Search Results (55)

The Joint Polar Satellite System (JPSS) is a collaborative program between NASA and NOAA to provide scientific measurements from multiple polar-orbiting satellites. The development, testing, launch, and operation of the satellites is jointly overseen by NASA and NOAA, with NASA responsible for developing and building instruments, spacecraft, ground systems, and launching into orbit. While three VIIRS instruments are currently on-orbit, spacecraft integration of the two VIIRS instruments planned for launch on the JPSS-3 and -4 spacecraft is ongoing. The latest build in the series, set to be launched on the JPSS-4 platform, recently completed its main ground calibration program at the vendor facility. This program covered a comprehensive series of performance metrics designed to ensure that the instrument can maintain its calibration successfully on-orbit. In this paper, we present the results from the radiometric calibration process, which includes metrics such as dynamic range, signal-to-noise ratio, noise equivalent differential temperature, polarization sensitivity, scattered light response, relative spectral response, response versus scan angle, and crosstalk. All key metrics have met or exceeded their design requirements, with some minor exceptions. Also included are comparisons with previous VIIRS instruments, as well as a description of their expected performance once on-orbit. Full article

Following the success of Visible Infrared Imaging Radiometer Suite (VIIRS) instruments currently operating onboard the Suomi-NPP, NOAA-20, and NOAA-21 spacecraft, preparations are underway for the final two VIIRS instruments for the Joint Polar Satellite System 3 (JPSS-3) and 4 (JPSS-4) platforms. To that end, each instrument underwent a comprehensive sensor-level test campaign at the Raytheon Technologies, El Segundo facility, in both ambient and thermal-vacuum environments. Unique among the 22 VIIRS sensing bands is the day-night band (DNB)—a panchromatic imager that leverages multiple CCD detectors set at different gain levels to make continuous (day and night) radiometric observations of the Earth. The results from the JPSS-3 and JPSS-4 VIIRS DNB pre-launch testing are presented and compared against the design specifications in this paper. Characterization parameters include dark offset, gain, linearity, uniformity, SNR, and uncertainty. Performance relative to past builds is also included where appropriate. Full article

The Joint Polar Satellite System 3 (JPSS-3) and -4 (JPSS-4) Visible Infrared Imaging Radiometer Suite (VIIRS) instruments are the last in the series (S-NPP VIIRS launched in October 2011, JPSS-1 VIIRS launched in November 2017, and JPSS-2 VIIRS launched in November 2022) of highly advanced polar-orbiting environmental satellites. Both instruments underwent a comprehensive sensor-level thermal vacuum (TVAC) testing at the Raytheon Technologies El Segundo facility to characterize the spatial, spectral, and radiometric aspects of the VIIRS sensor performance. This paper focuses on the radiometric performance of the 14 reflective solar bands (RSBs) that cover the wavelength range from 0.41 to 2.3 µm. Key instrument calibration parameters such as instrument gain, signal-to-noise ratio (SNR), dynamic range, and radiometric calibration uncertainty were derived from the TVAC measurements for both the primary and redundant electronics at three instrument temperature plateaus: cold, nominal, and hot. This paper shows that all the JPSS-3 and -4 VIIRS RSB detectors have been well characterized, with key performance metrics comparable to the previous VIIRS instruments on-orbit. The radiometric calibration uncertainty of the RSBs is within the 2% requirement, except in the case of band M1 of JPSS-4. Comparison of the radiometric performance to sensor requirements, as well as a summary of key instrument testing and performance issues, is also presented. Full article

The Joint Polar Satellite System 3 (JPSS-3) and 4 (JPSS-4) Visible Infrared Imaging Radiometer Suite (VIIRS) are the fourth and fifth in its series of instruments designed to provide high-quality data products for environmental and climate data records. The VIIRS instrument must be calibrated and characterized prior to launch to meet the data product needs. A comprehensive test program was conducted at the Raytheon Technologies facility in 2020 (JPSS-3) and 2023 (JPSS-4) that included extensive functional and environmental testing. The thermal band radiometric pre-launch performance and stability are the focus of this article, which also compares several instrument performance metrics to the design requirements. Brief comparisons with the JPSS-1 and -2 VIIRS instrument performance will also be discussed. Full article

