Persistent Hot Spot Detection and Characterisation Using SLSTR

Gas flaring is a disposal process widely used in the oil extraction and processing industry. It consists in the burning of unwanted gas at the tip of a stack and due to its thermal characteristic and the thermal emission it is possible to observe and to quantify it from space. Spaceborne observations allows us to collect information across regions and hence to provide a base for estimation of emissions on global scale. We have successfully adapted the Visible Infrared Imaging Radiometer Suite (VIIRS) Nightfire algorithm for the detection and characterisation of persistent hot spots, including gas flares, to the Sea and Land Surface Temperature Radiometer (SLSTR) observations on-board the Sentinel-3 satellites. A hot event at temperatures typical of a gas flare will produce a local maximum in the night-time readings of the shortwave and mid-infrared (SWIR and MIR) channels of SLSTR. The SWIR band centered at 1.61 μm is closest to the expected spectral radiance maximum and serves as the primary detection band. The hot source is characterised in terms of temperature and area by fitting the sum of two Planck curves, one for the hot source and another for the background, to the radiances from all the available SWIR, MIR and thermal infra-red channels of SLSTR. The flaring radiative power is calculated from the gas flare temperature and area. Our algorithm differs from the original VIIRS Nightfire algorithm in three key aspects: (1) It uses a granule-based contextual thresholding to detect hot pixels, being independent of the number of hot sources present and their intensity. (2) It analyses entire clusters of hot source detections instead of individual pixels. This is arguably a more comprehensive use of the available information. (3) The co-registration errors between hot source clusters in the different spectral bands are calculated and corrected. This also contributes to the SLSTR instrument validation. Cross-comparisons of the new gas flare characterisation with temporally close observations by the higher resolution German FireBIRD TET-1 small satellite and with the Nightfire product based on VIIRS on-board the Suomi-NPP satellite show general agreement for an individual flaring site in Siberia and for several flaring regions around the world. Small systematic differences to VIIRS Nightfire are nevertheless apparent. Based on the hot spot characterisation, gas flares can be identified and flared gas volumes and pollutant emissions can be calculated with previously published methods.

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Persistent Hot Spot Detection and Characterisation Using SLSTR

10.20944/preprints201805.0020.v2 ◽

2018 ◽

Author(s):

Alexandre Caseiro ◽

Gernot Ruecker ◽

Joachim Tiemann ◽

David Leimbach ◽

Eckehard Lorenz ◽

...

Keyword(s):

Land Surface ◽

Infrared Imaging ◽

Thermal Emission ◽

Hot Spot ◽

Thermal Characteristic ◽

Local Maximum ◽

Global Scale ◽

Pollutant Emissions ◽

Night Time ◽

Gas Flares

Gas flaring is a disposal process widely used in the oil extraction and processing industry. It consists in the burning of unwanted gas at the tip of a stack and due to its thermal characteristic and the thermal emission it is possible to observe and to quantify it from space. Spaceborne observations allows us to collect information across regions and hence to provide a base for estimation of emissions on global scale. We have successfully adapted the Visible Infrared Imaging Radiometer Suite (VIIRS) Nightfire algorithm for the detection and characterisation of persistent hot spots, including gas flares, to the Sea and Land Surface Temperature Radiometer (SLSTR) observations on-board the Sentinel- satellites. A hot event at temperatures typical of a gas flare will produce a local maximum in the night-time readings of the shortwave and mid-infrared (SWIR and MIR) channels of SLSTR. The SWIR band centered at 1.61 mmis closest to the expected spectral radiancemaximumand serves as the primary detection band. The hot source is characterised in terms of temperature and area by fitting the sum of two Planck curves, one for the hot source and another for the background, to the radiances from all the available SWIR, MIR and thermal infra-red channels of SLSTR. The flaring radiative power is calculated from the gas flare temperature and area. Our algorithm differs from the original VIIRS Nightfire algorithm in three key aspects: (1) It uses a granule-based contextual thresholding to detect hot pixels, being independent of the number of hot sources present and their intensity. (2) It analyses entire clusters of hot source detections instead of individual pixels. This is arguably a more comprehensive use of the available information. (3) The co-registration errors between hot source clusters in the different spectral bands are calculated and corrected. This also contributes to the SLSTR instrument validation. Cross-comparisons of the new gas flare characterisation with temporally close observations by the higher resolution German FireBIRD TET-1 small satellite and with the Nightfire product based on VIIRS on-board the Suomi-NPP satellite show general agreement for an individual flaring site in Siberia and for several flaring regions around the world. Small systematic differences to VIIRS Nightfire are nevertheless apparent. Based on the hot spot characterisation, gas flares can be identified and flared gas volumes and pollutant emissions can be calculated with previously published methods.

