scholarly journals Limb Correction of MODIS and VIIRS Infrared Channels for the Improved Interpretation of RGB Composites

2016 ◽  
Vol 33 (5) ◽  
pp. 1073-1087 ◽  
Author(s):  
Nicholas J. Elmer ◽  
Emily Berndt ◽  
Gary J. Jedlovec
Keyword(s):  
Situational Awareness ◽  
Infrared Imaging ◽  
Temporal Frequency ◽  
Multiple Sensors ◽  
Limb Effect ◽  
Moderate Resolution ◽  
Multiple Case ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Spectral Channels

AbstractRed–green–blue (RGB) composite imagery combines information from several spectral channels into one image to aid in the operational analysis of atmospheric processes. However, infrared channels are adversely affected by the limb effect, the result of an increase in optical pathlength of the absorbing atmosphere between the satellite and the earth as viewing zenith angle increases. This study develops a technique to quickly correct for limb effects in both clear and cloudy regions using latitudinally and seasonally varying limb correction coefficients for real-time applications. These limb correction coefficients account for the increase in optical pathlength in order to produce limb-corrected RGB composites. The improved functionality of limb-corrected RGB composites is demonstrated by multiple case studies of Air Mass and Dust RGB composites using Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) and Suomi–National Polar-Orbiting Partnership (SNPP) Visible Infrared Imaging Radiometer Suite (VIIRS) imagery. However, the limb correction can be applied to any polar-orbiting sensor infrared channels, provided the proper limb correction coefficients are calculated. Corrected RGB composites provide multiple advantages over uncorrected RGB composites, including increased confidence in the interpretation of RGB features, improved situational awareness for operational forecasters, and the ability to use RGB composites from multiple sensors jointly to increase the temporal frequency of observations.

scholarly journals Real-Time Detection of Daytime and Night-Time Fire Hotspots from Geostationary Satellites

2021 ◽  
Vol 13 (9) ◽  
pp. 1627
Author(s):  
Chermelle B. Engel ◽  
Simon D. Jones ◽  
Karin J. Reinke
Keyword(s):  
Real Time ◽  
Infrared Imaging ◽  
Night Time ◽  
Clear Link ◽  
Radiative Power ◽  
Biogeographical Region ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Fire Radiative Power

This paper introduces an enhanced version of the Biogeographical Region and Individual Geostationary HHMMSS Threshold (BRIGHT) algorithm. The algorithm runs in real-time and operates over 24 h to include both daytime and night-time detections. The algorithm was executed and tested on 12 months of Himawari-8 data from 1 April 2019 to 31 March 2020, for every valid 10-min observation. The resulting hotspots were compared to those from the Visible Infrared Imaging Radiometer Suite (VIIRS) and the Moderate Resolution Imaging Spectroradiometer (MODIS). The modified BRIGHT hotspots matched with fire detections in VIIRS 96% and MODIS 95% of the time. The number of VIIRS and MODIS hotspots with matches in the coincident modified BRIGHT dataset was lower (at 33% and 46%, respectively). This paper demonstrates a clear link between the number of VIIRS and MODIS hotspots with matches and the minimum fire radiative power considered.

scholarly journals Derivation of Shortwave Radiometric Adjustments for SNPP and NOAA-20 VIIRS for the NASA MODIS-VIIRS Continuity Cloud Products

2020 ◽  
Vol 12 (24) ◽  
pp. 4096 ◽  
Author(s):  
Kerry Meyer ◽  
Steven Platnick ◽  
Robert Holz ◽  
Steve Dutcher ◽  
Greg Quinn ◽  
...  
Keyword(s):  
Optical Properties ◽  
Infrared Imaging ◽  
Trend Detection ◽  
Adjustment Factors ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Water Cloud ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
New Generation ◽  
Cloud Mask

