MAGI: A New High-Performance Airborne Thermal-Infrared Imaging Spectrometer for Earth Science Applications

Hall, Jeffrey L.; Boucher, Richard H.; Buckland, Kerry N.; Gutierrez, David J.; Hackwell, J. A.; Johnson, Brian R.; Keim, E. R.; Moreno, Nery M.; Ramsey, Michael S.; Sivjee, Mazaher G.; Tratt, David M.; Warren, David W.; Young, Stephen J.

doi:10.1109/tgrs.2015.2422817

Cited by 22 publications

(10 citation statements)

References 41 publications

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“…Measuring accurate thermal properties of a molten lava surface is also propagation models [15,24]. With the increasing number of spectral bands in mor (e.g., HyTES [20] and the Mineral and Gas Identifier (MAGI) [25]), the radiat emissivity of an object's surface can be extracted with increasing accuracy [26-28 emissivity can then be used with approaches such as linear spectral deconv quantitatively determine possible spectral end-member that defines the minera thermal fractions [29][30][31][32]. Additionally, kinetic temperature (and to a lesser d required to determine the runout distance and hazard potential using radiant h propagation models [15,24].…”

Section: Aster Datamentioning

confidence: 99%

“…Measuring accurate thermal properties of a molten lava surface is also critical to lava flow propagation models [15,24]. With the increasing number of spectral bands in more recent TIR imagers (e.g., HyTES [20] and the Mineral and Gas Identifier (MAGI) [25]), the radiative temperature and emissivity of an object's surface can be extracted with increasing accuracy [26][27][28]. A well-constrained emissivity can then be used with approaches such as linear spectral deconvolution modeling to quantitatively determine possible spectral end-member that defines the mineralogical, textural, and thermal fractions [29][30][31][32].…”

Section: Introductionmentioning

confidence: 99%

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Uncertainty Analysis of Remotely-Acquired Thermal Infrared Data to Extract the Thermal Properties of Active Lava Surfaces

Thompson

Ramsey

2020

Remote Sensing

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Using thermal infrared (TIR) data from multiple instruments and platforms for analysis of an entire active volcanic system is becoming more common with the increasing availability of new data. However, the accuracy and uncertainty associated with these combined datasets are poorly constrained over the full range of eruption temperatures and possible volcanic products. Here, four TIR datasets acquired over active lava surfaces are compared to quantify the uncertainty, accuracy, and variability in derived surface radiance, emissivity, and kinetic temperature. These data were acquired at Kīlauea volcano in Hawai’i, USA, in January/February 2017 and 2018. The analysis reveals that spatial resolution strongly limits the accuracy of the derived surface thermal properties, resulting in values that are significantly below the expected values for molten basaltic lava at its liquidus temperature. The surface radiance is ~2400% underestimated in the orbital data compared to only ~200% in ground-based data. As a result, the surface emissivity is overestimated and the kinetic temperature is underestimated by at least 30% and 200% in the airborne and orbital datasets, respectively. A thermal mixed pixel separation analysis is conducted to extract only the molten fraction within each pixel in an attempt to mitigate this complicating factor. This improved the orbital and airborne surface radiance values to within 15% of the expected values and the derived emissivity and kinetic temperature within 8% and 12%, respectively. It is, therefore, possible to use moderate spatial resolution TIR data to derive accurate and reliable emissivity and kinetic temperatures of a molten lava surface that are comparable to the higher resolution data from airborne and ground-based instruments. This approach, resulting in more accurate kinetic temperature and emissivity of the active surfaces, can improve estimates of flow hazards by greatly improving lava flow propagation models that rely on these data.

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Section: Aster Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Uncertainty Analysis of Remotely-Acquired Thermal Infrared Data to Extract the Thermal Properties of Active Lava Surfaces

Thompson

Ramsey

2020

Remote Sensing

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“…Bradley, et al [5], Kastek, et al [6], Roberts, et al [7], Thorpe, et al [3] and Thorpe, et al [8] successfully mapped CH 4 plumes using shortwave infrared (SWIR: 1.1-3 µm) hyperspectral imagery. Tratt, et al [9], Hall, et al [10], Hulley, et al [1], and Scafutto, et al [2] proved that a more robust and sensitive detection of CH 4 is possible in the longwave infrared (LWIR: 7-14 µm). An analogous investigation using airborne midwave infrared (MWIR: 3-6 µm) sensors is lacking due to the limited data available in this spectral range.…”

Section: Introductionmentioning

confidence: 99%

Detection of Methane Plumes Using Airborne Midwave Infrared (3–5 µm) Hyperspectral Data

