Suomi NPP Research Articles

Assessment of the Reprocessed Suomi NPP VIIRS Enterprise Cloud Mask Product

The Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership (S-NPP) satellite continually provides global observations used to retrieve over 20 VIIRS Environmental Data Record (EDR) products. Among them, the cloud mask product is essential for many other VIIRS EDR products such as aerosols, ocean color, and active fire. The reprocessed S-NPP VIIRS Sensor Data Record (SDR) data produced by NOAA/Center for Satellite Applications and Research (STAR) have shown improved stability and consistency. Recently, the VIIRS Enterprise Cloud Mask (ECM) has been reprocessed using the reprocessed VIIRS SDR data. This study assesses the reprocessed ECM product by comparing the reprocessed cloud mask types and cloud probability with those from the operational VIIRS ECM product. It found that the overall differences are small. Most of the discrepancies occur between neighboring types at the cloud edge. These findings help lay the foundation for the user community to understand the reprocessed ECM product. In addition, due to the better quality of the reprocessed VIIRS SDR data that are utilized to generate the reprocessed ECM product, it is expected that the reprocessed ECM product will have better stability and consistency compared to the operational ECM products. Therefore, the reprocessed ECM product is a useful benchmark for the user community.

Urban growth analysis of Tamil Nadu state, India using VIIRS DNB night data during 2012 and 2016

Cities in developing countries like India are the real drivers of multi-sectoral growth. Temporal information acquired through geo-spatial technology helps in assessing the trends in urban growth. In this context, the urban growth is analyzed for Tamil Nadu State in India, during 2012–2016 using composite VIIRS DNB (Day and Night Band) Sensor Data acquired by the Suomi National Polar-orbiting Partnership (Suomi NPP) satellite. These images depict the night time illumination of different features as digital numbers. The processed VIIRS images have been classified into five categories based on the level of illumination, with high illumination level indicating presence of built up. Change detection with respect to Urban built up of entire Tamil Nadu has been carried out at two levels viz., district wise and also along the corridors. Image processing techniques have been used for classification of two time data sets and GIS has been used for change detection analysis. Based on this analysis districts have been classified into three categories viz., more, medium and less developed districts. By comparing and assessing the different corridors, western corridor is observed to have more urban growth than other areas of Tamil Nadu. A similar trend is observed from thematic maps generated using IRS AWiFS data for the same period. The study has potentials to help the policy makers and planners in developing industrial estates, improving transportation facilities, planning new industrial developments and, many other activities.

Calibration of the DSCOVR EPIC Visible and NIR Channels using Multiple LEO Radiometers

The first five years of operation of the Deep Space Climate Observatory (DSCOVR) Earth Polychromatic Imaging Camera (EPIC) at the Lagrange one point have produced results that uniquely complement the data from currently operating low orbit Earth-observing instruments. In this paper we describe an updated unified approach to EPIC calibration. In this approach, calibration coefficients and their trends were obtained by comparing EPIC observations to the measurements from polar orbiting radiometers. In this study L1B reflectances from Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the Aqua and Terra satellites, Multi-angle Imaging Spectroradiometer (MISR) onboard Terra and Visible Infrared Imaging Radiometer (VIIRS) onboard the Suomi National Polar-orbiting Partnership (Suomi NPP) spacecraft were used to infer calibration coefficients for four EPIC visible and near-infrared channels: 443 nm, 551 nm, 680 nm, and 780 nm. EPIC Version three measurements made between June 2015 and August 2020 were used for comparison. The calibration procedure identifies the most homogeneous low Earth orbit radiometer scenes matching scattering angles that are temporarily and spatially collocated with EPIC observations. These scenes are used to determine reflectance to count (R/C) ratios in spectrally analogous channels. Seasonal average R/C ratios were analyzed to obtain EPIC calibration gains and trends. The trends for the full dataset period are not statistically significant except in the 443 nm channel. No significant changes in calibration were found after the instrument’s exit from safe hold in March 2020. The R/C ratios were also used to determine the differences in EPIC gains resulting from separate calibrations: against MODIS Aqua or Terra, as well as against forward or aftward MISR cameras. Statistical tests indicate that the differences between the two datasets are not significant except in the 780 nm channels where Aqua-derived coefficients may be around 2% lower compared to Terra. The dependence of EPIC calibration gains on the instrument scattering angle and on DSCOVR-Earth distance were investigated. Lastly, model Low Earth Orbit (LEO) reflectances calculated to match the EPIC viewing geometry were employed to study how EPIC calibration coefficients depend on EPIC-LEO viewing geometry differences. The effect of LEO and EPIC angular mismatch on calibration was shown to be small.

