Suomi National Polar-orbiting Partnership Research Articles

An advanced spatial coregistration of cloud properties for the atmospheric Sentinel missions: application to TROPOMI

Abstract. The retrieval of cloud parameters from the atmospheric Sentinel missions requires Earth reflectance measurements from a set of spectral bands. The ground pixels of the involved spectral bands should be fully aligned, but when they are not, special treatment is required within the operational algorithms. This so-called inter-band spatial misregistration of passive spectrometers is present when the Earth reflectance measurements in different spectral bands are captured by different spectrometers. The cloud retrieval algorithm requires reflectance measurements in the UV(ultraviolet)–VIS (visible) band, where the first cloud parameter (i.e., radiometric cloud fraction) is retrieved from OCRA (Optical Cloud Recognition Algorithm). In addition, Earth reflectances in the NIR (near-infrared) band are needed for the retrieval of two additional cloud parameters (i.e., cloud height and cloud albedo or cloud-top height and optical thickness) from the ROCINN (Retrieval of Cloud Information using Neural Networks) algorithm. In the former TROPOMI (TROPOspheric Monitoring Instrument)/S5P (Sentinel-5 Precursor) retrieval, a coregistration scheme of the derived cloud parameters from the source band to the target band based on pre-calculated mapping weights from UV–VIS to NIR and vice versa is applied. In this paper we present a new scheme for the coregistration of the TROPOMI cloud parameters using collocated VIIRS (Visible Infrared Imaging Radiometer Suite)/SNPP (Suomi National Polar-orbiting Partnership) information. The new coregistration scheme based on the VIIRS data improves the TROPOMI cloud product quality and allows the addition of cloud information for the first (westernmost) TROPOMI UVIS ground pixel. In practice, the latter means that a significant number of valid data points are included in the TROPOMI cloud, total ozone, SO2 and HCHO product since 26 November 2023 (orbit 31705), when the new coregistration scheme became operational. From a comparison analysis between the two techniques, we found that the largest differences mainly appear for inhomogeneous scenes. From a validation exercise of TROPOMI against VIIRS in the across-track direction, we found that the old coregistration scheme tends to smooth out cloud structures along the scan line, whereas such structures can be maintained with the new scheme. The need to implement a similar inter-band spatial coregistration scheme is foreseen for the Sentinel-4/MTG-S (Meteosat Third Generation – Sounder) and Sentinel-5/MetOp-SG (Meteorological Operational Satellite – Second Generation) missions. In the case of the Sentinel-4 instrument, the external cloud information will originate from collocated data captured by the FCI (Flexible Combined Imager) on board the MTG-I (Meteosat Third Generation – Imager) satellite.

Estimation of nighttime PM2.5 concentrations over Seoul using Suomi NPP/VIIRS Day/Night Band

With rapid economic development and urban growth, Seoul experiences severe air pollution due to fine particulate matter with an aerodynamic diameter of ≤2.5 μm (PM2.5), which is detrimental to human health. Although recent studies have extensively focused on estimating daytime PM2.5 concentrations using various types of satellite data, there remains a significant lack of research on nighttime PM2.5 estimations. This study estimated nighttime PM2.5 in Seoul from December 2018 to November 2019 using multiple linear regression (MLR) and random forest (RF) models. These models, which incorporated data on radiance, moon illumination fraction, and terrain height from the Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) on board the Suomi National Polar-orbiting Partnership satellite covering all moon phases, also utilized meteorological data from the ERA5 reanalysis by the European Centre for Medium-Range Weather Forecasts (ECMWF). To address multicollinearity, seasonal models were developed using forward stepwise regression and variance inflation factor analysis. DNB radiance analysis indicates that the high intensity of artificial light sources in Seoul significantly reduces the impact of moonlight, leading to notable changes in the DNB radiation associated with PM2.5 concentrations. Consequently, this study estimated nighttime PM2.5 over Seoul across all moon phases. These estimates were then validated through 10-fold cross-validation. The RF model exhibited superior accuracy, with a coefficient of determination (R2) of 0.65–0.90, compared to MLR, with R2 of 0.15–0.50, reflecting seasonal fluctuations in the model performance. The developed models can be applied to estimate reliable nighttime PM2.5 concentrations in megacities with strong artificial light sources, utilizing a comprehensive dataset from satellite observations for all moon phases. Additionally, our findings can serve as scientific data for establishing environmental policies by providing valuable insights into understanding air pollution primarily caused by PM2.5.