Scafutto

Filho

2018

Remote Sensing

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Methane (CH 4 ) display spectral features in several regions of the infrared range (0.75-14 µm), which can be used for the remote mapping of emission sources through the detection of CH 4 plumes from natural seeps and leaks. Applications of hyperspectral remote sensing techniques for the detection of CH 4 in the near and shortwave infrared (NIR-SWIR: 0.75-3 µm) and longwave infrared (LWIR: 7-14 µm) have been demonstrated in the literature with multiple sensors and scenarios. However, the acquisition and processing of hyperspectral data in the midwave infrared (MWIR: 3-5 µm) for this application is rather scarce. Here, a controlled field experiment was used to evaluate the potential for CH 4 plume detection in the MWIR based on hyperspectral data acquired with the SEBASS airborne sensor. For comparison purposes, LWIR data were also acquired simultaneously with the same instrument. The experiment included surface and undersurface emission sources (ground stations), with flow rates ranging between 0.6-40 m 3 /h. The data collected in both ranges were sequentially processed using the same methodology. The CH 4 plume was detected, variably, in both datasets. The gas plume was detected in all LWIR images acquired over nine gas leakage stations. In the MWIR range, the plume was detected in only four stations, wherein 18 m 3 /h was the lowest flux sensed. We demonstrate that the interference of target reflectance, the low contrast between plume and background and a low signal of the CH 4 feature in the MWIR at ambient conditions possibly explain the inferior results observed for this range when compared to LWIR. Furthermore, we show that the acquisition time and weather conditions, including specific limits of temperature, humidity, and wind speed, proved critical for plume detection using daytime MWIR hyperspectral data.

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“…However, one of the biggest drawbacks of these imagers is their limited number of spectral bands defining the TIR region (7.5-12 µm). In response, a number of hyperspectral TIR sensors have been developed, starting with the narrow field-of-view SEBASS (Spatially Enhanced Broadband Array Spectograph System) (Hackwell et al, 1996), and including wide-swath capabilities such as MAKO (Warren et al, 2010), the Mineral and Gas Identifier (MAGI) (Hall et al, 2008(Hall et al, , 2015, AisaOWL (Doneus et al, 2014), SIELETERS (Ferrec et al, 2014), and HyTES (Hook et al, 2013). Table 1 compares the instrument characteristics of each of these six sensors.…”

Section: Introductionmentioning

confidence: 99%

High spatial resolution imaging of methane and other trace gases with the airborne Hyperspectral Thermal Emission Spectrometer (HyTES)

et al. 2016

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Abstract. Currently large uncertainties exist associated with the attribution and quantification of fugitive emissions of criteria pollutants and greenhouse gases such as methane across large regions and key economic sectors. In this study, data from the airborne Hyperspectral Thermal Emission Spectrometer (HyTES) have been used to develop robust and reliable techniques for the detection and wide-area mapping of emission plumes of methane and other atmospheric trace gas species over challenging and diverse environmental conditions with high spatial resolution that permits direct attribution to sources. HyTES is a pushbroom imaging spectrometer with high spectral resolution (256 bands from 7.5 to 12 µm), wide swath (1-2 km), and high spatial resolution (∼ 2 m at 1 km altitude) that incorporates new thermal infrared (TIR) remote sensing technologies. In this study we introduce a hybrid clutter matched filter (CMF) and plume dilation algorithm applied to HyTES observations to efficiently detect and characterize the spatial structures of individual plumes of CH 4 , H 2 S, NH 3 , NO 2 , and SO 2 emitters. The sensitivity and field of regard of HyTES allows rapid and frequent airborne surveys of large areas including facilities not readily accessible from the surface. The HyTES CMF algorithm produces plume intensity images of methane and other gases from strong emission sources. The combination of high spatial resolution and multi-species imaging capability provides source attribution in complex environments. The CMF-based detection of strong emission sources over large areas is a fast and powerful tool needed to focus on more computationally intensive retrieval algorithms to quantify emissions with error estimates, and is useful for expediting mitigation efforts and addressing critical science questions.

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MAGI: A New High-Performance Airborne Thermal-Infrared Imaging Spectrometer for Earth Science Applications

Cited by 22 publications

References 41 publications

Uncertainty Analysis of Remotely-Acquired Thermal Infrared Data to Extract the Thermal Properties of Active Lava Surfaces

Uncertainty Analysis of Remotely-Acquired Thermal Infrared Data to Extract the Thermal Properties of Active Lava Surfaces

Detection of Methane Plumes Using Airborne Midwave Infrared (3–5 µm) Hyperspectral Data

High spatial resolution imaging of methane and other trace gases with the airborne Hyperspectral Thermal Emission Spectrometer (HyTES)

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