An analysis of volcanic SO2 and ash emissions from Copahue volcano

During 2016, Copahue volcano (Neuquén, Argentina) made several eruptions and emitted ash and gases. This paper presents an analysis of the volcanic plume using dispersion models, remote sensor data, and analysis of sedimentary ash collected at the Caviahue Health Center from early October to 21st December 2016. Water-soluble elements contained in volcanic ash leachates were quantified. We have carried out a qualitative comparison of HYSPLIT dispersion simulations with SO2 and aerosol data obtained from OMI and OMPS sensor on board of AURA and SUOMI NPP satellites, as well as between modeled concentrations and sampled surface sediments. Good agreement was observed between gas dispersion plumes from HYSPLIT and total column SO2 values, as well as between volcanic ash plumes and satellite aerosol indices.The average SO2 emission rate in the analyzed events was 985.7 ± 492.9 t/d and the average emission rate of ash 2.6 103 kt/d (2.9 104 kg/s). In situ observations of ash deposition rates peaked at 0.23 g/h on 30th November. The average measured deposition rate from 1st October to 21st December was 0.12 g/h, consistent with modeled values. The geochemical analysis of ash leachate showed major content of sodium (Na), calcium (Ca) and magnesium (Mg) with 207.8 mg/kg, 209.5 mg/kg, and 195.3 mg/kg, respectively. Concentrations sulfate, chlorides, and fluoride anions were 990.7 mg/kg, 352.34 mg/kg, and 129.15 mg/kg, respectively. The SO2/HCl molar ratio was 1.05. In addition, traces of As, Cr, Hg, Mo, Pb, and Se were detected in concentrations ranging from 0.2 mg/kg to 6.0 mg/kg.

Spatial Registration Assessments for the SNPP and N20 VIIRS Reflective Solar Bands Using Unscheduled Lunar Observations

The Visible Infrared Imaging Radiometer Suite (VIIRS) is a multispectral Earth-observing instrument onboard the Suomi-NPP (SNPP) and NOAA-20 (N20) spacecraft, with spectral bands ranging in wavelength from 0.41 to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$12.2~\mu \text{m}$ </tex-math></inline-formula> . For the reflective solar bands (RSBs), the bands are calibrated on-orbit using both solar diffuser (SD) and lunar observations. The lunar observations use near-monthly scheduled spacecraft maneuvers in order to view the Moon within a desired phase angle range. While the primary purpose of the maneuvers is for radiometric calibration, these observations can also be used to characterize the spatial performance of the instrument, including the band-to-band and detector-to-detector registrations (BBR/DDR). The Moon can also be observed without spacecraft maneuvers. However, these observations are over a larger phase angle range. While the geometry of these unscheduled observations is more varied, they can still be used to assess the sensor performance. In this work, we will use unscheduled Moon data to analyze the BBR and DDR of the SNPP and N20 VIIRS RSB. For the BBR, we implemented an image cross correlation approach that removes the residual oscillations in the trending data compared to previous methodologies. For the DDR, we developed an edge-fitting approach that accounts for the lunar motion across the VIIRS focal plane array on a scan-by-scan basis using lunar and satellite ephemeris data. In our analysis, we find that the BBR and DDR for both VIIRS RSBs are stable on-orbit.

