPP-VIIRS 750-m and Landsat 8 OLI 30-m granules are aggregated to degrade the pixel resolution from the native level. The DCC pixels are identified using a BT threshold. Most of the brightest DCC pixels are also the coldest, although there are exceptions. It was found that increasing the BT threshold exponentially increased the number of darker pixels. The pixel resolution did not seem to impact the DCC reflectance PDF distribution for pixel resolutions less than 3 km, which suggests that imagers of varying pixel resolutions may be radiometrically scaled to each other using DCC targets.

DCC↗

NASA NTRS2022

A Compilation of Global Bio-Optical in Situ Data for Ocean Colour Satellite Applications – Version Three

A global in situ data set for validation of ocean colour products from the ESA Ocean Colour Climate Change Initiative (OC-CCI) is presented. This version of the compilation, starting in 1997, now extends to 2021, which is important for the validation of the most recent satellite optical sensors such as Sentinel 3B OLCI and NOAA-20 VIIRS. The data set comprises in situ observations of the following variables: spectral remote-sensing reflectance, concentration of chlorophyll-a, spectral inherent optical properties, spectral diffuse attenuation coefficient, and total suspended matter. Data were obtained from multi-project archives acquired via open internet services or from individual projects acquired directly from data providers. Methodologies were implemented for homogenization, quality control, and merging of all data. Minimal changes were made on the original data, other than conversion to a standard format, elimination of some points, after quality control and averaging of observations that were close in time and space. The result is a merged table available in text format. Overall, the size of the data set grew with 148 432 rows, with each row representing a unique station in space and time (cf. 136 250 rows in previous version; Valente et al., 2019). Observations of remote-sensing reflectance increased to 68 641 (cf. 59 781 in previous version; Valente et al., 2019). There was also a near tenfold increase in chlorophyll data since 2016. Metadata of each in situ measurement (original source, cruise or experiment, principal investigator) are included in the final table. By making the metadata available, provenance is better documented and it is also possible to analyse each set of data separately.

ocean color↗

NASA NTRS2023

Evaluation of Systematic Errors on Polarization Parameters from POLDER Instrument Data for Use In CLARREO Pathfinder-VIIRS Intercalibration

One of the CLARREO Pathfinder (CPF) mission’s science objectives is to intercalibrate the reflective solar bands of the NOAA-20 VIIRS instrument against high-accuracy CPF measurements utilizing coincident, co-angled, and co-located footprints acquired over diverse Earth targets. To alleviate the effect of high polarization sensitivity of select VIIRS channels on intercalibration analysis, the CPF team will limit the intercalibration footprints over low-polarized scene types, which will be identified based on an empirical estimation of their degree and angle of polarization (DOP and AOP) using POLDER data. This paper describes the methodology for evaluating systematic errors in the estimation of DOP and AOP for Earth-reflected radiances using POLDER’s polarized bands and investigates their potential impact on CPF-VIIRS intercalibration uncertainty. The systematic errors were found to be less than 0.01 for DOP and less than 2.2 deg. for AOP, which will have a negligible impact on CPF-VIIRS intercalibration uncertainty.

polarization↗

NASA NTRSDate not supplied

Using DSCOVR EPIC as an Additional Calibration Reference to Radiometrically Scale the Geo Imagers for the NASA CERES Project