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A Methodology for Gas Flaring Detection and Characterisation Using SLSTR

10.20944/preprints201805.0020.v1 ◽

2018 ◽

Author(s):

Alexandre Caseiro ◽

Gernot Rücker ◽

Joachim Tiemann ◽

David Leimbach ◽

Eckehard Lorenz ◽

...

Keyword(s):

Local Maximum ◽

Spectral Radiance ◽

Night Time ◽

Gas Flaring ◽

Registration Errors ◽

Spectral Bands ◽

Swir Band ◽

Radiative Power ◽

Key Aspects ◽

Gas Volume

Gas flaring is a disposal process widely used in the oil extraction and processing industry. It consists in the burning of unwanted gas at the tip of a stack. We have successfully adapted the VIIRS Nightfire algorithm for the detection and characterisation of gas flares to SLSTR observations on-board the Sentinel-3 satellites. A hot event at temperatures typical of a gas flare will produce a local maximum in the night-time readings of the shortwave and mid-infrared (SWIR and MIR) channels of SLSTR. The SWIR band centered at 1.61 μm is closest to the expected spectral radiance maximum and serves as the primary detection band. The hot source is characterised in terms of temperature and area by fitting the sum of two Planck curves, one for the hot source and another for the background, to the radiances from all the available SWIR, MIR and thermal infra-red channels of SLSTR. The flaring radiative power is calculated from the gas flare temperature and area. Our algorithm differs from the original VIIRS Nightfire algorithm in three key aspects: (1) It uses a granule-based contextual thresholding to detect hot pixels, being independent of the number of hot sources present and their intensity. (2) It analyses entire clusters of hot source detections instead of individual pixels. This is arguably a more comprehensive use of the available information. (3) The co-registration errors between hot source clusters in the different spectral bands are calculated and corrected. This also contributes to the SLSTR instrument validation. Cross-comparisons of the new gas flare characterisation with temporally close observations by the higher resolution German FireBIRD TET-1 small satellite and with the Nightfire product based on VIIRS on-board the Suomi-NPP satellite show general agreement for an individual flaring site in Siberia and for several flaring regions around the world. Small systematic differences to VIIRS Nightfire are nevertheless apparent. The retrieved flaring radiative power can be used to calculate flared gas volumes when calibrated against reported flared gas volumes. The estimated flared gas volume can be further combined with published emission factors in order to compute emissions of atmospheric trace constituents like carbon dioxide and black carbon.

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Gas flaring activity and black carbon emissions in 2017 derived from Sentinel-3A SLSTR

10.5194/essd-2019-99 ◽

2019 ◽

Cited By ~ 4

Author(s):

Alexandre Caseiro ◽

Berit Gehrke ◽

Gernot Rücker ◽

David Leimbach ◽

Johannes W. Kaiser

Keyword(s):

Black Carbon ◽

Carbon Emissions ◽

Land Surface ◽

Hot Spot ◽

Data Availability ◽

Atmospheric Pollutants ◽

Atmospheric Composition ◽

Data Set ◽

Gas Flaring ◽

Gas Flares

Abstract. Gas flares are a regionally and globally significant source of atmospheric pollutants. They can be detected by satellite remote sensing. We calculate the global flared gas volume and black carbon emissions in 2017 by (1) applying a previously developed hot spot detection and characterisation algorithm to all observations of the Sea and Land Surface Temperature Radiometer (SLSTR) instrument on-board the Copernicus satellite Sentinel-3A in 2017 and (2) applying newly developed filters for identifying gas flares and corrections for calculating flared gas volumes (Billion Cubic Meters, BCM) and black carbon emission estimates. The filter to discriminate gas flares from other hot spots combines the unique flaring characteristics in terms of persistence and temperature. The comparison of our results with those of the Visible Infrared Imaging Radiometer Suite (VIIRS) nightfire data set indicates a good fit between the two methods. The calculation of black carbon emissions using our gas flaring data set and published emission factors show good agreement with recently published black carbon inventories. The data presented here can therefore be used e.g. in atmospheric dispersion simulations. The advantage of using our algorithm with Sentinel-3A data lies in the previously demonstrated ability to detect and quantify small flares and the foreseen long term data availability from the Copernicus program. Our data (GFlaringS3, flaring activity and the related black carbon emissions) are available on the Emissions of atmospheric Compounds and Compilation of Ancillary Data (ECCAD) web site (https://eccad3.sedoo.fr/#GFlaringS3, DOI https://doi.org/10.25326/19 (Caseiro and Kaiser, 2019)) for use in, e.g., atmospheric composition modelling studies.