Evaluation of VIIRS and MODIS Snow Cover Fraction in High-Mountain Asia Using Landsat 8 OLI

We present the first application of the Snow Covered Area and Grain size model (SCAG) to the Visible Infrared imaging Radiometer Suite (VIIRS) and assess these retrievals with finer‐resolution fractional snow cover maps from Landsat 8 Operational Land Imager (OLI). Because Landsat 8 OLI avoids saturation issues common to Landsat 1–7 in the visible wavelengths, we re-assess the accuracy of the SCAG fractional snow cover maps from Moderate Resolution Imaging Spectroradiometer (MODIS) that were previously evaluated using data from earlier Landsat sensors. Use of the fractional snow cover maps from Landsat 8 OLI shows a negative bias of −0.5% for MODSCAG and −1.3% for VIIRSCAG, whereas previous MODSCAG evaluations found a bias of −7.6% in the Himalaya. We find similar root mean squared error (RMSE) values of 0.133 and 0.125 for MODIS and VIIRS, respectively. The Recall statistic (probability of detection) for cells with more than 15% snow cover in this challenging steep topography was found to be 0.90 for both MODSCAG and VIIRSCAG, significantly higher than previous evaluations based on Landsat 5 Thematic Mapper (TM) and 7 Enhanced Thematic Mapper Plus (ETM+). In addition, daily retrievals from MODIS and VIIRS are consistent across gradients of elevation, slope, and aspect. Different native resolutions of the gridded products at 1 km and 500 m for VIIRS and MODIS, respectively, result in snow cover maps showing a slightly different distribution of values with VIIRS having more mixed pixels and MODIS having 7% more pure snow pixels. Despite the resolution differences, the snow maps from both sensors produce similar total snow-covered areas and snow-line elevations in this region, with R2 values of 0.98 and 0.88, respectively. We find that the SCAG algorithm performs consistently across various spatial resolutions and that fractional snow cover maps from the VIIRS instruments aboard Suomi NPP, JPPS–1, and JPPS–2 can be a suitable replacement as MODIS sensors reach their ends of life.

Bio-Optical Characteristics of the Black Sea Coastal Waters near Sevastopol: Assessment of the MODIS and VIIRS Products Accuracy

Purpose. The purpose of the work is to evaluate accuracy of the satellite products for the coastal waters near Sevastopol, generated by the standard algorithms based on the MODIS and VIIRS (installed at the artificial Earth satellites Aqua and Terra, and at Suomi NPP, respectively) data. Methods and Results. In situ sampling was carried out at the station (44°37’26" N and 33°26’05" E) located at a distance of two miles from the Sevastopol Bay. The chlorophyll a concentration was measured by the spectrophotometric method. The spectral light absorption coefficients by optically active components were measured in accordance with the current NASA protocol. The spectroradiometers MODIS and VIIRS Level-2 data with spatial resolution 1 km in nadir around the in situ station (44°37’26"±0°00’32" N and 33°26’05"±0°00’54" E) were used. The satellite products were processed by the SeaDAS 7.5.3 software developed in NASA. The research showed that the standard NASA algorithms being applied to the MODIS and VIIRS data, yielded incorrect values of the optically active components’ content in the Black Sea coastal waters near Sevastopol as compared to the data of in situ measurements in the same region: the satellite-derived “chlorophyll a concentration” was on average 1.6 times lower in spring, and 1.4 times higher in summer; the contribution of phytoplankton to total light absorption at 443 nm was underestimated in 8.7 times; the light absorption by colored detrital matter was overestimated in 2.2 times. Conclusions. The NASA standard algorithms are inapplicable to calculating bio-optical indices in the coastal waters of the Black Sea near Sevastopol since they provide incorrect values of the satellite products (Ca-s, aph-s(443) and aCDM-s(443)). Operative ecological monitoring based on satellite data requires development of a regional algorithm taking into account the seawater optical features in the region and in the coastal zone, in particular.