Climate studies, including trend detection and other time series analyses, necessarily require stable, well-characterized and long-term data records. For satellite-based geophysical retrieval datasets, such data records often involve merging the observational records of multiple similar, though not identical, instruments. The National Aeronautics and Space Administration (NASA) cloud mask (CLDMSK) and cloud-top and optical properties (CLDPROP) products are designed to bridge the observational records of the Moderate-resolution Imaging Spectroradiometer (MODIS) onboard NASA’s Aqua satellite and the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the joint NASA/National Oceanic and Atmospheric Administration (NOAA) Suomi National Polar-orbiting Partnership (SNPP) satellite and NOAA’s new generation of operational polar-orbiting weather platforms (NOAA-20+). Early implementations of the CLDPROP algorithms on Aqua MODIS and SNPP VIIRS suffered from large intersensor biases in cloud optical properties that were traced back to relative radiometric inconsistency in analogous shortwave channels on both imagers, with VIIRS generally observing brighter top-of-atmosphere spectral reflectance than MODIS (e.g., up to 5% brighter in the 0.67 µm channel). Radiometric adjustment factors for the SNPP and NOAA-20 VIIRS shortwave channels used in the cloud optical property retrievals are derived from an extensive analysis of the overlapping observational records with Aqua MODIS, specifically for homogenous maritime liquid water cloud scenes for which the viewing/solar geometry of MODIS and VIIRS match. Application of these adjustment factors to the VIIRS L1B prior to ingestion into the CLDMSK and CLDPROP algorithms yields improved intersensor agreement, particularly for cloud optical properties.

scholarly journals Calibration of the DSCOVR EPIC visible and NIR channels using MODIS Terra and Aqua data and EPIC lunar observations

2018 ◽  
Vol 11 (1) ◽  
pp. 359-368 ◽  
Author(s):  
Igor V. Geogdzhayev ◽  
Alexander Marshak
Keyword(s):  
Low Earth Orbit ◽  
Natural Choice ◽  
Imaging Camera ◽  
Lunar Observations ◽  
Moderate Resolution ◽  
Important Addition ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Spectral Channels ◽  
Calibration Coefficients

Abstract. The unique position of the Deep Space Climate Observatory (DSCOVR) Earth Polychromatic Imaging Camera (EPIC) at the Lagrange 1 point makes an important addition to the data from currently operating low Earth orbit observing instruments. EPIC instrument does not have an onboard calibration facility. One approach to its calibration is to compare EPIC observations to the measurements from polar-orbiting radiometers. Moderate Resolution Imaging Spectroradiometer (MODIS) is a natural choice for such comparison due to its well-established calibration record and wide use in remote sensing. We use MODIS Aqua and Terra L1B 1 km reflectances to infer calibration coefficients for four EPIC visible and NIR channels: 443, 551, 680 and 780 nm. MODIS and EPIC measurements made between June 2015 and 2016 are employed for comparison. We first identify favorable MODIS pixels with scattering angle matching temporarily collocated EPIC observations. Each EPIC pixel is then spatially collocated to a subset of the favorable MODIS pixels within 25 km radius. Standard deviation of the selected MODIS pixels as well as of the adjacent EPIC pixels is used to find the most homogeneous scenes. These scenes are then used to determine calibration coefficients using a linear regression between EPIC counts s−1 and reflectances in the close MODIS spectral channels. We present thus inferred EPIC calibration coefficients and discuss sources of uncertainties. The lunar EPIC observations are used to calibrate EPIC O2 absorbing channels (688 and 764 nm), assuming that there is a small difference between moon reflectances separated by ∼ 10 nm in wavelength and provided the calibration factors of the red (680 nm) and NIR (780 nm) are known from comparison between EPIC and MODIS.

scholarly journals The Continuity MODIS-VIIRS Cloud Mask

2020 ◽  
Vol 12 (20) ◽  
pp. 3334 ◽  
Author(s):  
Richard A. Frey ◽  
Steven A. Ackerman ◽  
Robert E. Holz ◽  
Steven Dutcher ◽  
Zach Griffith
Keyword(s):  
Infrared Imaging ◽  
Cloud Fraction ◽  
The Arctic ◽  
Orthogonal Polarization ◽  
Cloud Detection ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Hit Rates ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Cloud Mask