Version 1 NOAA-20/OMPS Nadir Mapper total column SO2 product: continuation of NASA long-term global data record

Abstract. For nearly 2 decades, the Ozone Monitoring Instrument (OMI) aboard the NASA Aura spacecraft (launched in 2004) and the Ozone Mapping and Profiler Suite (OMPS) aboard the NASA/NOAA Suomi National Polar-orbiting Partnership (SNPP) satellite (launched in 2011) have been providing global monitoring of SO2 column densities from both anthropogenic and volcanic activities. Here, we describe the version-1 NOAA-20 (N20)/OMPS SO2 product (https://doi.org/10.5067/OMPS/OMPS_N20_NMSO2_PCA_L2_Step1.1, Li et al., 2023), aimed at extending the long-term climate data record. To achieve this goal, we apply a principal component analysis (PCA) retrieval technique, also used for the OMI and SNPP/OMPS SO2 products, to N20/OMPS. For volcanic SO2 retrievals, the algorithm is identical between N20 and SNPP/OMPS and produces consistent retrievals for eruptions such as Kilauea in 2018 and Raikoke in 2019. For anthropogenic SO2 retrievals, the algorithm has been customized for N20/OMPS, considering its greater spatial resolution and reduced signal-to-noise ratio as compared with SNPP/OMPS. Over background areas, N20/OMPS SO2 slant column densities (SCDs) show relatively small biases, comparable retrieval noise with SNPP/OMPS (after aggregation to the same spatial resolution), and remarkable stability with essentially no drift during 2018–2023. Over major anthropogenic source areas, the two OMPS retrievals are generally well-correlated, but N20/OMPS SO2 is biased low, especially for India and the Middle East, where the differences reach ∼ 20 % on average. The reasons for these differences are not fully understood but are partly due to algorithmic differences. Better agreement (typical differences of ∼ 10 %–15 %) is found over degassing volcanoes. SO2 emissions (https://doi.org/10.5067/MEASURES/SO2/DATA406, Fioletov et al., 2022) from large point sources, inferred from N20/OMPS retrievals, agree well with those based on OMI, SNPP/OMPS, and the TROPOspheric Monitoring Instrument (TROPOMI), with correlation coefficients >0.98 and overall differences <10 %. The ratios between the estimated emissions and their uncertainties offer insights into the ability of different satellite instruments to detect and quantify SO2 sources. While TROPOMI has the highest ratios of all four sensors, the ratios from N20/OMPS are slightly greater than OMI and substantially greater than SNPP/OMPS. Overall, our results suggest that the version-1 N20/OMPS SO2 product will successfully continue the long-term OMI and SNPP/OMPS SO2 data records. Efforts currently underway will further enhance the consistency of retrievals between different instruments, facilitating the development of multi-decade, coherent global SO2 datasets across multiple satellites.

Decade‐Long Ozone Profile Record From Suomi NPP OMPS Limb Profiler: Assessment of Version 2.6 Data

AbstractWe evaluate a decadal ozone profile record derived from the Suomi National Polar‐orbiting Partnership (SNPP) Ozone Mapping and Profiler Suite (OMPS) Limb Profiler (LP) satellite instrument. In 2023, the OMPS LP data were re‐processed with the new version 2.6 retrieval algorithm that combines measurements from the ultraviolet (UV) and visible (VIS) parts of the spectra. It employs the second order Tikhonov regularization to retrieve a single vertical ozone profile between 12.5 km (or cloud tops) and 57.5 km with a vertical resolution of about 1.9–2.5 km between 20 and 55 km. The algorithm uses radiances measured at six UV ozone‐sensitive wavelengths (295, 302, 306, 312, 317, and 322 nm) paired with 353 nm, and one VIS wavelength at 606 nm combined with 510 and 675 nm to form a triplet. Each wavelength pair or triplet is used over a limited range of tangent altitudes where the sensitivity to ozone change is strongest. A new aerosol correction scheme was implemented based on a gamma‐function particle size distribution. In addition, numerous calibration changes that affected ozone retrievals were applied to measured LP radiances, including updates in altitude registration, radiometric calibration, stray light, and spectral registration. The key version 2.6 enhancement is the improved stability of the LP ozone record confirmed by the reduction in relative drifts between LP ozone and correlative measurements, linked previously to a drift in the version 2.5 LP altitude registration.