Bioopticheskiye kharakteristiki pribrezhnykh vod Chernogo morya vblizi Sevastopolya: otsenka tochnosti sputnikovykh produktov, vosstanovlennykh po dannym MODIS i VIIRS

Purpose. The purpose of the work is to evaluate accuracy of the satellite products for the coastal waters near Sevastopol, reconstructed by the standard algorithms based on the MODIS and VIIRS (installed at the artificial Earth satellites Aqua and Terra, and at Suomi NPP, respectively) data. Methods and Results. In situ sampling was carried out at the station (44°37'26" N and 33°26'05" E) located at a distance of two miles from the Sevastopol Bay. The chlorophyll a concentration was measured by the spectrophotometric method. The spectral light absorption coefficients by optically active components were measured in accordance with the current NASA protocol. The spectroradiometers MODIS and VIIRS Level 2 data with spatial resolution 1 km in nadir around the in situ station (44°37'26"±0°00'32" N and 33°26'05"±0°00'54" E) were used. The satellite products were processed by the SeaDAS 7.5.3 software developed in NASA. The research showed that the standard NASA algorithms being applied to the MODIS and VIIRS data, yielded incorrect values of the optically active components’ content in the Black Sea coastal waters near Sevastopol as compared to the data of in situ measurements in the same region: the satellite-derived “chlorophyll a concentration” was on average 1.6 times lower in spring, and 1.4 times higher in summer; the contribution of phytoplankton pigments to total light absorption at 443 nm was underestimated in 8.7 times; the light absorption by colored detrital organic matter was overestimated in 2.2 times. Conclusions. The NASA standard algorithms are inapplicable to calculating bio-optical indices in the coastal waters of the Black Sea near Sevastopol since they provide incorrect values of the satellite products (Ca-s, aph-s(443) and aCDM-s(443)). Operative ecological monitoring based on satellite data requires development of a regional algorithm taking into account the seawater optical features in the region and in the coastal zone, in particular.

Mission-Long Recalibrated Science Quality Suomi NPP VIIRS Radiometric Dataset Using Advanced Algorithms for Time Series Studies

Suomi NPP has been successfully operating since its launch on 28 October 2011. As one of the major payloads, along with microwave and infrared sounders (Advanced Technology Microwave Sounder (ATMS), Cross-track Infrared Sounder (CrIS)), and ozone mapping/profiling (OMPS) instruments, the Visible Infrared Imaging Radiometer Suite (VIIRS) has performed for well beyond its mission design life. Its data have been used for a variety of applications for nearly 30 environmental data products, including global imagery twice daily with 375 and 750 m resolutions, clouds, aerosol, cryosphere, ocean color and sea-surface temperature, a number of land products (vegetation, land-cover, fire and others), and geophysical and social economic studies with nightlights. During the early days of VIIRS operational calibration and data production, there were inconsistencies in both algorithms and calibration inputs, for several reasons. While these inconsistencies have less impact on nowcasting and near real-time applications, they introduce challenges for time series analysis due to calibration artifacts. To address this issue, we developed a comprehensive algorithm, and recalibrated and reprocessed the Suomi NPP VIIRS radiometric data that have been produced since the launch. In the recalibration, we resolved inconsistencies in the processing algorithms, terrain correction, straylight correction, and anomalies in the thermal bands. To improve the stability of the reflective solar bands, we developed a Kalman filtering model to incorporate onboard solar, lunar, desert site, inter-satellite calibration, and a deep convective cloud calibration methodology. We further developed and implemented the Solar Diffuser Surface Roughness Rayleigh Scattering model to account for the sensor responsivity degradation in the near infrared bands. The recalibrated dataset was validated using vicarious sites and alternative methods, and compared with independent processing from other organizations. The recalibrated radiometric dataset (namely, the level 1b or sensor data records) also incorporates a bias correction for the reflective solar bands, which not only addresses known calibration biases, but also allows alternative calibrations to be applied if so desired. The recalibrated data have been proven to be of high quality, with much improved stability (better than 0.3%) and accuracy (by up to 2%). The recalibrated radiance data are now available from 2012 to 2020 for users and will eventually be archived on the NOAA CLASS database.