This paper introduces the Continuity Moderate Resolution Imaging Spectroradiometer (MODIS)-Visible Infrared Imaging Radiometer Suite (VIIRS) Cloud Mask (MVCM), a cloud detection algorithm designed to facilitate continuity in cloud detection between the MODIS (Moderate Resolution Imaging Spectroradiometer) on the Aqua and Terra platforms and the series of VIIRS (Visible Infrared Imaging Radiometer Suite) instruments, beginning with the Soumi National Polar-orbiting Partnership (SNPP) spacecraft. It is based on the MODIS cloud mask that has been operating since 2000 with the launch of the Terra spacecraft (MOD35) and continuing in 2002 with Aqua (MYD35). The MVCM makes use of fourteen spectral bands that are common to both MODIS and VIIRS so as to create consistent cloud detection between the two instruments and across the years 2000–2020 and beyond. Through comparison data sets, including collocated Aqua MODIS and Cloud-Aerosol LIdar with Orthogonal Polarization (CALIOP) from the A-Train, this study was designed to assign statistical consistency benchmarks between the MYD35 and MVCM cloud masks. It is shown that the MVCM produces consistent cloud detection results between Aqua MODIS, SNPP VIIRS, and NOAA-20 VIIRS and that the quality is comparable to the standard Aqua MODIS cloud mask. Globally, comparisons with collocated CALIOP lidar show combined clear and cloudy sky hit rates of 88.2%, 87.5%, 86.8%, and 86.8% for MYD35, MVCM Aqua MODIS, MVCM SNPP VIIRS, and MVCM NOAA-20 VIIRS, respectively, for June through until August, 2018. For the same months and in the same order for 60S–60N, hit rates are 90.7%, 90.5%, 90.1%, and 90.3%. From the time series constructed from gridded daily means of 60S–60N cloud fractions, we found that the mean day-to-day cloud fraction differences/standard deviations in percent to be 0.68/0.55, 0.94/0.64, −0.20/0.50, and 0.44/0.82 for MVCM Aqua MODIS-MVCM SNPP VIIRS day and night, and MVCM NOAA-20 VIIRS-MVCM SNPP VIIRS day and night, respectively. It is seen that the MODIS and VIIRS 1.38 µm cirrus detection bands perform similarly but with MODIS detecting slightly more clouds in the middle to high levels of the troposphere and the VIIRS detecting more in the upper troposphere above 16 km. In the Arctic, MVCM Aqua MODIS and SNPP VIIRS reported cloud fraction differences of 0–3% during the mid-summer season and −3–4% during the mid-winter.

First-Light Imagery from Suomi NPP VIIRS

2013 ◽  
Vol 94 (7) ◽  
pp. 1019-1029 ◽  
Author(s):  
Donald Hillger ◽  
Thomas Kopp ◽  
Thomas Lee ◽  
Daniel Lindsey ◽  
Curtis Seaman ◽  
...  
Keyword(s):  
Infrared Imaging ◽  
Fire Detection ◽  
Satellite System ◽  
Defense Meteorological Satellite Program ◽  
Wide Range ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Low Levels ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Meteorological Satellite

The Suomi National Polar-Orbiting Partnership (NPP) satellite was launched on 28 October 2011, heralding the next generation of operational U.S. polar-orbiting satellites. It carries the Visible– Infrared Imaging Radiometer Suite (VIIRS), a 22-band visible/infrared sensor that combines many of the best aspects of the NOAA Advanced Very High Resolution Radiometer (AVHRR), the Defense Meteorological Satellite Program (DMSP) Operational Linescan System (OLS), and the National Aeronautics and Space Administration (NASA) Moderate Resolution Imaging Spectroradiometer (MODIS) sensors. VIIRS has nearly all the capabilities of MODIS, but offers a wider swath width (3,000 versus 2,330 km) and much higher spatial resolution at swath edge. VIIRS also has a day/night band (DNB) that is sensitive to very low levels of visible light at night such as those produced by moonlight reflecting off low clouds, fog, dust, ash plumes, and snow cover. In addition, VIIRS detects light emissions from cities, ships, oil flares, and lightning flashes. NPP crosses the equator at about 0130 and 1330 local time, with VIIRS covering the entire Earth twice daily. Future members of the Joint Polar Satellite System (JPSS) constellation will also carry VIIRS. This paper presents dramatic early examples of multispectral VIIRS imagery capabilities and demonstrates basic applications of that imagery for a wide range of operational users, such as for fire detection, monitoring ice break up in rivers, and visualizing dust plumes over bright surfaces. VIIRS imagery, both single and multiband, as well as the day/night band, is shown to exceed both requirements and expectations.

scholarly journals Variabilidade Temporal das Concentrações de Clorofila -A e da Temperatura Superficial do Mar na Costa Central de Santa Catarina

2019 ◽  
Vol 2 ◽  
pp. 82-88
Author(s):  
Gabriel Serrato ◽  
Mauricio Noernberg
Keyword(s):  
Infrared Imaging ◽  
Santa Catarina ◽  
Chl A ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer

