Search Results (83)

Operational satellite-based flood products are generated by comparing water classification maps from satellite imagery with permanent or normal water masks. This approach may misclassify some water bodies—such as irrigated paddy rice fields—as floodwaters because they are not masked as permanent or normal water sources. Due to the importance of paddy fields for food security, in this study, methodologies based on the long-time duration of water presence combined with paddy rice phenological algorithms and change detection analysis are developed to extract paddy rice fields from the operational VIIRS (Visible Infrared Imaging Radiometer Suite) flood products. This method is also compared with the regression analysis and the Mann–Kendall analysis. Evaluations are performed through confusion matrix analysis by comparing with the USDA rice data. The three paddy rice extraction algorithms show good agreement and can achieve an accuracy of 93% with an F1-score exceeding 80%. Full article

Grayscale-based Encryption-then-Compression (EtC) systems transform RGB images into the YCbCr color space, concatenate the components into a single grayscale image, and apply block permutation, block rotation/flipping, and block-wise negative–positive inversion. Because this pipeline separates color components and disrupts inter-channel statistics, existing extended jigsaw puzzle solvers (JPSs) have been regarded as ineffective, and grayscale-based EtC systems have been considered resistant to ciphertext-only visual reconstruction. In this paper, we present a practical ciphertext-only attack against grayscale-based EtC. The proposed attack introduces three key components: (i) Texture-Based Component Classification (TBCC) to distinguish luminance (Y) and chrominance (Cb/Cr) blocks and focus reconstruction on structure-rich regions; (ii) Regularized Single-Channel Edge Compatibility (R-SCEC), which applies Tikhonov regularization to a single-channel variant of the Mahalanobis Gradient Compatibility (MGC) measure to alleviate covariance rank-deficiency while maintaining robustness under inversion and geometric transforms; and (iii) Adaptive Pruning based on the TBCC-reduced search space that skips redundant boundary matching computations to further improve reconstruction efficiency. Experiments show that, in settings where existing extended JPS solvers fail, our method can still recover visually recognizable semantic content, revealing a potential vulnerability in grayscale-based EtC and calling for a re-evaluation of its security. Full article

This study provides a comprehensive, long-term evaluation of inter-sensor radiometric calibration biases for the NOAA OMPS Nadir and CrIS instruments using four complementary validation methodologies implemented within the Inter-Sensor Radiometric Bias Assessment (iSensor-RCBA) portal, a component of the STAR Integrated Calibration/Validation System. Overall, SDR data quality from the three OMPS Nadir instruments and three CrIS instruments aboard SNPP, NOAA-20, and NOAA-21 remains stable. The iSensor-RCBA portal has also proven to be a powerful diagnostic resource, enabling the detection of both new and previously unrecognized calibration issues and anomalies. Using the 32-day averaged difference method, we were the first to discover and identify the root cause of an inconsistency near 280 nm in inter-sensor radiometric biases between the SNPP and NOAA-20 OMPS NP instruments. The same method also revealed an unusual radiometric feature in NOAA-21 CrIS SDRs over the southern high latitudes during spring and summer. In addition, we derived decade-long degradation rates at 11 Metop-B GOME-2 wavelengths using an independent dataset—Simultaneous Nadir Overpass observations between SNPP OMPS and Metop-B GOME-2. Furthermore, iSensor-RCBA monitoring confirmed two geolocation anomalies in SNPP CrIS through a new approach involving SNO-based inter-sensor biases between GOES-16 ABI and SNPP CrIS. These cases demonstrate that iSensor-RCBA is not only a monitoring visualization tool but also a diagnostic tool that delivers unique, complementary insight into instrument performance, enabling early identification of radiometric and geolocation issues across JPSS and other satellite missions. Importantly, the analysis methods used in this study are broadly applicable to current and future missions, including JPSS-03, JPSS-04, and non-NOAA satellite systems. Full article

Satellite observations of carbon monoxide (CO) are essential for monitoring global air quality, pollution transport, and climate-related emissions. This study evaluates the continuity and consistency of CO measurements derived from the Atmospheric Infrared Sounder (AIRS) and the Cross-track Infrared Sounder (CrIS), both operating in the thermal infrared band near 4.6 µm. By comparing retrievals from the AIRS Science Team v7 and the CLIMCAPS (Community Long-term Infrared Microwave Combined Atmospheric Product System) algorithms across AIRS and CrIS radiances, we demonstrate that the interannual CO variability is consistent across instruments and algorithms. These findings are validated using the long-term MOPITT record. Additionally, we show that mid-tropospheric CO variabilities correspond with fire detections from MODIS and surface vapor pressure deficit (VPD) anomalies, indicating a rise in wildfire activity in the Northern Hemisphere. The results shown here provide confidence in the utility of a combined AIRS/CrIS CO record. With the scheduled continuation of CrIS observations through future JPSS platforms, the combined CO record from U.S. hyperspectral sounders in the afternoon orbit is set to continue to 2045 and beyond, providing a possible means to quantify trends and interannual variability over multiple decades. Full article