Development of an Algorithm for Assessing the Scope of Large Forest Fire Using VIIRS-Based Data and Machine Learning

Forest fires pose a multifaceted threat, encompassing human lives and property loss, forest resource destruction, and toxic gas release. This crucial disaster’s global occurrence and impact have risen in recent years, primarily driven by climate change. Hence, the scope and frequency of forest fires must be collected to establish disaster prevention policies and conduct relevant research projects. However, some countries do not share details, including the location of forest fires, which can make research problematic when it is necessary to know the exact location or shape of a forest fire. This non-disclosure warrants remote surveys of forest fire sites using satellites, which sidestep national information disclosure policies. Meanwhile, original data from satellites have a great advantage in terms of data acquisition in that they are independent of national information disclosure policies, making them the most effective method that can be used for environmental monitoring and disaster monitoring. The Visible Infrared Imaging Radiometer Suite (VIIRS) aboard the Suomi National Polar-Orbiting Partnership (NPP) satellite has worldwide coverage at a daily temporal resolution and spatial resolution of 375 m. It is widely used for detecting hotspots worldwide, enabling the recognition of forest fires and affected areas. However, information collection on affected regions and durations based on raw data necessitates identifying and filtering hotspots caused by industrial activities. Therefore, this study used VIIRS hotspot data collected over long periods and the Spatio-Temporal Density-Based Spatial Clustering of Applications with Noise (ST-DBSCAN) algorithm to develop ST-MASK, which masks said hotspots. By targeting the concentrated and fixed nature of these hotspots, ST-MASK is developed and used to distinguish forest fires from other hotspots, even in mountainous areas, and through an outlier detection algorithm, it generates identified forest fire areas, which will ultimately allow for the creation of a global forest fire watch system.

Characterizing fire and fire atmospheric states from space using collocated hyperspectral infrared sounding and narrow-band imagery

Under the influence of global climate change, wildfires are becoming increasingly significant within ecosystems. Satellite technology offers a critical perspective for studying fire. The synergy of high-spatial resolution narrow-band imagers and hyperspectral infrared (IR) sounders allow for comprehensive observation and long-term global monitoring of fire characteristics and associated atmospheric changes on the pixel scale. This study demonstrates methods to utilize the pixel-scale collocated fire observations from the Visible Infrared Imaging Radiometer Suite (VIIRS) and the IR radiance spectra from the Cross Track Infrared Sounder (CrIS), onboard NASA's Suomi National Polar-orbiting Partnership (SNPP) satellite, to analyze fire and the atmospheric conditions before, during, and after fires. Two months of satellite observations over the Southwest United States and the Amazonia regions when large fires occurred in the regions of interest (October 2017 and August 2020) are used. Our findings reveal that CrIS has high sensitivity to fire that affects as little as 1% of its field of view (FOV) with FOV total fire radiative power (FRP) larger than 200 MW (MW) at night and 1000 MW during the day. By employing spectral principal component analysis (PCA), the CrIS spectral signature to atmospheric temperature, humidity, and trace gases corresponding to fire characteristics are quantified. This approach highlights that collocated imager and IR sounder data, when paired with PCA, provide a powerful method to effectively identify and monitor wildfires. This technique also allows for the observation of subsequent atmospheric alterations while managing data volume efficiently, ensuring that crucial spectral information is preserved. This methodology advances our ability to understand and respond to the multifaceted impacts of wildfires on both local and global scales, reinforcing the value of integrated satellite observations in environmental science.

Turning Night into Day: The Creation and Validation of Synthetic Nighttime Visible Imagery Using the Visible Infrared Imaging Radiometer Suite (VIIRS) Day–Night Band (DNB) and Machine Learning

Abstract Meteorological satellite imagery is a critical asset for observing and forecasting weather phenomena. The Joint Polar Satellite System (JPSS) Visible Infrared Imaging Radiometer Suite (VIIRS) Day–Night Band (DNB) sensor collects measurements from moonlight, airglow, and artificial lights. DNB radiances are then manipulated and scaled with a focus on digital display. DNB imagery performance is tied to the lunar cycle, with the best performance during the full moon and the worst with the new moon. We propose using feed-forward neural network models to transform brightness temperatures and wavelength differences in the infrared spectrum to a pseudo-lunar reflectance value based on lunar reflectance values derived from observed DNB radiances. JPSS NOAA-20 and Suomi National Polar-Orbiting Partnership (SNPP) satellite data over the North Pacific Ocean at night for full moon periods from December 2018 to November 2020 were used to design the models. The pseudo-lunar reflectance values are quantitatively compared to DNB lunar reflectance, providing the first-ever lunar reflectance baseline metrics. The resulting imagery product, Machine Learning Nighttime Visible Imagery (ML-NVI), is qualitatively compared to DNB lunar reflectance and infrared imagery across the lunar cycle. The imagery goal is not only to improve upon the consistent performance of DNB imagery products across the lunar cycle, but ultimately to lay the foundation for transitioning the algorithm to geostationary sensors, making global continuous nighttime imagery possible. ML-NVI demonstrates its ability to provide DNB-derived imagery with consistent contrast and representation of clouds across the full lunar cycle for nighttime cloud detection. Significance Statement This study explores the creation and evaluation of a feed-forward neural network to generate synthetic lunar reflectance values and imagery from VIIRS infrared channels. The model creates lunar reflectance values typical of full moon scenes, enabling quantifiable comparisons for nighttime imagery evaluations. Additionally, it creates imagery that highlights low clouds better than its infrared counterparts. Results indicate the ability to create visually consistent nighttime visible imagery across the full lunar cycle for the improved nighttime detection of low clouds. Wavelengths chosen are available on both polar and geostationary satellite sensors to support the utilization of the algorithm on multiple sensor platforms for improved temporal resolution and greater simultaneous geographic coverage over polar orbiters alone.