O fenômeno das florações de algas é conhecido mundialmente devido aos impactos diretos na saúde dos ecossistemas aquáticos, nos recursos pesqueiros e aquícolas, nas atividades recreacionais e na saúde humana. O presente estudo avaliou o uso de sensores satelitais na quantificação dos parâmetros de temperatura superficial do mar (TSM) e concentração de clorofila-a na superfície do mar (Chl-a) na costa de Santa Catarina, durante o período entre 2002 e 2019, objetivando identificar a variabilidade temporal da TSM e da Chl-a a partir da decomposição das séries temporais em tendência, sazonalidade e resíduos. Para atingir este objetivo foram adquiridas estimativas de TSM e Chl-a oriundas dos sensores orbitais Moderate-Resolution Imaging Spectroradiometer (MODIS) a bordo dos satélites (Aqua e Terra) e Visible Infrared Imaging Radiometer Suite (VIIRS) a bordo dos satélites (SNPP e NOAA-20). Através da extração dos valores de TSM e Chl-a para um ponto ao largo da costa central de Santa Catarina foram geradas séries temporais para o período especificado e aplicada a metodologia de decomposição de séries temporais para identificar oscilações de baixa frequência. Foi possível identificar que os eventos com maior Chl-a estão relacionados com eventos com menores valores de TSM. As componentes encontradas poderão ser utilizadas para modelos preditivos auxiliando assim a minimizar os impactos associados às florações de algas.

scholarly journals Calibration of the DSCOVR EPIC visible and NIR channels using MODIS and EPIC lunar observations

2017 ◽  
Author(s):  
Igor V. Geogdzhayev ◽  
Alexander Marshak
Keyword(s):  
Near Ir ◽  
Natural Choice ◽  
Imaging Camera ◽  
Lunar Observations ◽  
Moderate Resolution ◽  
Important Addition ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Spectral Channels ◽  
Calibration Coefficients

Abstract. The unique position of the Deep Space Climate Observatory (DSCOVR) Earth Polychromatic Imaging Camera (EPIC) at the Lagrange 1 point makes an important addition to the data from currently operating low orbit Earth observing instruments. EPIC instrument does not have an onboard calibration facility. One approach to its calibration is to compare EPIC observations to the measurements from polar orbiting radiometers. Moderate Resolution Imaging Spectroradiometer (MODIS) is a natural choice for such comparison due to its well-established calibration record and wide use in remote sensing. We use MODIS Aqua and Terra L1B 1 km reflectances to infer calibration coefficients for four EPIC visible and NIR channels: 443 nm, 551 nm, 680 nm and 780 nm. MODIS and EPIC measurements made between June 2015 and June 2016 are employed for comparison. We first identify favorable MODIS pixels with scattering angle matching temporarily collocated EPIC observations. Each EPIC pixel is then spatially collocated to a subset of the favorable MODIS pixels within 25 km radius. Standard deviation of the selected MODIS pixels as well as of the adjacent EPIC pixels is used to find the most homogeneous scenes. These scenes are then used to determine calibration coefficients using a linear regression between EPIC counts and reflectances in the close MODIS spectral channels. We present thus inferred EPIC calibration coefficients and discuss sources of uncertainties. The Lunar EPIC observations are used to calibrate EPIC O2 absorbing channels (688 nm and 764 nm) assuming that there is a small difference between moon reflectances separated by 10 nm difference in wavelength and the calibration factors of the red (680 nm) and near-IR (780 nm) are known from comparison between EPIC and MODIS.

scholarly journals Evaluation of VIIRS, GOCI, and MODIS Collection 6 AOD retrievals against ground sunphotometer observations over East Asia

2016 ◽  
Vol 16 (3) ◽  
pp. 1255-1269 ◽  
Author(s):  
Q. Xiao ◽  
H. Zhang ◽  
M. Choi ◽  
S. Li ◽  
S. Kondragunta ◽  
...  
Keyword(s):  
Air Quality ◽  
East Asia ◽  
Infrared Imaging ◽  
Environmental Data ◽  
Urban Centers ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Terra Modis

Abstract. Persistent high aerosol loadings together with extremely high population densities have raised serious air quality and public health concerns in many urban centers in East Asia. However, ground-based air quality monitoring is relatively limited in this area. Recently, satellite-retrieved Aerosol Optical Depth (AOD) at high resolution has become a powerful tool to characterize aerosol patterns in space and time. Using ground AOD observations from the Aerosol Robotic Network (AERONET) and the Distributed Regional Aerosol Gridded Observation Networks (DRAGON)-Asia Campaign, as well as from handheld sunphotometers, we evaluated emerging aerosol products from the Visible Infrared Imaging Radiometer Suite (VIIRS) aboard the Suomi National Polar-orbiting Partnership (S-NPP), the Geostationary Ocean Color Imager (GOCI) aboard the Communication, Ocean, and Meteorology Satellite (COMS), and Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) (Collection 6) in East Asia in 2012 and 2013. In the case study in Beijing, when compared with AOD observations from handheld sunphotometers, 51 % of VIIRS Environmental Data Record (EDR) AOD, 37 % of GOCI AOD, 33 % of VIIRS Intermediate Product (IP) AOD, 26 % of Terra MODIS C6 3 km AOD, and 16 % of Aqua MODIS C6 3 km AOD fell within the reference expected error (EE) envelope (±0.05 ± 0.15 AOD). Comparing against AERONET AOD over the Japan–South Korea region, 64 % of EDR, 37 % of IP, 61 % of GOCI, 39 % of Terra MODIS, and 56 % of Aqua MODIS C6 3 km AOD fell within the EE. In general, satellite aerosol products performed better in tracking the day-to-day variability than tracking the spatial variability at high resolutions. The VIIRS EDR and GOCI products provided the most accurate AOD retrievals, while VIIRS IP and MODIS C6 3 km products had positive biases.