Reprocessing of Suomi NPP CrIS Sensor Data Records to Improve the Radiometric and Spectral Long-Term Accuracy and Stability

Since early 2012, the cross-track infrared sounder (CrIS) on board the Suomi National Polar-orbiting Partnership (S-NPP) satellite has continually provided the hyperspectral infrared observations for profiling atmospheric temperature, moisture, and greenhouse gases. In this study, the CrIS sensor data record (SDR) data are improved for climate applications with its fine-tuning of calibration coefficients in an NOAA reprocessing project. A specific software system was developed to reprocess the CrIS SDR. This software system was updated with a new calibration algorithm, nonlinearity, and geolocation to improve the SDR data quality and long-term consistency. The calibration coefficients are refined with the latest updates, which were used to calibrate the latest operational SDR products and replace those in the engineering packet (EP) in the raw data record (RDR) data stream. The resampling wavelength was updated based on the metrology laser wavelength and resulted in zero sampling error in the spectral calibration. All the historical SDRs (from February 2012 to March 2017) were generated with the same calibration coefficients and same version of the processing software system, resulting in improved accuracy and stability in terms of spectral and radiometric calibration during the CrIS lifetime mission. The quality of the reprocessed CrIS SDR data at nominal spectral resolution (NSR) is assessed in terms of its radiometric and spectral calibration. Comparisons against the operational SDR data are carried out to demonstrate the improved long-term stability of the reprocessed CrIS SDR data. Overall radiometric biases are found to be small and highly stable over the instrument mission, the FOV-to-FOV differences are less than ~10 mK, and much better than that from the operational SDR data. It is shown that the CrIS metrology laser wavelength varies within 4 ppm as measured by the neon calibration system. The reprocessed SDR data have spectral errors less than 0.5 ppm, which is much better than the operational SDR data with about 4 ppm. This baseline version of the reprocessed SNPP CrIS SDR data is suitable for long-term climate monitoring and model assessments and can provide an infrared reference observation to assess other narrow- or broadband infrared instruments’ calibration accuracy.

ECOSTRESS estimates gross primary production with fine spatial resolution for different times of day from the International Space Station

Accurate estimation of gross primary production (GPP), the amount of carbon absorbed by plants via photosynthesis, is of great importance for understanding ecosystem functions, carbon cycling, and climate-carbon feedbacks. Remote sensing has been widely used to quantify GPP at regional to global scales. However, polar-orbiting satellites (e.g., Landsat, Sentinel, Terra, Aqua, Suomi NPP, JPSS, OCO-2) lack the capability to examine the diurnal cycles of GPP because they observe the Earth's surface at the same time of day. The Ecosystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS), launched in June 2018, observes the land surface temperature (LST) at different times of day with high spatial resolution (70 m × 70 m) from the International Space Station (ISS). Here, we made use of ECOSTRESS data to predict instantaneous GPP with high spatial resolution for different times of day using a data-driven approach based on machine learning. The predictive GPP model used instantaneous ECOSTRESS LST observations along with the daily enhanced vegetation index (EVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS), land cover type from the National Land Cover Database (NCLD), and instantaneous meteorological data from the ERA5 reanalysis dataset. Our model estimated instantaneous GPP across 56 flux tower sites fairly well (R2 = 0.88, Root Mean Squared Error (RMSE) = 2.42 μmol CO2 m−2 s−1). The instantaneous GPP estimates driven by ECOSTRESS LST captured the diurnal variations of tower GPP for different biomes. We then produced multiple high resolution ECOSTRESS GPP maps for the central and northern California. We found distinct changes in GPP at different times of day (e.g., higher in late morning, peak around noon, approaching zero at dusk), and clear differences in productivity across landscapes (e.g., savannas, croplands, grasslands, and forests) for different times of day. ECOSTRESS GPP also captured the seasonal variations in the diurnal cycling of photosynthesis. This study demonstrates the feasibility of using ECOSTRESS data for producing instantaneous GPP (i.e., GPP for the acquisition time of the ECOSTRESS data) for different times of day. The ECOSTRESS GPP can shed light on how plant photosynthesis and water use vary over the course of the diurnal cycle and inform agricultural management and future improvement of terrestrial biosphere/land surface models.