Sea surface temperature (SST) data from the Sea and Land Surface Temperature Radiometer (SLSTR) onboard the Sentinel-3 satellites have been used in the Met Office’s Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) since 2019 (Sentinel-3A SST data since March 2019 and Sentinel-3B data since December 2019). The impacts of using SLSTR SSTs and the SLSTR as the reference sensor for the bias correction of other satellite data have been assessed using independent Argo float data. Combining Sentinel-3A and -3B SLSTRs with two Visible Infrared Imaging Radiometer Suite (VIIRS) sensors (onboard the joint NASA/NOAA Suomi National Polar-orbiting Partnership and National Oceanic and Atmospheric Administration-20 satellites) in the reference dataset has also been investigated. The results indicate that when using the SLSTR as the only reference satellite sensor, the OSTIA system becomes warmer overall, although there are mixed impacts in different parts of the global ocean. Using both the VIIRS and the SLSTR in the reference dataset leads to moderate but more consistent improvements globally. Numerical weather prediction (NWP) results also indicate a better performance when using both the VIIRS and the SLSTR in the reference dataset compared to only using the SLSTR at night. Combining the VIIRS and the SLSTR with latitudinal weighting shows the best validation results against Argo, but further investigation is required to refine this method. Full article

The Visible Infrared Imaging Radiometer Suite (VIIRS) instrument, deployed on multiple satellites including the Suomi National Polar-orbiting Partnership (S-NPP), National Oceanic and Atmospheric Administration 20 (NOAA-20), NOAA-21, Joint Polar Satellite System (JPSS-3), and JPSS-4 spacecraft, with launches in 2011, 2017, 2022, 2032, and 2027, respectively, has polarization sensitivity that affects the at-aperture radiometric Sensor Data Record (SDR) calibration in the Visible Near InfraRed (VNIR) spectral region. These SDRs are key inputs into the VIIRS atmospheric, land, and water Environmental Data Records (EDRs) that are integral to weather and climate applications. If the polarization sensitivity of the VIIRS instrument is left uncorrected, EDR quality will degrade, causing diminished quality of weather and climate data. Pre-launch characterization of the instrument’s polarization sensitivity was performed to mitigate this on-orbit calibration effect and improve the quality of the EDRs. Specialized ground test equipment, built specifically for the VIIRS instrument, enabled high-fidelity characterization of the instrument’s polarization performance. This paper will discuss the polarization sensitivity characterization test approach, methodology, and results for the JPSS-3 and -4 builds. This includes a description of the ground test equipment, instrument requirements, and how the testing was executed and analyzed. A comparison of the polarization sensitivity results of the on-orbit S-NPP, NOAA-20, and -21 instruments with the JPSS-3 and -4 VIIRS instruments will be discussed as well. Full article

Independent radiometric data collected from multiple ground sites as part of vicarious calibration activities can be combined to harmonize the data products of Earth observation sensors with different temporal, spectral, and spatial resolutions. Recent coordinated international efforts for open fiducial reference measurements have provided the worldwide user community with new ways to explore the calibration and harmonization of data produced by the sensors. To be correct, the results from each ground system must be traceable to the same well-understood standard system, and ideally to the international system of units (SI). Additionally, the calibration test site should be homogeneous over an area larger than the spatial resolutions of each sensor, so that ground measurements are representative of the area seen by the sensors being calibrated. Here, we use a combination of independent and SI-traceable radiometric data provided from two sites of the Radiometric Calibration Network (RadCalNet) to compare the radiometric response of sensors with different spectral and spatial resolutions that operate on different orbits. These sensors are Operational Land Imagers (OLI) of the Landsat-8 and Landsat-9 missions, and Visible Infrared Imaging Radiometer Suites (VIIRS) of the Suomi-National Polar-Orbiting Operational Environmental Satellite System Preparatory Project (SNPP) and Joint Polar Satellite System-1 (JPSS-1) missions. The sensor radiometric responses are compared via temporal averaging of the ratios of top-of-atmosphere reflectance values for each sensor to those reported by RadCalNet. Our intercomparison results show that these on-orbit sensors are calibrated within their absolute radiometric uncertainties. The absolute radiometric uncertainties of single-sensor over single-site intercomparisons at 550 nm is between 5% and 6%. Having the opportunity to look at the intercomparison results of Landsat-9 OLI compared to each calibration site individually and then in combination allowed us to investigate potential systematic site-dependent biases. We did not observe significant site-dependent biases in the behavior of the four on-orbit sensors compared to the calibration sites. The absolute radiometric uncertainty of a single sensor over multiple-site intercomparisons at 550 nm is 5.4%. We further investigated site-dependent biases by looking at the double-ratio calibration coefficients of the on-orbit sensors, calculated with reference to those sites. Full article