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Gas flaring activity and black carbon emissions in 2017 derived from the Sentinel-3A Sea and Land Surface Temperature Radiometer

Earth System Science Data ◽

10.5194/essd-12-2137-2020 ◽

2020 ◽

Vol 12 (3) ◽

pp. 2137-2155

Author(s):

Alexandre Caseiro ◽

Berit Gehrke ◽

Gernot Rücker ◽

David Leimbach ◽

Johannes W. Kaiser

Keyword(s):

Confidence Interval ◽

Surface Temperature ◽

Black Carbon ◽

Land Surface Temperature ◽

Carbon Emissions ◽

Land Surface ◽

Hot Spot ◽

Data Set ◽

Gas Flares ◽

Gas Volume

Abstract. Gas flares are a regionally and globally significant source of atmospheric pollutants. They can be detected by satellite remote sensing. We calculate the global flared gas volume and black carbon emissions in 2017 by applying (1) a previously developed hot spot detection and characterisation algorithm to all observations of the Sea and Land Surface Temperature Radiometer (SLSTR) instrument on board the Copernicus satellite Sentinel-3A and (2) newly developed filters for identifying gas flares and corrections for calculating both flared gas volumes (billion cubic metres, BCM) and black carbon (BC) emissions (g). The filter to discriminate gas flares from other hot spots uses the observed hot spot characteristics in terms of temperature and persistence. A regression function is used to correct for the variability of detection opportunities. A total of 6232 flaring sites are identified worldwide. The best estimates of the annual flared gas volume and the BC emissions are 129 BCM with a confidence interval of [35, 419 BCM] and 73 Gg with a confidence interval of [20, 239 Gg], respectively. Comparison of our activity (i.e. BCM) results with those of the Visible Infrared Imaging Radiometer Suite (VIIRS) Nightfire data set and SWIR-based calculations show general agreement but distinct differences in several details. The calculation of black carbon emissions using our gas flaring data set with a newly developed dynamic assignment of emission factors lie in the range of recently published black carbon inventories, albeit towards the lower end. The data presented here can therefore be used e.g. in atmospheric dispersion simulations. The advantage of using our algorithm with Sentinel-3 data lies in the previously demonstrated ability to detect and quantify small flares, the long-term data availability from the Copernicus programme, and the increased detection opportunity of global gas flare monitoring when used in conjunction with the VIIRS instruments. The flaring activity and related black carbon emissions are available as “GFlaringS3” on the Emissions of atmospheric Compounds and Compilation of Ancillary Data (ECCAD) website (https://doi.org/10.25326/19, Caseiro and Kaiser, 2019).

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The Potential of Moonlight Remote Sensing: A Systematic Assessment with Multi-Source Nightlight Remote Sensing Data

Remote Sensing ◽

10.3390/rs13224639 ◽

2021 ◽

Vol 13 (22) ◽

pp. 4639

Author(s):

Di Liu ◽

Qingling Zhang ◽

Jiao Wang ◽

Yifang Wang ◽

Yanyun Shen ◽

...

Keyword(s):