Pre-Launch Polarization Assessment of JPSS-3 and -4 VIIRS VNIR Bands and Comparison with Previous Builds

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.

Global retrieval of stratospheric and tropospheric BrO columns from the Ozone Mapping and Profiler Suite Nadir Mapper (OMPS-NM) on board the Suomi-NPP satellite

Abstract. Quantifying the global bromine monoxide (BrO) budget is essential to understand ozone chemistry better. In particular, the tropospheric BrO budget has not been well characterized. Here, we retrieve nearly a decade (February 2012–July 2021) of stratospheric and tropospheric BrO vertical columns from the Ozone Mapping and Profiling Suite Nadir Mapper (OMPS-NM) on board the Suomi National Polar-orbiting Partnership (Suomi-NPP) satellite. In quantifying tropospheric BrO enhancements from total slant columns, the key aspects involve segregating them from stratospheric enhancements and applying appropriate air mass factors. To address this concern and improve upon the existing methods, our study proposes an approach that applies distinct BrO vertical profiles based on the presence or absence of tropospheric BrO enhancement at each pixel, identifying it dynamically using a satellite-derived stratospheric-ozone–BrO relationship. We demonstrate good agreement for both stratosphere (r = 0.81–0.83) and troposphere (r = 0.50–0.70) by comparing monthly mean BrO vertical columns from OMPS-NM with ground-based observations from three stations (Lauder, Utqiaġvik, and Harestua). Although algorithm performance is primarily assessed at high latitudes, the OMPS-NM BrO retrievals successfully capture tropospheric enhancements not only in polar regions but also in extrapolar areas, such as the Rann of Kutch and the Great Salt Lake. We also estimate random uncertainties in the retrievals pixel by pixel, which can assist in quantitative applications of the OMPS-NM BrO dataset. Our BrO retrieval algorithm is designed for cross-sensor applications and can be adapted to other space-borne ultraviolet spectrometers, contributing to the creation of continuous long-term satellite BrO observation records.

Propagation of NO2 originated in intense fires in the Paraná River Delta analyzed from satellite observations