The accurate estimation of land surface temperature (LST) is a vital parameter in various fields, such as hydrology, meteorology, and surface energy balance analysis. This study focuses on the estimation of LST using data acquired from Joint Polar-Orbiting Satellite System (JPSS) satellites, specifically JPSS-1/NOAA-20 and JPSS-2/NOAA-21. The methodology for this research centers on the utilization of the split-window algorithm, a well-established and recognized technique renowned for its proficiency in extracting accurate land surface temperature (LST) values from remotely sensed data. This algorithm leverages the differential behavior of thermal infrared (TIR) radiance measured in two adjacent spectral channels to estimate LST, effectively mitigating the influence of atmospheric distortions on the acquired measurements. To establish the accuracy of the proposed approach, the coefficients of the split-window algorithm were determined using linear regression analysis, utilizing a dataset generated via extensive radiative transfer modeling. The calculated LST values were subsequently compared with LST products provided by the National Oceanic and Atmospheric Administration (NOAA). The evaluation process encompassed the computation of root mean square error (RMSE) values, offering insights into the performance of the algorithm for both JPSS-1/NOAA-20 and JPSS-2/NOAA-21 missions. LST retrieval validation with standard atmospheric simulation indicates that the JPSS-1/NOAA-20 and The JPSS-1/NOAA-21 algorithms have demonstrated an accuracy of 1.4 K in retrieval of LST with different errors. The obtained results demonstrate the potential of the split-window algorithm to effectively estimate LST from JPSS satellite data. The RMSE values, 2.05 and 1.71 for JPSS-1/NOAA-20 and JPSS-2/NOAA-21, respectively, highlight the algorithm’s capability to provide accurate LST estimates for different mission datasets. This research contributes to enhancing our understanding of land surface temperature dynamics using remote sensing technology and showcases the valuable insights that can be gained from JPSS missions in monitoring and studying Earth’s surface processes. Full article

Land surface temperature (LST) plays a pivotal role in the dynamic exchange of energy between the Earth’s surface and the atmosphere. This research centers on the assessment of LST from satellite data acquired by the Joint Polar-orbiting Satellite System (JPSS), specifically JPSS-2/NOAA-21, employing an innovative split-window algorithm (SWA). Atmospheric water vapor content (WVC) and surface emissivity are the two main input variables in the split-window technique. Therefore, the moderate resolution transmittance code, version 4.0 (MODTRAN 4.0), was used to simulate WVC and atmospheric transmittance. The performance of the SWA was rigorously assessed against standard atmospheric conditions, revealing its capacity to achieve an LST retrieval accuracy of 1.4 Kelvin (K), even in the presence of various errors. Moreover, the LST retrieval algorithm was validated using ground truth data sets from two Australian sites, and the RMSE value was 1.71 K. The achieved results demonstrate the algorithm’s capability to provide accurate LST estimation for NOAA-21 satellite data. Full article

Radiative transfer model (RTM) simulated microwave (MW) brightness temperatures (Tbs) are commonly used to monitor and evaluate space-based MW sensor-observed antenna temperature (Ta) data. Although these simulated Tbs are paramount to this data integrity maintenance activity, their uncertainties have not been quantified. This study develops and implements a method to estimate these simulated Tb uncertainties based on a statistical comparison of two Community RTM (CRTM)-simulated operational MW sounder Tb data sets, separately generated using the European Center for Medium Range Forecasts (ECMWF) Atmospheric Model High Resolution (HRES) and Global Navigation Satellite System (GNSS) Radio Occultation (RO) sounding inputs. The study shows the smallest single-sensor CRTM-simulated Tb uncertainties, computed from differences of ECMWF HRES and GNSS RO soundings-based simulated Tbs data sets, are on the order of 10⁻⁴ relative to a 300 K Tb for the two NOAA operational MW sounder channels with low- to mid-tropospheric peak weighting function sensitivity. Meanwhile, inter-sensor-simulated Tb differences, computed from the double difference of single-sensor-simulated Tb differences, lead to CRTM-simulated Tb uncertainties on the order for 10⁻⁴ for at least nine MW sounder channels with peak sensitivity from the low-troposphere to the low-stratosphere. These findings provide the basis of future work to assess the ability to identify and quantify suspected on-orbit MW sounder calibration anomalies using RTM-driven, on-orbit MW instrument monitoring techniques. Full article