Remote Sensing ◽

Land Surface ◽

Infrared Imaging ◽

Space Station ◽

Remote Sensing Data ◽

Optical Remote Sensing ◽

Night Time ◽

Systematic Assessment ◽

Sensing Applications ◽

Satellite Sensors

One recent trend in optical remote sensing is to increase observation frequencies. However, there are still challenges on the night side when sunlight is not available. Due to their powerful capabilities in low-light sensing, nightlight satellite sensors have been deployed to capture nightscapes of Earth from space, observing anthropomorphic and natural activities at night. To date, the mainstream of nightlight remote sensing applications has mainly focused on artificial lights, especially within cities or self-luminous bodies, such as fisheries, oil, offshore rigs, etc. Observations taken under moonlight are often discarded or corrected to reduce lunar effects. Some researchers have discussed the possibility of using moonlight as a useful illuminating source at night for the detection of nocturnal features on Earth, but no quantitative analysis has been reported so far. This study aims to systematically evaluate the potential of moonlight remote sensing with mono-spectral Visible Infrared Imaging Radiometer Suite/Day-Night-Band (VIIRS/DNB) imagery and multi-spectral photos taken by astronauts from the International Space Station (ISS), as well as unmanned aerial vehicle (UAV) night-time imagery. Using the VIIRS/DNB, ISS and UAV moonlight images, the possibilities of the moonlight remote sensing were first discussed. Then, the VIIRS/DNB, ISS, UAV images were classified over different non-self-lighting land surfaces to explore the potential of moonlight remote sensing. The overall accuracies (OA) and kappa coefficients are 79.80% and 0.45, 87.16% and 0.77, 91.49% and 0.85, respectively, indicating a capability to characterize land surface that is very similar to daytime remote sensing. Finally, the characteristics of current moonlight remote sensing are discussed in terms of bands, spatial resolutions, and sensors. The results confirm that moonlight remote sensing has huge potential for Earth observation, which will be of great importance to significantly increase the temporal coverage of optical remote sensing during the whole diurnal cycle. Based on these discussions, we further examined requirements for next-generation nightlight remote sensing satellite sensors.

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Spatial Hotspot Analysis of Bucharest’s Urban Heat Island (UHI) Using Modis Data

Annals of Valahia University of Targoviste Geographical Series ◽

10.2478/avutgs-2018-0002 ◽

2018 ◽

Vol 18 (1) ◽

pp. 14-22 ◽

Cited By ~ 1

Author(s):

Georgiana Grigoraș ◽

Bogdan Urițescu

Keyword(s):

Surface Temperature ◽

Air Temperature ◽

Land Surface Temperature ◽

Hot Spots ◽

Land Surface ◽

Hot Spot ◽

Monitoring Network ◽

Residential Areas ◽

Hotspot Analysis ◽

The City

Abstract The aim of the study is to find the relationship between the land surface temperature and air temperature and to determine the hot spots in the urban area of Bucharest, the capital of Romania. The analysis was based on images from both moderate-resolution imaging spectroradiometer (MODIS), located on both Terra and Aqua platforms, as well as on data recorded by the four automatic weather stations existing in the endowment of The National Air Quality Monitoring Network, from the summer of 2017. Correlation coefficients between land surface temperature and air temperature were higher at night (0.8-0.87) and slightly lower during the day (0.71-0.77). After the validation of satellite data with in-situ temperature measurements, the hot spots in the metropolitan area of Bucharest were identified using Getis-Ord spatial statistics analysis. It has been achieved that the “very hot” areas are grouped in the center of the city and along the main traffic streets and dense residential areas. During the day the "very hot spots” represent 33.2% of the city's surface, and during the night 31.6%. The area where the mentioned spots persist, falls into the "very hot spot" category both day and night, it represents 27.1% of the city’s surface and it is mainly represented by the city center.

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

Remote Sensing ◽

10.3390/rs13091627 ◽

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.

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Validation of the Meteosat storm detection and nowcasting system Cb-TRAM with lightning network data – Europe and South Africa

Atmospheric Measurement Techniques ◽

10.5194/amt-6-1567-2013 ◽

2013 ◽

Vol 6 (6) ◽

pp. 1567-1583 ◽

Cited By ~ 17

Author(s):

T. Zinner ◽

C. Forster ◽

E. de Coning ◽

H.-D. Betz

Keyword(s):

South Africa ◽

South African ◽

Thermal Emission ◽

Climatic Conditions ◽

Probability Of Detection ◽

Night Time ◽

Detection Scheme ◽

Lightning Detection ◽

Physical Limitations ◽

Cloud Tops

Abstract. In this paper, recent changes to the Meteosat thunderstorm TRacking And Monitoring algorithm (Cb-TRAM) are presented as well as a validation of Cb-TRAM against data from the European ground-based LIghtning NETwork (LINET) of Nowcast GmbH and the South African Weather Service Lightning Detection Network (SAWS LDN). Validation is conducted along the well-known skill measures probability of detection (POD) and false alarm ratio (FAR) on the basis of Meteosat/SEVIRI pixels as well as on the basis of thunderstorm objects. The values obtained demonstrate specific limitations of Cb-TRAM, as well as limitations of satellite methods in general which are based on thermal emission and solar reflectivity information from thunderstorm cloud tops. Although the climatic conditions and the occurrence of thunderstorms are quite different for Europe and South Africa, quality score values are similar. Our conclusion is that Cb-TRAM provides robust results of well-defined quality for very different climatic regimes. The POD for a thunderstorm with intense lightning is about 80% during the day. The FAR for a Cb-TRAM detection which is not even close to intense lightning is about 50%. If only proximity to any lightning activity is required, FAR is much lower at about 15%. Pixel-based analysis shows that detected thunderstorm object size is not indiscriminately large, but well within physical limitations of the satellite method. Night-time POD and FAR are somewhat worse as the detection scheme does not use the high-resolution visible information during night-time hours. Nowcasting scores show useful values up to approximately 30 min in advance.