scholarly journals Correcting for trace gas absorption when retrieving aerosol optical depth from satellite observations of reflected shortwave radiation

2018 ◽  
Author(s):  
Falguni Patadia ◽  
Robert Levy ◽  
Shana Mattoo
Keyword(s):  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Infrared Imaging ◽  
Gas Absorption ◽  
Shortwave Radiation ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer

Abstract. Retrieving aerosol optical depth (AOD) from top-of-atmosphere (TOA) satellite-measured radiance requires separating the aerosol signal from the total observed signal. Total TOA radiance includes signal from underlying surface and from atmospheric constituents such as aerosols, clouds and gases. Multispectral retrieval algorithms, such as the dark-target (DT) algorithm that operates upon Moderate Resolution Imaging Spectroradiometer (MODIS, onboard Terra and Aqua satellites) and Visible Infrared Imaging Radiometer Suite (VIIRS, onboard Suomi-NPP) sensors, use wavelength bands in “window” regions. However, while small, the gas absorptions in these bands are non-negligible and require correction. In this paper we use High-resolution TRANsmission (HITRAN) database and Line-by-Line Radiative Transfer Model (LBLRTM) to derive consistent gas corrections for both MODIS and VIIRS wavelength bands. Absorptions from H2O, CO2 and O3 are considered, as well as other trace gases. Even though MODIS and VIIRS bands are “similar”, they are different enough that applying MODIS specific gas corrections to VIIRS observations results in an underestimate of global mean AOD (by 0.01), but with much larger regional AOD biases up to 0.07. As recent studies are attempting to create a long-term data record by joining multiple satellite datasets, including MODIS and VIIRS, the consistency of gas correction becomes even more crucial.

scholarly journals Performance Evaluation of On-Orbit Calibration of SNPP VIIRS Reflective Solar Bands via Intersensor Comparison with Aqua MODIS

2018 ◽  
Vol 35 (2) ◽  
pp. 385-403 ◽  
Author(s):  
Mike Chu ◽  
Junqiang Sun ◽  
Menghua Wang
Keyword(s):  
Selection Criteria ◽  
Infrared Imaging ◽  
Ocean Color ◽  
Sensor Data ◽  
Moderate Resolution ◽  
Key Issues ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Better Than

AbstractAn intersensor comparison is carried out to evaluate the radiometric performance of the reflective solar bands (RSBs) of the first Visible Infrared Imaging Radiometer Suite (VIIRS) on board the Suomi National Polar-Orbiting Partnership (SNPP) satellite. Two versions of sensor data records (SDRs) for moderate-resolution RSBs M1–M8 (410–1238 nm)—one version from the NOAA Ocean Color (OC) Team and the operational version from the Interface Data Processing Segment (IDPS)—are compared against the well-calibrated Moderate Resolution Imaging Spectroradiometer (MODIS) on the Aqua satellite. This comparison fully exploits the moderate resolution of the sensors and a precise simultaneous nadir overpass (SNO) analysis in a “nadir only” approach to achieve a precision better than 1%. The key issues found to impact the SNO analysis are 1) an underlying bias beyond the 80-km spatial scale, 2) a scene-based sporadic variability of about 2% affecting the sample size selection criteria, and 3) large relative deviations at low radiances. It is shown that the OC SDRs achieve significantly better agreement with Aqua MODIS, such as smaller temporal variation, improved agreement in the early mission, and no observable long-term drift. The lone exception is the downward drift of about 1% in the Aqua MODIS band 8 (412 nm) versus SNPP VIIRS band M1 time series that possibly started in late 2013, which is ultimately attributed to errors in Aqua MODIS band 8. Finally, the long-term drift in the IDPS SDRs further illustrates the consequence of the worsening bias within the standard RSB calibration that will infect any versions of the VIIRS SDRs not mitigated for this error.

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