Evaluation of Antarctic Ozone Profiles derived from OMPS-LP by using Balloon-borne Ozonesondes

Predicting radiative forcing due to Antarctic stratospheric ozone recovery requires detecting changes in the ozone vertical distribution. In this endeavor, the Limb Profiler of the Ozone Mapping and Profiler Suite (OMPS-LP), aboard the Suomi NPP satellite, has played a key role providing ozone profiles over Antarctica since 2011. Here, we compare ozone profiles derived from OMPS-LP data (version 2.5 algorithm) with balloon-borne ozonesondes launched from 8 Antarctic stations over the period 2012–2020. Comparisons focus on the layer from 12.5 to 27.5 km and include ozone profiles retrieved during the Sudden Stratospheric Warming (SSW) event registered in Spring 2019. We found that, over the period December-January–February-March, the root mean square error (RMSE) tends to be larger (about 20%) in the lower stratosphere (12.5–17.5 km) and smaller (about 10%) within higher layers (17.5–27.5 km). During the ozone hole season (September–October–November), RMSE values rise up to 40% within the layer from 12.5 to 22 km. Nevertheless, relative to balloon-borne measurements, the mean bias error of OMPS-derived Antarctic ozone profiles is generally lower than 0.3 ppmv, regardless of the season.

Evolution of the representation of global vegetation by vegetation continuous fields

Two decades of global annual fractional vegetation cover products have been derived using daily surface reflectance and land surface temperature data from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument aboard NASA's Terra satellite. These MODIS Vegetation Continuous Fields (VCF) products are unique and a distinct advance over discrete (ordinal) land cover characterizations. VCF consists of three continuous fractional cover layers at 250 m resolution summing to 100%: tree cover, non-tree vegetation and non-vegetated surface plus surface water. This paper describes the fully-automated machine learning algorithm used to create MODIS VCF and details recent algorithm improvements. Comparisons of the latest VCF products in the MOD44B collection 6, with field-based fractional tree cover and other remote sensing fractional cover products demonstrate improved agreement: the root mean square (RMS) deviations range from 9 to 23%. A comparison of MODIS canopy cover values with those from the Global Ecosystem Dynamics Investigation (GEDI) gives an R2 value of 0.53. Automation of the algorithm provides flexibility for readily porting the method to derive fractional cover products from additional instruments with similar radiometric characteristics such as the Visible Infrared Imaging Radiometer (VIIRS) payload aboard Suomi NPP and JPSS platforms, and successors.

Assimilation of Satellite Microwave Observations over the Rainbands of Tropical Cyclones

AbstractA novel Bayesian Monte Carlo integration (BMCI) technique was developed to retrieve geophysical variables from satellite microwave radiometer data in the presence of tropical cyclones. The BMCI technique includes three steps: generating a stochastic database, simulating satellite brightness temperatures using a radiative transfer model, and retrieving geophysical variables such as profiles of temperature, relative humidity, and cloud liquid and ice water content from real observations. The technique also provides uncertainty estimates for each retrieval and can output the error covariance matrix of selected parameters. The measurements from the Advanced Technology Microwave Sounder (ATMS) on board Suomi National Polar-Orbiting Partnership (Suomi NPP) and the Global Precipitation Measurement (GPM) Microwave Imager (GMI) were used as input. A new technique was developed to correct the ATMS and GMI observations for the beam-filling effect, which is due to small-scale variability of precipitation and clouds when compared with the instrument footprint and also the nonlinear relation between the brightness temperature and precipitation. In addition, the assimilation of the BMCI retrievals into the NASA GEOS model is discussed for Hurricane Maria. The results show that assimilating the BMCI retrievals can influence the dynamical features of the cyclone, including a stronger warm core, a symmetric eye, and vertically aligned wind columns. Two possible factors that may limit the impact of the BMCI retrievals include 1) the resolution of the model (about 25 km), which was too coarse to show the potential of the BMCI data in improving the representation of tropical storms in the model forecast, and 2) the data assimilation system not being able to consider vertically correlated observation errors.