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Monitoring of the 2015 Villarrica Volcano Eruption by Means of DLR’s Experimental TET-1 Satellite

Remote Sensing ◽

10.3390/rs10091379 ◽

2018 ◽

Vol 10 (9) ◽

pp. 1379 ◽

Cited By ~ 5

Author(s):

Simon Plank ◽

Michael Nolde ◽

Rudolf Richter ◽

Christian Fischer ◽

Sandro Martinis ◽

...

Keyword(s):

Infrared Imaging ◽

Thermal Emission ◽

Atmospheric Correction ◽

Landsat 8 ◽

Thermal Anomalies ◽

Villarrica Volcano ◽

Radiant Power ◽

Moderate Resolution Imaging Spectroradiometer ◽

German Aerospace ◽

Active Volcanoes

Villarrica Volcano is one of the most active volcanoes in the South Andes Volcanic Zone. This article presents the results of a monitoring of the time before and after the 3 March 2015 eruption by analyzing nine satellite images acquired by the Technology Experiment Carrier-1 (TET-1), a small experimental German Aerospace Center (DLR) satellite. An atmospheric correction of the TET-1 data is presented, based on the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Emissivity Database (GDEM) and Moderate Resolution Imaging Spectroradiometer (MODIS) water vapor data with the shortest temporal baseline to the TET-1 acquisitions. Next, the temperature, area coverage, and radiant power of the detected thermal hotspots were derived at subpixel level and compared with observations derived from MODIS and Visible Infrared Imaging Radiometer Suite (VIIRS) data. Thermal anomalies were detected nine days before the eruption. After the decrease of the radiant power following the 3 March 2015 eruption, a stronger increase of the radiant power was observed on 25 April 2015. In addition, we show that the eruption-related ash coverage of the glacier at Villarrica Volcano could clearly be detected in TET-1 imagery. Landsat-8 imagery was analyzed for comparison. The information extracted from the TET-1 thermal data is thought be used in future to support and complement ground-based observations of active volcanoes.

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Use of multi-spectral visible and near-infrared satellite data for timely estimates of the Earth's surface reflectance in cloudy conditions: Part 2 - image restoration with HICO satellite data in overcast conditions

10.31223/x5ww4z ◽

2021 ◽

Author(s):

Joanna Joiner ◽

Zachary Fasnacht ◽

Bo-Cai Gao ◽

Wenhan Qin

Keyword(s):

Spatial Resolution ◽

Satellite Data ◽

Precision Agriculture ◽

Land Surface ◽

Near Infrared ◽

Infrared Imaging ◽

Space Station ◽

Water Quality Assessment ◽

Low Contrast

Satellite-based visible and near-infrared imaging of the Earth's surface is generally not performed in moderate to highly cloudy conditions; images that look visibly cloud covered to the human eye are typically discarded. Here, we expand upon previous work that employed machine learning (ML) to estimate underlying land surface reflectances at red, green, and blue (RGB) wavelengths in cloud contaminated spectra using a low spatial resolution satellite spectrometer. Specifically, we apply the ML methodology to a case study at much higher spatial resolution with the Hyperspectral Imager for the Coastal Ocean (HICO) that flew on the International Space Station (ISS). HICO spatial sampling is of the order of 90 m. The purpose of our case study is to test whether high spatial resolution features can be captured using multi-spectral imaging in lightly cloudy and overcast conditions. We selected one clear and one cloudy image over a portion ofthe panhandle coastline of Florida to demonstrate that land features are partially recoverable in overcast conditions. Many high contrast features are well recovered in the presence of optically thin clouds. However, some of the low contrast features, such as narrow roads, are smeared out in the heavily clouded part of the reconstructed image. This case study demonstrates that our approach may be useful for many science and applications that are being developed for current and upcoming satellite missions including precision agriculture and natural vegetation analysis, water quality assessment as well as disturbance, change, hazard, and disaster detection.

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