Wildfires in the Paraná River Delta started to be a common situation in the year 2008 and during the first years of the 2020́s decade: 2020, 2021, 2022. These fires affected natural areas, primarily the islands within the delta, situated in front of Rosario city (32.95 °S, 60.67 °W, 25 meters above sea level (m.a.s.l.)), Argentina. They mostly occur during the period between June and September each year and are more intense when a strong La Niña event is present. The aim of this study is to examine the association between the propagation of high intensity nitrogen dioxide (NO2) plumes and the significant fire events that occurred in the Paraná River Delta from 6 to 17 September 2022. Satellite data of NO2 obtained from the TROPOspheric Monitoring Instrument (TROPOMI) on board the European Space Agency (ESA) Sentinel-5 Precursor satellite, air mass forward trajectories performed with The Hybrid Single-Particle Lagrangian Integrated Trajectory model (HYSPLIT), Fire Radiative Power (FRP) data provided by Visible Infrared Imaging Radiometer Suite (VIIRS) on board The National Oceanic and Atmospheric Administration - 20 (NOAA-20) and Suomi National Polar-orbiting Partnership (S-NPP) satellites, Aerosol Optical Depth (AOD) from VIIRS S-NPP and Smoke Injection Height from Multi-Angle Implementation of Atmospheric Correction (MAIAC) Thermal Technique were employed. The trajectories followed by the air masses coming from the major fire sources locations nearby Rosario city and its surrounding area, were analyzed at altitudes of 20, 500, 1000, 1500 and 2000 m above ground level (AGL) in order to evaluate the impact of the NO2 clouds present in the atmosphere and carried by the winds, at different sites of Rosario and its adjacent region. The NO2 plumes has the potential to pose a significant health risk for this region, inhabited by an approximate population of one and a half million individuals. This risk is contingent upon the altitude where the highest levels of NO2 in the tropospheric column are found. Tropospheric vertical column densities (VCDs) of NO2 in the range of 4.68×1014 to 1.66×1016 molecules/cm2 were determined. The results indicate a robust correlation between elevated tropospheric NO2 VCD values and intense fires, as characterized by their FRP. Additionally, high tropospheric NO2 VCDs were observed to impact cities and towns located in close proximity to the fires. In particular, the unusually high tropospheric NO2 VCDs determined for the towns of Fighiera and Pueblo Esther, situated along the coast and near the Paraná River Delta, are noteworthy. This is uncommon considering they are small towns in rural areas that typically have good air quality. The results regarding FRP, provided by VIIRS on board NOAA-20 and S-NPP satellites, indicate that the fire characterization of hotspots during the considered period had a mean value in the range of 20.17–21.47 MW. On specific days (12, 13 and 16 September 2022), the cumulative (total) daily values were very high, ranging from 2000 to 10000 MW. Furthermore, on 12 and 13 September 2022, the smoke aerosols did not reach significant altitudes in the atmosphere, remaining within the range of 0 to 1500 m AGL. During the fire period, AOD values close to 1 were observed within the fire plume. Notably, Puerto San Martín (0.27), San Lorenzo (0.22), and Ibarlucea (0.20) recorded higher average AOD values compared to other sites. The remaining sites generally exhibited lower average AOD values, with a slight distinction between rural locations (Pueblo Esther: 0.14 and Fighiera: 0.14) and the city center of Rosario (0.09).

Evaluation of VIIRS Thermal Emissive Bands Long-Term Calibration Stability and Inter-Sensor Consistency Using Radiative Transfer Modeling

This study investigates the long-term stability of the Suomi National Polar-orbiting Partnership (S-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) moderate-resolution Thermal Emissive Bands (M TEBs; M12–M16) covering a period from February 2012 to August 2020. It also assesses inter-sensor consistency of the VIIRS M TEBs among three satellites (S-NPP, NOAA-20, and NOAA-21) over eight months spanning from 18 March to 30 November 2023. The field of interest is limited to the ocean surface between 60°S and 60°N, specifically under clear-sky conditions. Taking radiative transfer modeling (RTM) as the transfer reference, we employed the Community Radiative Transfer Model (CRTM) to simulate VIIRS TEB brightness temperature (BTs), incorporating European Centre for Medium-range Weather Forecasts (ECMWF) reanalysis data as inputs. Our results reveal two key findings. Firstly, the reprocessed S-NPP VIIRS TEBs exhibit a robust long-term stability, as demonstrated through analyses of the observation minus background BT differences (O-B ∆BTs) between VIIRS measurements (O) and CRTM simulations (B). The drifts of the O-B BT differences are consistently less than 0.102 K/Decade across all S-NPP VIIRS M TEB bands. Notably, observations from VIIRS M14 and M16 stand out with drifts well within 0.04 K/Decade, reinforcing their exceptional reliability for climate change studies. Secondly, excellent inter-sensor consistency among these three VIIRS instruments is confirmed through the double-difference analysis method (O-O). This method relies on the O-B BT differences obtained from daily VIIRS operational data. The mean inter-VIIRS O-O BT differences remain within 0.08 K for all M TEBs, except for M13. Even in the case of M13, the O-O BT differences between NOAA-21 and NOAA-20/S-NPP have values of 0.312 K and 0.234 K, respectively, which are comparable to the 0.2 K difference observed in overlapping TEBs between VIIRS and MODIS. These disparities are primarily attributed to the significant differences in the Spectral Response Function (SRF) of NOAA-21 compared to NOAA-20 and S-NPP. It is also found that the remnant scene temperature dependence of NOAA-21 versus NOAA-20/S-NPP M13 O-O BT difference after accounting for SRF difference is ~0.0033 K/K, an order of magnitude smaller than the corresponding rates in the direct BT comparisons between NOAA-21 and NOAA-20/S-NPP. Our study confirms the versatility and effectiveness of the RTM-based TEB quality evaluation method in assessing long-term sensor stability and inter-sensor consistency. The double-difference approach effectively mitigates uncertainties and biases inherent to CRTM simulations, establishing a robust mechanism for assessing inter-sensor consistency. Moreover, for M12 operating as a shortwave infrared channel, it is found that the daytime O-B BT differences of S-NPP M12 exhibit greater seasonal variability compared to the nighttime data, which can be attributed to the idea that M12 radiance is affected by the reflected solar radiation during the daytime. Furthermore, in this study, we’ve also characterized the spatial distributions of inter-VIIRS BT differences, identifying variations among VIIRS M TEBs, as well as spatial discrepancies between the daytime and nighttime data.