Mapping volcanic flow deposits can be achieved by considering backscattering characteristics as a metric of surface roughness. In this study, we developed an approach to extract a measure of surface roughness from dual-band airborne Synthetic Aperture Radar (ASAR) backscattering data to characterize and map various volcanic flow deposits—namely, debris avalanches, lahars, lava flows, and pyroclastic density currents. We employed ASAR and Indian Space Research Organization (ISRO) airborne SAR datasets, from a joint project (ASAR-ISRO), acquired in December 2019 at 2 m spatial resolution, to assess the role and importance of incorporating dual-band data, i.e., L-band and S-band, into surface roughness models. Additionally, we derived and analyzed surface roughness from a digital surface model (DSM) generated from unoccupied aircraft systems (UAS) acquisitions using Structure from Motion (SfM) photogrammetry techniques. These UAS-derived surface roughness outputs served as meter-scale calibration products to validate the radar roughness data over targeted areas. Herein, we applied our method to a region in the United States over the Mount St. Helens volcano in the Cascade Range of Washington state. Our results showed that dual-band systems can be utilized to characterize different types of volcanic deposits and range of terrain roughness. Importantly, we found that a combination of radar wavelengths (i.e., 9 and 24 cm), in tandem with high-spatial-resolution backscatter measurements, yields improved surface roughness maps, compared to single-band, satellite-based approaches at coarser resolution. The L-band (24 cm) can effectively differentiate small, medium, and large-scale structures, namely, blocks/boulders from fine-grained lahar deposits and hummocks from debris avalanche deposits. Additionally, variation in the roughness estimates of lahar and debris avalanche deposits can be identified and quantified individually. In contrast, the S-band (9 cm) can distinguish different soil moisture conditions across variable terrain; for example, identify wet active channels. In principle, this dual-band approach can also be employed with time series of various other SAR data of higher coherence (such as satellite SAR), using different wavelengths and polarizations, encompassing a wider range of surface roughness, and ultimately enabling additional applications at other volcanoes worldwide and even beyond volcanology. Full article

Among the monitored telemetry raw data record (RDR) parameters with the STAR Integrated/Validation System (ICVS), the Advanced Technology Microwave Sounder (ATMS) scan motor mechanism temperature is especially important because the instrument might be unavoidably damaged if the mechanism temperature exceeds 50 °C. In the current operational flight processing software, the instrument automatically enters safe mode and stops collecting scientific data whenever the mechanism temperature exceeds 40 °C. This approach inevitably leads to the instrument entering safe mode unnecessarily at a premature time, causing the loss of scientific data before the mechanism temperature reaches 50 °C. This study seeks to leverage the influence the main motor current, compensation motor current, and main motor loop integral error have on mechanism temperature to forecast the maximum mechanism temperature over the upcoming 6 min. A long short-term memory (LSTM) neural network predicts maximum mechanism temperature using ATMS RDR telemetry data as the input. The performance of the LSTM is compared with observed maximum mechanism temperatures by applying the LSTM coefficients to several cases. In all cases studied, the mean average error (MAE) of the forecast remained under 1.1 °C, and the correlation between forecasts and measurements remained above 0.96. These forecasts of maximum mechanism temperature are expected to be able to provide information on when the ATMS instrument should enter safe mode without needlessly losing valuable data for the ATMS flight operational team. Full article