The NASA CERES project provides climate-quality observed TOA and calculated surface fluxes to the climate community. The CERES instruments are mounted on the Aqua, Terra, NPP, and NOAA-20 low earth orbiting (LEO) satellites, where they are used alongside the VIIRS and MODIS based cloud properties necessary for converting CERES radiance observations into fluxes. The CERES SYN1deg product provides the regional hourly fluxes in between the Terra and Aqua/NOAA-20-CERES observations based on geostationary imager (GEO) radiances. The GEO radiances are radiometrically scaled to the MODIS or VIIRS calibration references. The GEO and MODIS inter-calibration events are limited by the Aqua-MODIS or VIIRS local equator crossing time of 1:30 PM. The DSCOVR satellite orbits the Lagrange-1 (L1) point about 1.5 million kilometers from Earth, and the Earth Polychromatic Imaging Camera (EPIC) instrument onboard DSCOVR has a constant view of the sunlit side of the Earth while taking images ranging from the UV to the NIR throughout the day. While the EPIC sensor has no onboard calibration systems, multiple inter-calibration studies have indicated that EPIC is radiometrically stable, which allows it to potentially be used as a transfer radiometer between imagers. This study will assess the viability of using EPIC as a consistent and stable calibration source for GEO imagers. EPIC will be inter-calibrated with Himawari-8 that has been radiometrically scaled with Aqua. The Himawari-8 based EPIC calibration gains will be verified by the EPIC calibration gains determined directly from Aqua-MODIS matches. The EPIC calibration gains based on Himawari-8 will be stratified by local time to determine any diurnal dependence of the GEO/EPIC calibration strategy.

C O Haney↗

NASA NTRSDate not supplied

Climate Absolute Radiance and Refractivity Observatory (CLARREO) Pathfinder Intercalibration Data Analysis Strategy

One of the prime science objectives of NASA’s Climate Absolute Radiance and Refractivity Observatory (CLARREO) Pathfinder (CPF) mission is to acquire unprecedentedly accurate Système Internationale (SI)-traceable Earth-view measurements that can be used as reference for intercalibrating the Clouds and the Earth’s Radiant Energy System (CERES) and Visible Infrared Imaging Radiometer Suite (VIIRS) instruments onboard NOAA-20 satellite. The hyperspectral nature of CPF measurements will significantly reduce spectrally induced biases when intercalibrating multiband or broadband satellite instruments with CPF. This advancement eliminates the requirement for spectral band adjustment factors, representing a substantial improvement in sensor intercalibration studies. The CPF intercalibration team is aiming to achieve a maximum intercalibration methodology uncertainty of 0.3 % (k=1). Our studies have revealed that the most significant contribution to the targeted uncertainty budget originates from the combined effects of spatial and temporal matching errors. Spatial matching error arises from discrepancies in CPF and target instrument pixel resolution and geolocation uncertainty, while temporal matching error is caused by changes in scene radiances over time, occurring between when the target and reference instruments observe the same scenes. To estimate the maximum expected uncertainty contribution from these sources, spatial and temporal matching noise analyses were conducted using algorithmically filtered Landsat 9 Operational Land Imager (OLI) and Geostationary Operational Environmental Satellite (GOES)-16 ABI CONUS scan data as proxies for CPF and target instruments. In the upcoming conference presentation, we will elaborate on the methodology employed in these experiments, provide details of the data filtering algorithms, and present results of the spatial and temporal matching uncertainty analyses.

Intercalibration↗

NASA NTRSDate not supplied

Progress Towards a Common CERES Cloud Mask Algorithm for MODIS and VIIRS: Evaluation using CALIOP Data