VIIRS Version 2 Deep Blue Aerosol Products

AbstractNASA's Deep Blue aerosol project has developed global aerosol data records using consistent retrieval algorithms applied to various satellite sensors. The primary components of these data records are derived from the series of Visible Infrared Imaging Radiometer Suite (VIIRS) aboard the Suomi National Polar‐orbiting Partnership (SNPP) and the National Oceanic and Atmospheric Administration or NOAA‐20+ satellites as well as the Moderate Resolution Imaging Spectroradiometer (MODIS), among others. These instruments provide over 23 years of measurements with similar radiometric characteristics for aerosol retrievals. The algorithms used for the initial Version 1 SNPP VIIRS data set were based on the MODIS Collection 6.1 Deep Blue algorithm over land and Satellite Ocean Aerosol Retrieval (SOAR) algorithm over water. For VIIRS Version 2 data reprocessing, major updates have been made to the algorithm suite, including better accounting for effects of surface pressure, improved determination of surface reflectance, and the inclusion of fine‐mode aerosol optical models to better represent anthropogenic aerosols over land. Cross‐calibration gain factors are derived for the NOAA‐20 VIIRS measurements to be consistent with the SNPP VIIRS, which allows the use of a unified algorithm package for both instruments. Comparisons against AERONET observations indicate that the Version 2 AOD data from SNPP VIIRS are significantly better than the Version 1 counterpart over land and slightly degraded over water in exchange for better spatial coverage. The AOD data from SNPP and NOAA‐20 VIIRS are comparable, indicating that cross‐calibration enables the creation of consistent aerosol data records using the series of VIIRS sensors.

Investigating the performance of SDGSAT-1/GIU and NPP/VIIRS nighttime light data in representing nighttime vitality and its relationship with the built environment: A comparative study in Shanghai, China

In recent years, a substantial body of research has demonstrated the close relationship between the built environment and urban nighttime vitality. While scholars have established slightly varied evaluation systems for the built environment, most have utilized the open-source nighttime light (NTL) data (2012 ∼ ) from the Visible Infrared Imaging Radiometer Suite (VIIRS) sensor on the Suomi National Polar-orbiting Partnership (NPP) Satellite to characterize nighttime vitality. However, with the release of the high spatial resolution NTL data from Glimmer Imager for Urbanization (GIU) sensor on the Sustainable Development Goals Satellite 1 (SDGSAT-1) by the Chinese Academy of Sciences (CAS), more options are now available for indicating nighttime vitality. Yet, no studies have so far measured the representational capability of this new NTL data. In this study, we selected Shanghai, one of the most urbanized cities in Asia, as our research area. We utilized both GIU NTL data and VIIRS NTL data to represent nighttime vitality and collected multidimensional geospatial big data as indicators of the built environment. Various regression models were employed to explore the differences in explanatory power when using different NTL data. To obtain more reliable conclusions, we have also divided the study area according to urban functional zone categories and conducted experiments on both the overall scale (entire study area) and local scale (different urban functional zones). The results showed that regression models using GIU NTL data had higher R2 values both at overall scale(0.529 > 0.268) and local scale (0.704 > 0.641, 0.701 > 0.408, 0.537 > 0.512, 0.674 > 0.589), indicating stronger explanatory power of GIU NTL data. Furthermore, we found that the use of GIU NTL data revealed an amplification effect, where the impact of certain representative built environment factors (such as road density, building height, and vegetation coverage) on nighttime vitality was magnified, demonstrating more pronounced spatial heterogeneity. Our study investigated the performance of the new open-source SDGSAT1/GIU NTL data in representing nighttime vitality and shows its potential application in research on the relationship between the built environment and nighttime vitality. Additionally, our study also reveals the differences in the impact of the built environment on nighttime vitality across different urban functional zones. All these results will provide more valuable references for city managers aspiring to enhance the nighttime vitality and help them to make more rational planning plans.