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

Monitoring grassland biomass accurately and frequently is critical for ecological management, climate change assessment, and sustainable resource use. However, the use of single-satellite data faces challenges due to trade-offs between spatial resolution and temporal frequency, especially for large areas. High-resolution imagery, such as PlanetScope, provides detailed spatial data but presents significant challenges in data management and processing over large regions. Conversely, low-resolution sensors such as JPSS-VIIRS offer daily global coverage with low memory data but lack the spatial detail required for precise biomass estimation, making it difficult to retrieve or validate model parameters due to the mismatch with small ground reference data polygons. To overcome these limitations, this study introduces a robust methodology for accurate frequent biomass estimation based on JPSS-VIIRS data through spectral harmonization, adapting a high-resolution biomass estimation model originally developed from PlanetScope imagery. The core innovation is an optimized Spectral Band Adjustment Factor (SBAF) approach tailored specifically to grassland spectral characteristics. This method significantly enhances spectral alignment, reducing red-band reflectance discrepancies from 6.2% to 4.8% in grassy areas and from 6.9% to 4.0% in bare areas. NDVI discrepancies also improved substantially. Applied across Mongolia, the harmonized VIIRS data estimated a five-year average biomass of 71.4 g/m², clearly reflecting environmental variability. Specifically, the P375 dataset showed average biomass estimates of 54.8 g/m² for desert grasslands (10.5% higher than PlanetScope), 122.6 g/m² for dry grasslands (9.6% higher), and 134 g/m² for mountain grasslands (1.9% lower). The uncertainty analysis showed strong overall agreement with PlanetScope-derived biomass, with an RMSE of 11.6 g/m², a mean percentage difference of 10.74%, and an R² of 0.92. While mountain grasslands exhibited the lowest RMSE, a relatively lower R² indicated limited variability. Higher uncertainty in desert and dry grasslands highlighted the impact of ecological heterogeneity on biomass estimation accuracy. These detailed comparisons demonstrate the effectiveness and accuracy of the proposed methodology in bridging spatial and temporal gaps, providing a valuable tool for large-scale weekly grassland biomass monitoring with applicability beyond the Mongolian context. 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

The Ozone Mapping and Profiler Suites (OMPS) Nadir Mapper (NM) is a grating spectrometer within the OMPS nadir instruments onboard the SNPP, NOAA-20, and NOAA-21 satellites. It is designed to measure Earth radiance and solar irradiance spectra in wavelengths from 300 nm to 380 nm for operational retrievals of the nadir total column ozone. This study presents calibration and validation analysis results for the NOAA-21 OMPS NM SDR data to meet the JPSS scientific requirements. The NOAA-21 OMPS SDR calibration derives updates of several previous OMPS algorithms, including the dark current correction algorithm, one-time wavelength registration from ground to on-orbit, daily intra-orbit wavelength shift correction, and stray light correction. Additionally, this study derives an empirical scale factor to remove 2.2% of systematic biases in solar flux data, which were caused by pre-launch solar calibration errors of the OMPS nadir instruments. The validation of the NOAA-21 OMPS SDR data is conducted using various methods. For example, the 32-day average method and radiative transfer model are employed to estimate inter-sensor radiometric calibration differences from either the SNPP or NOAA-20 data. The quality of the NOAA-21 OMPS NM SDR data is largely consistent with that of the SNPP and NOAA-20 OMPS data, with differences generally within ±2%. This meets the scientific requirements, except for some deviations mainly in the dichroic range between 300 nm and 303 nm. The deep convective cloud target approach is used to monitor the stability of NOAA-21 OMPS reflectance above 330 nm, showing a variation of 0.5% over the observed period. Data from the NOAA-21 VIIRS M1 band are used to estimate OMPS NM data geolocation errors, revealing that along-track errors can reach up to 3 km, while cross-track errors are generally within ±1 km. Full article

Visible Infrared Imaging Radiometer Suite (VIIRS) and Advanced Baseline Imager (GOES-R ABI) flood products have been widely used by the National Weather Service (NWS) for river flood monitoring, and by the Federal Emergency Management Agency (FEMA) for rescue and relief efforts. Some water bodies, like wetlands, are detected as water but not marked as permanent or normal water, which may result in their misclassification as floodwaters by VIIRS and GOES-R flood products. These water bodies generally do not cause significant property damage or fatalities, but they can complicate the identification of truly hazardous floods. This study utilizes the severe Louisiana flood event caused by Hurricane Ida to demonstrate how to differentiate wetlands from real-hazard flooding. Since Hurricane Ida made landfall in 2021, and there was no major flood event in 2022, VIIRS and ABI flood data from 2021 and 2022 were selected. The difference in annual total flooding days between 2021 and 2022 was calculated and combined with long-time flood frequency to distinguish non-hazard floodwaters due to wetlands identified from real-hazard floods caused by the hurricane. The results were compared with the wetlands from the change detection analysis. The confusion matrix analysis indicated an accuracy of 91.58%, precision of 89.97%, and F1-score of 76.63% for the VIIRS flood products. For the GOES-R ABI flood products, the confusion matrix analysis yielded an accuracy of 86.88%, precision of 97.49%, and F1-score of 75.21%. The accuracy and F1-score values for the GOES-R ABI flood products are slightly lower than those for the VIIRS flood products, possibly due to their lower spatial resolution, but still within a feasible range. Full article