Satellite Remote Sensing for Monitoring of Astroclimatic Conditions of the Khovsgol Aimag (Mongolia)

The problem of exploring a territory for designing a full-scale gamma observatory is discussed. It is caused by the need to increase the sensitivity and energy threshold for detecting gamma rays of the existing TAIGA gamma observatory array.&#x0D; The study of astroclimatic conditions over the territory of the Khovsgol Aimag (Mongolia) was carried out for the purposes of performing nighttime astrophysical observations. Spatial distributions of cloudless events fraction and atmospheric water vapor content were obtained for several periods of active phases of astrophysical observations using the night-time data from the VIIRS instrument onboard Suomi NPP satellite as well as hyperspectrometer AIRS onboard Aqua satellite.&#x0D; Analysis of satellite data has shown that upon topographic and infrastructure criteria, the plateau of Lake Khuvsgul is the most suitable region for the deployment of a full-scale gamma-ray observatory with a hybrid detector system. According to VIIRS/SNPP data over the territory of interest for two seasons, it was revealed that the average fraction of cloudless events is about 65 % and the atmospheric water vapor content (according to AIRS/ Aqua data) is low, amounting to 2-2.5 kg/m2.

Can VIIRS continue the legacy of MODIS for near real-time monitoring of tropical forest disturbance?

Near real-time monitoring of forest disturbance is a critical tool for combating illegal logging. The only feasible data sources for near real-time monitoring over large area are remote sensing data, and historically the only sensors with repeat times sufficient for near-real time monitoring are from coarse-resolution sensors such as MODIS. The MODIS sensors onboard the Terra and Aqua satellites have provided near daily global observations in the red and NIR spectral bands at 250 m resolution since 1999. While their longevity is impressive, the sensors are nearing the end of their lifespan and the MODIS data record will be continued by the VIIRS sensor onboard Suomi NPP. But because of the spatial and spectral differences between MODIS and VIIRS, it is worth investigating whether VIIRS can replace MODIS as input data for near real-time monitoring. In this article, we compared the performance of the NRT-CCDC (Near Real-Time Continuous Change Detection and Classification) algorithm using the Terra/ and Aqua/MODIS composites at 250 m resolution, Terra/MODIS 250 m and 500 m surface reflectance, Aqua/MODIS 250 m and 500 m surface reflectance, and VIIRS 500 m surface reflectance. We found that the algorithm using composites from both MODIS sensors and Terra/MODIS data at 250 m resolution outperformed the results obtained when using VIIRS data. The results were similar when using 500 m data from Terra/MODIS alone compared to VIIRS data. We found that a morning overpass, like that of Terra, is preferable to an afternoon overpass, which is employed by VIIRS and Aqua. Spatial and temporal resolution of the input data are two key factors in successful near real-time monitoring of forest disturbance.

Radiometric consistency between GOES-16 ABI and VIIRS on Suomi NPP and NOAA-20

NOAA has both geostationary (GEO) and polar orbiting (LEO) satellites that are procured through different vendors. For long-term environmental studies, it is essential to establish consistent radiometric calibration among satellite instruments irrespective of the satellite platforms. The Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership (S-NPP) and NOAA-20 satellites has been providing critical weather and climate-related data. With the launch of NOAA-20 in November 2017, the global coverage of VIIRS has doubled. With more than 7 years in space, S-NPP VIIRS has been rigorously calibrated and validated. We aim to quantify the radiometric consistency between the two VIIRS instruments through double differencing using GOES-16 Advanced Baseline Imager (ABI) as a reference instrument. We also provide insight into the temporal radiometric consistency between VIIRS and ABI. Since no direct simultaneous nadir overpasses (SNOs) exist between SNPP and NOAA-20 for intercomparison, GEO-LEO intercalibration is performed using SNOs between ABI and VIIRS instruments. We show that the NOAA-20 VIIRS measured top-of-atmosphere (TOA) reflectance is consistently lower than that of the S-NPP, mostly on the order of 2% to 3%, consistent with the past studies. GOES-16 ABI-observed reflectance is higher than both VIIRS instruments. SNOs over the all-sky tropical ocean are analyzed to quantify the bias for both NOAA-20 and S-NPP VIIRS relative to ABI. The impact on bias due to spectral differences is accounted for using spectral band adjustment factors estimated using hyperspectral measurements from SCIAMACHY. Uncertainties exist mainly due to calibration uncertainties in VIIRS and ABI, the time difference between the VIIRS and ABI observations, differences in cloud contamination, spectral response function differences, and lack of in-situ hyperspectral data to account for the spectral bias.