For over two decades The Clouds and the Earth’s Radiant Energy System (CERES) project has endeavored to produce a long-term global climate data record for detecting changes in the Earth’s radiation budget and to improve understanding of how clouds contribute to those changes. CERES incorporates cloud information derived from passive narrowband satellite imaging radiometers, and over the course of the project different instruments have contributed to this effort, namely the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Visible Infrared Imaging Radiometer Suite (VIIRS).Cloud properties have been derived from MODIS on the Aqua satellite for CERES since 2002,and VIIRS on the NOAA-20 satellite launched in 2017 will continue the cloud record once Aqua-MODIS reaches the end of its operational lifetime. However, MODIS and VIIRS have different spectral capabilities and characteristics which complicates our ability to seamlessly transition the record from MODIS to VIIRS. This study evaluates some of the recent algorithm modifications incorporated by the CERES Cloud Working Group towards developing a unified cloud mask algorithm for MODIS and VIIRS that utilizes a set of spectral bands common to both instruments. Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) data products are used to evaluate MODIS and VIIRS regional cloud fraction estimates and identify areas where improvements can be made. The cloud mask algorithm relies on computed estimates of cloud-free top-of-atmosphere radiances to differentiate cloudy and cloud-free imager pixels. The unified cloud mask algorithm currently under development uses a reduced number of spectral bands common to both MODIS and VIIRS and so relies on the accuracy of these simulated cloud-free radiances more heavily than predecessor algorithms, i.e., the CERES Edition 4 cloud mask, which use as much spectral information as available on each imager with less consideration for cross-platform consistency. Our validation strategy using CALIOP data is also described. The CALIOP observations are critical for assessing cloud detection accuracies and for independently confirming cloud-free conditions which enables a more robust evaluation of the simulated cloud-free radiances for biases due to factors such as view angle and the spectral dependence of water vapor absorption. These effects are known to differ for the two satellite instruments. Comparisons of MODIS and VIIRS cloud fractions are presented in context with estimates from CALIOP and from the CERES Edition 4 cloud mask algorithm to gauge current progress in developing accurate and consistent MODIS and VIIRS cloud properties for CERES.

CERES↗

NASA NTRS2023

Calibration of the SNPP and NOAA 20 VIIRS Sensors for Continuity of the MODIS Climate Data Records

Accurate long-term sensor calibration and periodic re-processing to ensure consistency and continuity of atmospheric, land and ocean geophysical retrievals from space within the mission period and across different missions is a major requirement of climate data records. In this work, we applied the Multi-Angle Implementation of Atmospheric Correction (MAIAC)-based vicarious calibration technique over Libya-4 desert site to perform calibration analysis of Visible Infrared Imaging Radiometer Suite (VIIRS) on Suomi National Polar-orbiting Partnership (SNPP) and NOAA-20 satellites. For both VIIRS sensors we characterized residual linear calibration trends and cross-calibrated both sensors to MODerate resolution Imaging Spectroradiometer (MODIS) Aqua regarded as a calibration standard. The relative spectral response (RSR) differences were accounted for using the German Aerospace Center (DLR) Earth Sensing Imaging Spectrometer (DESIS) hyperspectral surface reflectance data. Our results agree with independent vicarious calibration results of both the MODIS/VIIRS Characterization Support Team as well as the CERES Imager and Geostationary Calibration Group within estimated uncertainty of 1–2%. Analysis of MAIAC geophysical products with the new calibration shows a high level of agreement of MAIAC aerosol, surface reflectance and NDVI records between MODIS and VIIRS. Excluding high aerosol optical depth (AOD), all three sensors agree in AOD with mean difference (MD) less than 0.01 and residual mean squared difference rmsd ∼ 0.04. Spectral geometrically normalized surface reflectance agrees within rmsd of 0.003–0.005 in the visible and 0.01–0.012 at longer wavelengths. The residual surface reflectance differences are fully explained by differences in spectral filter functions. Finally, difference in NDVI is characterized by rmsd ∼ 0.02 and MD less than 0.003 for NDVI based on VIIRS imagery bands I1/I2 and less than 0.01 for NDVI based on VIIRS radiometric bands M5/M7. In practical sense, these numbers indicate consistency and continuity in MAIAC records ensuring the smooth transition from MODIS to VIIRS.

MAIAC↗

NASA NTRSDate not supplied

On-Orbit Calibration Assessment of NOAA-21 VIIRS Thermal Emissive Bands and Implications for JPSS-4 VIIRS