The Joint Polar Satellite System (JPSS) mission has provided over ten years of high-quality data products for environment forecasting and monitoring through the current Suomi National Polar-orbiting Partnership (S-NPP) and NOAA-20 satellites. Particularly, the sensor data record (SDR) and the derived environmental data record (EDR) products from the Visible Infrared Imaging Radiometer Suite (VIIRS), the Cross-track Infrared Sounder (CrIS), the Advanced Technology Microwave Sounder (ATMS), and the Ozone Mapping and Profiler Suite (OMPS) offer an unprecedented opportunity to observe severe weather and environmental events over the Earth. This paper presents the observations about atmospheric features of the Hunga Tonga Volcanic eruption of January 2022, e.g., the gravity wave, volcanic cloud, and aerosol (sulfate) plume phenomena, by using the ATMS, CrIS, OMPS, and VIIRS SDR and EDR products. Powerful gravity waves ringing through the atmosphere after the eruption of the Hunga Tonga volcano are discovered at two CrIS upper sounding channels (670 cm⁻¹ and 2320 cm⁻¹) in the deviations of the observed brightness temperature (O) from the simulated baseline brightness temperature (B) using the Community Radiative Transfer Model (CRTM), i.e., O—B. A similar pattern is also observed in the ATMS global maps at channel 15, whose peak weighting function is around 40 km, showing the atmospheric disturbance caused by the eruption that reached 40 km above the surface. The Tonga volcanic cloud (plume) was also captured by the OMPS SO₂ EDR product. The gravity wave features were also captured in the native resolution image of the S-NPP VIIRS I-5 band nighttime observations. In addition, the VIIRS Aerosol Optical Depth (AOD) captured and tracked the volcanic aerosol (sulfate) plume successfully. These discoveries demonstrate the scientific potential of the JPSS SDR and EDR products in monitoring and tracking the eruption of the Hunga Tonga volcano and its severe environmental impacts. This paper presents the atmospheric features of the Hunga Tonga volcano eruption that is uniquely captured by all four advanced sensors onboard JPSS satellites, with different spectral coverages and spatial resolutions. Full article

The Visible Infrared Imaging Radiometer Suite (VIIRS) instruments on-board the Suomi National Polar-orbiting Partnership (S-NPP) and Joint Polar Satellite System (JPSS) spacecrafts 1 and 2 provides calibrated sensor data record (SDR) reflectance, radiance, and brightness temperatures for use in environment data record (EDR) products. The SDRs and EDRs are used in weather forecasting models, weather imagery and climate applications such as ocean color, sea surface temperature and active fires. The VIIRS has 22 bands covering a spectral range 0.4–12.4 µm with resolutions of 375 m and 750 m for imaging and moderate bands respectively on four focal planes. The bands are stratified into three different types based on the source of energy sensed by the bands. The reflective solar bands (RSBs) detect sunlight reflected from the Earth, thermal emissive bands (TEBs) sense emitted energy from the Earth and the day/night band (DNB) detects both solar and lunar reflected energy from the Earth. The SDR calibration uses a combination of pre-launch testing and the solar diffuser (SD), on-board calibrator blackbody (OBCBB) and space view (SV) on-orbit calibrator sources. The pre-launch testing transfers the National Institute of Standards and Technology (NIST) traceable calibration to the SD, for the RSB, and the OBCBB, for the TEB. Post-launch, the on-board calibrators track the changes in instrument response and adjust the SDR product as necessary to maintain the calibration. This paper will discuss the pre-launch radiometric calibration portion of the SDR calibration for the RSBs that includes the dynamic range, detector noise, calibration coefficients and radiometric uncertainties for JPSS-2 VIIRS. Full article

Snow cover has significant impacts on the global water cycle, ecosystem, and climate change. At present, satellite remote sensing is regarded as the most efficient approach to detect long-term and multiscale observations of snow cover extent. The Visible Infrared Imaging Radiometer Suite (VIIRS) sensor onboard Joint Polar Satellite System (JPSS) satellites will replace the Moderate-Resolution Imaging Spectroradiometer (MODIS) to prolong data recording in the future. Therefore, it is a fundamental task to analyze and evaluate the consistency of the snow cover products retrieved from these two sensors. In this study, we performed comparisons and a consistency evaluation between the MODIS and VIIRS snow cover products in three major snow distribution regions in China: Northeast China (NE), Northwest China (NW) and the Qinghai–Tibet Plateau (QT). The results demonstrated that (1) the normalized difference snow index (NDSI)-derived snow cover products showed suitable consistency between VIIRS and MODIS under clear sky conditions, with a mean difference value of less than 5%; (2) the VIIRS snow cover product presented much more snow and fewer clouds than that of MODIS in the snow season due to the differences in cloud-masking algorithms; (3) cloud mask strongly affects the potential of snow cover observation, and presents seasonal pattern in the test regions; and (4) VIIRS is able to distinguish clouds from snow with greater accuracy. The comparisons indicated that the greater the difference in cloud cover, the poorer the agreement in snow cover. This evaluation implies that perfecting the cloud-masking algorithm of VIIRS to update the MODIS would be the best solution to achieve better consistency for long-term and high-quality snow cover products. Full article