Revisiting the relationships between energy consumption, economic development and urban size: A global perspective using remote sensing data

Existing methods of measuring energy consumption require complex statistics and computing. A real-time and globally applicable approach for comparing energy consumption across different cities is still lacking. Additionally, the nonlinear relationships and varying thresholds of energy consumption in relation to economic activities and urbanization remain unconfirmed. This study aims to fill these gaps by utilizing Suomi National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) nighttime light data in 2015 and a top-down approach based on a multiple regression model to examine energy consumption in global cities employing a redefined urban boundary. It also explores the accurate relationship between energy consumption, population density (as a proxy of urbanization), and per capita gross domestic product (GDP) across different regions and urban sizes using generalized additive models and regression models. High-resolution gridded population and GDP datasets covering the entire planet are utilized for this purpose. The study also estimates the development potentiality. The study yields followings outcomes: Firstly, the top 30 cities with the highest per capita energy consumption account for over 0.66% of the total per capita energy consumption of all cities. Secondly, in East Asia (EA) and Southeast Asia (SEA), the per capita energy consumption decreases when per capita GDP reaches $40,000 and $75,000, respectively, while it remains stable in cities located in Western Europe (WE) and North America (NA) as per capita GDP increases. Thirdly, the per capita energy consumption declines with increasing urban population density until reaching 10,000 person/km2, 22,000 person/km2, and 4000 person/km2 in EA, SEA, and NA, respectively. Fourthly, in Central Asia (CA), megacities can save over 100 Mbtu/population when per capita GDP increases by $1000 compared to big cities. This pioneering study provides a comparable investigation of energy consumption at the global city level, exploring its relationship with urbanization and economy by employing a unified calculation standard. It will facilitate long-term energy-saving policies and urban planning strategies.

Light-Pollution-Monitoring Method for Selected Environmental and Social Elements

Light pollution significantly interferes with animal and human life and should, therefore, be included in the factors that threaten ecosystems. The main aim of this research is to develop a methodology for monitoring environmental and social elements subjected to light pollution in anthropogenic areas. This research is based on yearly and monthly photographs acquired from the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-Orbiting Partnership (Suomi NPP) satellite; land cover data from the CORINE Land Cover (CLC) program; and environmental data from the European Environment Agency (EEA) and the World Database on Protected Areas (WDPA). The processing of input data for further analyses, the testing of the methodology and the interpretation of the final results were performed in GIS-type software (ArcGIS Pro). Light pollution in the investigated area was analyzed with the use of maps generated for the years 2014 and 2019. The environmental and social elements were spatially identified in five light pollution classes. The research results demonstrate that the proposed methodology allows for the identification of environmental and social elements that emit light, as well as those that are subjected to light pollution. The methodology used in this work allows us to observe changes resulting from light pollution (decreasing or increasing the intensity). Owing to the use of publicly available data, the methodology can be applied to light pollution monitoring as part of spatial planning in anthropogenic areas. The proposed methodology makes it possible to cover the area exposed to light pollution and to observe (almost online) the environmental and social changes resulting from reductions in light emitted by anthropogenic areas.

Assessment and characteristics of S-NPP VIIRS Deep Blue and Dark Target aerosol properties under clean, polluted and fire scenarios over the Amazon

The present study carries out the systematic performance evaluation of aerosol optical depth (AOD) products retrieved using Visible Infrared Imaging Radiometer Suite (VIIRS) Deep Blue (DB) and Dark Target (DT) onboard Suomi National Polar-orbiting Partnership (S-NPP) satellite over the Amazon Basin. Characteristics and uncertainty were evaluated under distinct air pollution scenarios such as a clean background in the wet season, polluted conditions in the dry season with biomass burning emissions and peak burning season with higher fire activity. VIIRS retrievals were also analyzed under aerosol loading, particle size and surface vegetation coverage against the Aerosol Robotic Network (AERONET) measurements at 9 sites in 2012–2022. VIIRS DB showed good accuracy, with 78% of AOD matchups falling within the expected error, 84% in the wet season, and 71% in the dry and burning seasons. In contrast, VIIRS DT indicated poor accuracy (64%) and trends overestimate the AOD in all air pollution scenarios. Both algorithms were sensitive to AERONET sites with high elevation and dark vegetated coverage characteristics. VIIRS DB and DT systematically overestimated AERONET AOD as increased aerosol loading. DB trends underestimate aerosol under background conditions and overestimate with coarse and fine particle predominance. Additionally, both algorithms indicated poor accuracy under forest type with a large positive bias. VIIRS DB demonstrated the highest accuracy in the presence of aerosol loading in sites characterized by mixed land cover type. This was observed in both coarse and fine mode scenarios for grassland. VIIRS DT demonstrated satisfactory accuracy under background conditions and dominance of coarse particles within grassland land cover type. For mixed land cover, satisfactory accuracy was found under intermediate aerosol loading conditions. DB algorithm showed greater uncertainty associated with the coarse particle aerosol for the full period and all polluted scenarios. Overall, VIIRS DT accuracy was more sensitive to varying air pollution scenarios.