The Visible Infrared Imaging Radiometer Suite (VIIRS) instrument onboard the NOAA-21 (N21) satellite has been successfully operating for over three years collecting valuable scientific measurements. A large suite of weather forecasting models and scientific research applications are supported with the VIIRS measurements from N21 combined with those acquired from the VIIRS instrument onboard the S-NPP and NOAA-20 spacecraft. Among the 22 VIIRS spectral bands, there are seven thermal emissive bands (TEB) covering the 3.7 to 12.2 µm spectral range at two different spatial resolutions. The VIIRS TEB detectors are calibrated onboard using a blackbody at a controlled temperature and a space view for background signal measurement. In this paper, we discuss the on-orbit performance of the N21 VIIRS TEB using various parameters such as the detector gain, noise, and offset. This on-orbit assessment provides critical insights into instrument behavior and calibration performance that will inform pre-launch testing and post-launch validation strategies for the upcoming JPSS-4 VIIRS mission scheduled for launch in 2027. The methodologies and lessons learned from characterizing the N21 TEB performance will enable more efficient and comprehensive radiometric assessment of the next VIIRS instrument. Specific focus is placed on identifying performance trends and anomaly signatures observed during the N21 commissioning phase that can enhance the JPSS4 instrument checkout procedures and accelerate the transition to routine on-orbit operations. The established performance baselines and uncertainty estimates will serve as an acceptance criterion for the JPSS-4 commissioning activities, ensuring mission readiness and data continuity for operational users.

Amit Angal↗

NASA NTRSDate not supplied

PRELIMINARY JPSS-3 VIIRS POLARIZATION SENSITIVITY AND COMPARISON WITH S-NPP, JPSS-1 AND -2

The Visible-Infrared Imaging Radiometer Suite (VIIRS) was first launched on-board the Suomi National Polar-orbiting Partnership (S-NPP) spacecraft in October of 2011. There have been three subsequent builds of the VIIRS sensor for the Joint Polar Satellite System (JPSS) program with JPSS-1, -2 and -3 having launch dates of November 2017, March 2022 and 2026 respectively. There is also a JPSS-4 VIIRS, that is in hardware integration during 2020, with a launch date of 2031. VIIRS has 22 bands: 7 thermal emissive bands (TEBs), 14 reflective solar bands (RSBs) and a Day Night Band (DNB). Ocean Color/Chlorophyll (OCC) products use calibrated Science Data Records (SDRs) for bands M1-M7(0.412-0.865μm) to compute their ocean chemistry products. These bands require accurate polarization sensitivity characterization to compensate for polarized upwelling Rayleigh scatter and produce accurate OCC Environment Data Products (EDRs). VIIRS polarization sensitivity requirement failures have driven hardware modifications to the bandpass filters and dichroic beam splitter over the program. This paper will discuss the preliminary JPSS-3 polarization results and how these hardware modifications, as the JPSS program progresses, have affected the sensor performance. Comparisons of the polarization sensitivities between sensor builds will be discussed along with the hardware modifications that contributed to their differences.

VIIRS↗

NASA NTRSDate not supplied

Preliminary JPSS-3 VIIRS Polarization Sensitivity and Comparison with S-NPP, JPSS-1 and -2

The Visible-Infrared Imaging Radiometer Suite (VIIRS) was first launched on-board the Suomi National Polar-orbiting Partnership (S-NPP) spacecraft in October of 2011. There have been three subsequent builds of the VIIRS sensor for the Joint Polar Satellite System (JPSS) program with JPSS-1, -2 and -3 having launch dates of November 2017, March 2022 and 2026 respectively. There is also a JPSS-4 VIIRS, that is in hardware integration during 2020, with a launch date of 2031. VIIRS has 22 bands: 7 thermal emissive bands (TEBs), 14 reflective solar bands (RSBs) and a Day Night Band (DNB). Ocean Color/Chlorophyll (OCC) products use calibrated Science Data Records (SDRs) for bands M1-M7 (0.412-0.865μm) to compute their ocean chemistry products. These bands require accurate polarization sensitivity characterization to compensate for polarized upwelling Rayleigh scatter and produce accurate OCC Environment Data Products (EDRs). VIIRS polarization sensitivity requirement failures have driven hardware modifications to the bandpass filters and dichroic beam splitter over the program. This paper will discuss the preliminary JPSS-3 polarization results and how these hardware modifications, as the JPSS program progresses, have affected the sensor performance. Comparisons of the polarization sensitivities between sensor builds will be discussed along with the hardware modifications that contributed to their differences.

VIIRS↗