MAKO: a high-performance, airborne imaging spectrometer for the long-wave infrared

Warren, David W.; Boucher, Richard H.; Gutierrez, David J.; Keim, E. R.; Sivjee, Mazaher G.

doi:10.1117/12.861374

Cited by 32 publications

(17 citation statements)

References 6 publications

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“…We found that using the maximum brightness temperature "most hits" method as proposed by Young et al (2002) resulted in pixels consisting of different types of soils in agricultural environments, with emissivities < 0.95 often being included in the fitting procedure. This was verified by comparing these pixels with emissivity information from the ASTER Global Emissivity Database (ASTER GED) at ∼ 100 m spatial resolution (Hulley et al, 2015).…”

Section: In-scene Atmospheric Correction (Isac) Methodsmentioning

confidence: 99%

“…In these cases even small misregistrations between the observed and modeled data near strong absorption lines will amplify instead of reduce the effects of atmospheric attenuation, making correction of the radiance spectrum very difficult. To address these issues, an in-scene atmospheric correction (ISAC) approach was developed for the SEBASS airborne hyperspectral sensor (Young et al, 2002). The main advantage of the ISAC method is that atmospheric correction is accomplished using the hyperspectral data itself without the need for external atmospheric profiles or an RTM.…”

Section: In-scene Atmospheric Correction (Isac) Methodsmentioning

confidence: 99%

“…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%

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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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Section: In-scene Atmospheric Correction (Isac) Methodsmentioning

confidence: 99%

Section: In-scene Atmospheric Correction (Isac) Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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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“…These supporting data were collected on 24 September 2013 by the Mako [37,38] sensor and provided by the Aerospace Corporation (El Segundo, CA, USA). Mako is an airborne hyperspectral imaging system with 128 spectral bands from 7.6 to 13.15 µm.…”

Section: Data Setsmentioning

confidence: 99%

Enhanced Compositional Mapping through Integrated Full-Range Spectral Analysis

McDowell

Kruse

2016

Remote Sensing

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Abstract:We developed a method to enhance compositional mapping from spectral remote sensing through the integration of visible to near infrared (VNIR,~0.4-1 µm), shortwave infrared (SWIR,~1-2.5 µm), and longwave infrared (LWIR,~8-13 µm) data. Spectral information from the individual ranges was first analyzed independently and then the resulting compositional information in the form of image endmembers and apparent abundances was integrated using ISODATA cluster analysis. Independent VNIR, SWIR, and LWIR analyses of a study area near Mountain Pass, California identified image endmembers representing vegetation, manmade materials (e.g., metal, plastic), specific minerals (e.g., calcite, dolomite, hematite, muscovite, gypsum), and general lithology (e.g., sulfate-bearing, carbonate-bearing, and silica-rich units). Integration of these endmembers and their abundances produced a final full-range classification map incorporating much of the variation from all three spectral ranges. The integrated map and its 54 classes provide additional compositional information that is not evident in the VNIR, SWIR, or LWIR data alone, which allows for more complete and accurate compositional mapping. A supplemental examination of hyperspectral LWIR data and comparison with the multispectral LWIR data used in the integration illustrates its potential to further improve this approach.

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“…We use a single image at 34.4 m/pixel that is approximately coincident with the more extensive AVIRIS data set and was collected on the same day, 5/03/2013 (image 1394200_06). The hyperspectral LWIR data is from the Mako sensor 6 and was provided by the Aerospace Corporation. Mako measures radiance in 128 spectral bands from 7.6 to 13.15 μm.…”

Section: Data Descriptionmentioning

confidence: 99%

Integrated visible to near infrared, short wave infrared, and long wave infrared spectral analysis for surface composition mapping near Mountain Pass, California

McDowell

Kruse

2015

SPIE Proceedings

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We have developed new methods for enhanced surface material identification and mapping that integrate visible to near infrared (VNIR, ~0.4 -1 μm), short wave infrared (SWIR, ~1 -2.5 μm), and long wave infrared (LWIR, ~8 -12 μm) multispectral and hyperspectral imagery. This approach produces a single map of surface composition derived from the full spectral range. We applied these methods to a spectrally diverse region around Mountain Pass, CA. A comparison of the integrated results with those obtained from analyzing the spectral ranges individually reveals compositional information not exhibited by the VNIR, SWIR or LWIR data alone. We also evaluate the benefit of hyperspectral rather than multispectral LWIR data for this integrated approach.

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MAKO: a high-performance, airborne imaging spectrometer for the long-wave infrared

Cited by 32 publications

References 6 publications

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

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

Enhanced Compositional Mapping through Integrated Full-Range Spectral Analysis

Integrated visible to near infrared, short wave infrared, and long wave infrared spectral analysis for surface composition mapping near Mountain Pass, California

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