Evaluation and Improvement of the Near-Real-Time Linear Fit SO2Retrievals From Suomi NPP Ozone Mapping and Profiler Suite

Changing a logic switch threshold in the linear fit sulfur dioxide (LFSO2) algorithm improves the performance based on the evaluation of the NOAA operational atmospheric SO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> near-real-time (NRT) retrieval. The LFSO2 is used to create estimates from measurements made by the Suomi NPP (S-NPP) Ozone Mapping and Profiler Suite (OMPS). We evaluate the LFSO2 and compare the results to those from a principal component analysis (PCA) offline algorithm. Twenty independent volcanic scenarios and one environmental disaster scenario spread over eight years are selected for this comparison. More than three months of Kilauea volcanic activity in 2018 are monitored and are included in this evaluation and comparison. We found that the operational LFSO2 retrievals at lower troposphere (TRL), mid-troposphere (TRM), and lower stratosphere (STL) exhibited a discontinuity and have a saturation-like relationship if compared with PCA results. Using the new retrieval logic, the discontinuity in LFSO2 retrievals and the saturation appearance in comparisons vanished and a close to a linear relationship with the matchup data from the PCA retrieval products is demonstrated. The minimum detectable values for all three SO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> layer products and the planetary boundary layer (PBL) products are estimated with the updated LFSO2 algorithm. Results for a volcanic cloud over Colombia for the updated LFSO2 for OMPS and a Differential Optical Absorption Spectroscopy (DOAS) algorithm for the Tropospheric Monitoring Instrument (TROPOMI) measurements are also examined. Similar SO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> total mass estimates over the region are obtained from the two products.

Santa Catarina visto do espaço à noite e o sistema viário

O produto cartográfico derivado de imagens noturnas (Visible Infrared Imaging Radiometer - VIIRS) do sensor Suomi NPP pode observar pouca luz até a escala de uma lâmpada de estrada isolada ou de um barco de pesca. A proposta foi aplicar este recurso para identificar os espaços luminiosos e opacos, discutidos em Santos e Oliveira (2001). Neste artigo descreve-se a metodologia aplicada. O produto final se mostrou interessante para usos no planejamento e gestão territorial.

Intercomparison of the SNPP and NOAA-20 VIIRS DNB High-Gain Stage Using Observations of Bright Stars

The Visible Infrared Imaging Radiometer Suite (VIIRS) on board the Suomi-NPP (SNPP) and NOAA-20 (N20) spacecrafts is a multispectral Earth-observing instrument with bands covering wavelengths from visible to long-wave infrared. Among these bands is a panchromatic day/night band (DNB) with a broad spectral response ranging from 500 to 900 nm, and a high dynamic range spanning over seven orders of magnitude, allowing for observations to take place during both daytime and nighttime. The DNB operates at three gain levels, with low- and mid-gain stages and two high-gain stages (HGSs). The HGS is capable of detecting dim city lights during Earth-view observations at night as well as bright stars through the instrument space-view port. Since SNPP and N20 are at opposite points of the same orbit, each VIIRS instrument is able to observe the same stars with the DNB in successive orbits. This will allow us to make a direct comparison of the relative calibration of each instrument using stars over a range of spectral classes. In this article, we develop methodology for accurately identifying target stars in order to make proper comparisons between the DNB HGS of each instrument. We then take observations from multiple stars in order to compute the ratio in the measured irradiance for each instrument as a function of spectral class. For K-type stars, which have the least spectral change over the DNB wavelength range, we measure a calibration bias between the SNPP and N20 DNB HGS of approximately 4%, which is stable over the duration of the N20 mission.