The Visible Infrared Imaging Radiometer Suite (VIIRS) is one of the key instruments on the recently launched NOAA-21 (previously known as JPSS-2) satellite. The VIIRS, like its predecessors on the SNPP and NOAA-20 satellites, provides daily global coverage in 22 spectral bands from 412 nm to 12 μm. The geometrically and radiometrically calibrated observations are the basis for many operational applications and scientific research studies. A total of 14 of the 22 bands are reflective solar bands (RSBs), covering photon wavelengths from 412 nm to 2.25 μm. The RSBs were radiometrically calibrated prelaunch and have been regularly calibrated on orbit through the onboard solar diffuser (SD) and scheduled lunar observations. The on-orbit SD’s reflectance change is determined by the onboard solar diffuser stability monitor (SDSM). We review the calibration algorithms and present the early mission performance of the NASA N21 VIIRS RSBs. Using the calibration data collected at both the yaw maneuver and regular times, we derive the screen transmittance functions. The visible and near-infrared bands’ radiometric gains have been stable, nearly independent of time, and so were the radiometric gains of the shortwave-infrared bands after the second mid-mission outgassing. Further, we assess the Earth-view striping observed in the immediate prior collection (Collection 2.0) and apply a previously developed algorithm to mitigate the striping. The N21 VIIRS RSB detector signal-to-noise ratios are all above the design values with large margins. Finally, the uncertainties of the retrieved Earth-view top-of-the-atmosphere spectral reflectance factors at the respective typical spectral radiance levels are estimated to be less than 1.5% for all the RSBs, except band M11 whose reflectance factor uncertainty is 2.2%. Full article

The VIIRS instrument on the JPSS-2 (renamed NOAA-21 upon reaching orbit) spacecraft was launched in November 2022, making it the third sensor in the VIIRS series, following those onboard the SNPP and NOAA-20 spacecrafts, which are operating nominally. As a multi-disciplinary instrument, the VIIRS provides the worldwide user community with high-quality imagery and radiometric measurements of the land, atmosphere, cryosphere, and oceans. This study provides an early assessment of the calibration stability and radiometric consistency of the NOAA-21 VIIRS RSBs using the latest NASA SIPS C2 L1B products. Vicarious approaches are employed, relying on reflectance data obtained from the Libya-4 desert and Dome C sites, deep convective clouds, and simultaneous nadir overpasses, as well as intercomparison with the first two VIIRS instruments using MODIS as a transfer radiometer. The impact of existing band spectral differences on sensor-to-sensor comparison is corrected using scene-specific a priori hyperspectral observations from the SCIAMACHY sensor onboard the ENVISAT platform. The results indicate that the overall radiometric performance of the newly launched NOAA-21 VIIRS is quantitatively comparable to the NOAA-20 for the VIS and NIR bands. For some SWIR bands, the measured reflectances are lower by more than 2%. An upward adjustment of 6.1% in the gain of band M11 (2.25 µm), based on lunar intercomparison results, generates more consistent results with the NOAA-20 VIIRS. 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

Satellite-based hyper-spectral infrared (IR) sensors such as the Atmospheric Infrared Sounder (AIRS), the Cross-track Infrared Sounder (CrIS), and the Infrared Atmospheric Sounding Interferometer (IASI) cover many methane (CH₄) spectral features, including the ν1 vibrational band near 1300 cm⁻¹ (7.7 μm); therefore, they can be used to monitor CH₄ concentrations in the atmosphere. However, retrieving CH₄ remains a challenge due to the limited spectral information provided by IR sounder measurements. The information required to resolve the weak absorption lines of CH₄ is often obscured by interferences from signals originating from other trace gases, clouds, and surface emissions within the overlapping spectral region. Consequently, currently available CH₄ data product derived from IR sounder measurements still have large errors and uncertainties that limit their application scope for high-accuracy climate and environment monitoring applications. In this paper, we describe the retrieval of atmospheric CH₄ profiles using a novel spectral fingerprinting methodology and our evaluation of performance using measurements from the CrIS sensor aboard the Suomi National Polar-orbiting Partnership (SNPP) satellite. The spectral fingerprinting methodology uses optimized CrIS radiances to enhance the CH₄ signal and a machine learning classifier to constrain the physical inversion scheme. We validated our results using the atmospheric composition reanalysis results and data from airborne in situ measurements. An inter-comparison study revealed that the spectral fingerprinting results can capture the vertical variation characteristics of CH₄ profiles that operational sounder products may not provide. The latitudinal variations in CH₄ concentration in these results appear more realistic than those shown in existing sounder products. The methodology presented herein could enhance the utilization of satellite data to comprehend methane’s role as a greenhouse gas and facilitate the tracking of methane sources and sinks with increased reliability. Full article