Spectral Fingerprinting of Methane from Hyper-Spectral Sounder Measurements Using Machine Learning and Radiative Kernel-Based Inversion

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.

The Assessment of Industrial Agglomeration in China Based on NPP-VIIRS Nighttime Light Imagery and POI Data

Industrial agglomeration, as a typical aspect of industrial structures, significantly influences policy development, economic growth, and regional employment. Due to the collection limitations of gross domestic product (GDP) data, the traditional assessment of industrial agglomeration usually focused on a specific field or region. To better measure industrial agglomeration, we need a new proxy to estimate GDP data for different industries. Currently, nighttime light (NTL) remote sensing data are widely used to estimate GDP at diverse scales. However, since the light intensity from each industry is mixed, NTL data are being adopted less to estimate different industries’ GDP. To address this, we selected an optimized model from the Gaussian process regression model and random forest model to combine Suomi National Polar-Orbiting Partnership—Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) NTL data and points-of-interest (POI) data, and successfully estimated the GDP of eight major industries in China for 2018 with an accuracy (R2) higher than 0.80. By employing the location quotient to measure industrial agglomeration, we found that a dominated industry had an obvious spatial heterogeneity. The central and eastern regions showed a developmental focus on industry and retail as local strengths. Conversely, many western cities emphasized construction and transportation. First-tier cities prioritized high-value industries like finance and estate, while cities rich in tourism resources aimed to enhance their lodging and catering industries. Generally, our proposed method can effectively measure the detailed industry agglomeration and can enhance future urban economic planning.

JPSS-3 / 4 VIIRS response versus scan angle characterization and performance

Scientific studies of the Earth’s climate increasingly rely on high-quality satellite observations. The Visible Infrared Imaging Radiometer Suite (VIIRS) is a key sensor onboard a series of satellites [Suomi National Polar-orbiting Partnership (SNPP) and Joint Polar-orbiting Satellite System 1–4 (JPSS-1–JPSS-4)] that generate scientific data from land, ocean, and atmosphere used in these climate models. Providing quality scientific data from space-borne sensors requires the instruments to be well-calibrated. While much of the calibration can be maintained on-orbit, some aspects of the calibration can best be measured prior to launch. One VIIRS parameter that needs to be measured pre-launch is the response versus scan angle (RVS). The RVS measures the relative change in the reflectance of the scanning optics as a function of the angle of incidence. With the RVS, the gain calibration measured on-orbit can be transferred to any scan angle. The JPSS-3 and JPSS-4 instruments have undergone ground testing including the RVS measurements, which is the subject of this work. Results indicate that the measurements are comparable to previous VIIRS builds and are expected to contribute to the generation of high-quality science data once JPSS-3 and JPSS-4 are on-orbit.

The Hunga Tonga‐Hunga Ha'apai Volcanic Eruption as Seen in Satellite Microwave Observations and MiRS Temperature Retrievals

AbstractThe strongest volcanic eruption since the 19th century occurred on 15 January 2022 at Hunga Tonga‐Hunga Ha'apai, generating unprecedented atmospheric waves not seen before in observations. We used satellite microwave observations from (a) Advanced Technology Microwave Sounder (ATMS) on board the National Oceanic and Atmospheric Administration (NOAA)‐20 and the Suomi‐National Polar‐orbiting Partnership (SNPP) and (b) Advanced Microwave Sounding Unit (AMSU)‐A on board Meteorological operational satellite (MetOp)‐B/MetOp‐C to study these waves in the stratosphere immediately after the eruption. The NOAA Microwave Integrated Retrieval System (MiRS) was applied to these microwave observations to produce atmospheric temperature profiles. The atmospheric Lamb wave and fast‐traveling gravity waves are clearly revealed in both the brightness temperatures and the MiRS retrieved temperatures, revealing their vertical phase structures. This study is the first attempt to perform a detailed analysis of the stratospheric impact of the Tonga eruption on operational satellite microwave observations and the corresponding MiRS retrievals.