Dr. Matthew Montanaro Profile

Dr. Matthew Montanaro

Calibration Scientist at NASA Goddard Space Flight Ctr.

SPIE Involvement:

Author

Publications (33)

Proceedings Article | 20 October 2023 Presentation + Paper

Thermal band observations of the May 2022 total lunar eclipse by the Landsat thermal infrared sensors

Matthew Montanaro, Dennis Reuter, Allen Lunsford, James Briscoe

Proceedings Volume 12686, 1268602 (2023) https://doi.org/10.1117/12.2682931

KEYWORDS: Landsat, Equipment, Calibration, Space operations, Observatories, Image processing, Detector arrays, Shadows, Thermography, Infrared sensors

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Proceedings Article | 4 October 2023 Presentation + Paper

Landsat 8 decade of on-orbit operation summary of events and current state

Raviv Levy, Jeffery Miller, Julia Barsi, Kurtis Thome, Edward Kaita, Matthew Montanaro, Brian Markham

Proceedings Volume 12685, 126850Q (2023) https://doi.org/10.1117/12.2678051

KEYWORDS: Calibration, Landsat, Space operations, Diffusers, Spectral response, Reflectivity, Lamps

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Proceedings Article | 30 September 2022 Presentation + Paper

Early radiometric performance of Landsat-9 Thermal Infrared Sensor

Julia Barsi, Matthew Montanaro, Kurtis Thome, Nina Raqueno, Simon Hook, Cody Anderson, Esad Micijevic

Proceedings Volume 12232, 122320U (2022) https://doi.org/10.1117/12.2634058

KEYWORDS: Landsat, Earth observing sensors, Calibration, Stray light, Black bodies

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Proceedings Article | 19 August 2021 Presentation + Paper

Landsat-8 TIRS radiometric calibration status

Julia Barsi, Brian Markham, Matthew Montanaro, Simon Hook, Nina Raqueno, Esad Micijevic, Kerry Cawse-Nicholson

Proceedings Volume 11829, 118290F (2021) https://doi.org/10.1117/12.2595527

KEYWORDS: Calibration, Black bodies, Landsat, Earth observing sensors, Sensors, Data acquisition, Contamination

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Proceedings Article | 16 September 2020 Presentation + Paper

Landsat-8 TIRS thermal radiometric calibration status

Julia Barsi, Brian Markham, Matthew Montanaro, Simon Hook, Nina Raqueno, Jeffrey Miller, Rasa Willette

Proceedings Volume 11501, 115010L (2020) https://doi.org/10.1117/12.2567807

KEYWORDS: Calibration, Earth observing sensors, Landsat, Error analysis, Sensors, Stray light, Infrared sensors, Infrared imaging, Thermography, Infrared radiation

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The Landsat-8 Thermal Infrared Sensor (TIRS) has been acquiring two-band thermal infrared images of the Earth’s surface since 2013. The calibration of the two-band system has been monitored using the on-board calibrator and validated with vicarious calibration performed by NASA/JPL and RIT since launch. Soon after launch, it was discovered that the instrument had a significant stray light effect that was affecting the radiometric calibration. The stray light was corrected in the processing system in 2017. Since then, it has become apparent that there was an additional radiometric error, based on the vicarious calibration results. With a failure within the primary electronic system and subsequent switch to the redundant electronic system, the TIRS instrument effectively has two separate calibration regimes. The vicarious calibration found a statistically significant calibration error, primarily a constant over time, in Band 11 on the primary electronics (Feb 11, 2013 through March 5, 2015) of about -0.6K at 300K. The calibration error in Band 10 was smaller though still statistically significant at about 0.2K at 300K. On the redundant side (March 5, 2015 to present), the calibration error is more signal dependent than time dependent. Both bands are affected, with Band 10 having an error between 1K and -0.4K (between 273-320K) and Band 11 having an error between 0.8K and -1.44K (between 273-320K). This calibration error will be corrected within the USGS Landsat Product Generation System with the release of Landsat Collection-2 products. The Collection-2 release also includes a correction to the relative radiometric calibration of TIRS data. Striping as a result of poor detector-to-detector normalization has been increasing in the imagery since launch. The TIRS relative radiometric calibration will be updated based on internal calibrator data to remove the stripes on a quarterly basis. The visible stripes are generally at 0.1-0.2% level, though there are some detectors in each band that have changed by 1% or more. The Collection-2 release will result in much more uniform TIRS images.

Proceedings Article | 28 August 2020 Presentation + Paper

Landsat 9 mission update and status

Brian Markham, Del Jenstrom, Brian Sauer, Steven Pszcolka, Vicki Dulski, Jason Hair, Joel McCorkel, Geir Kvaran, Kurtis Thome, Matthew Montanaro, Jeffrey Pedelty, Cody Anderson, Michael Choate, Julia Barsi, Edward Kaita, Jeffrey Miller

Proceedings Volume 11501, 115010O (2020) https://doi.org/10.1117/12.2569748

KEYWORDS: Landsat, Earth observing sensors, Space operations, Stray light, Sensors, Signal to noise ratio, Observatories, Short wave infrared radiation, Calibration

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Proceedings Article | 23 October 2018 Paper

Landsat-8 on-orbit and Landsat-9 pre-launch sensor radiometric characterization

Brian Markham, Julia Barsi, Matthew Montanaro, Joel McCorkel, Aaron Gerace, Jeffrey Pedelty, Simon Hook, Nina Raqueno, Cody Anderson, Md Obaidul Haque

Proceedings Volume 10781, 1078104 (2018) https://doi.org/10.1117/12.2324715

KEYWORDS: Sensors, Earth observing sensors, Landsat, Stray light, Calibration, Lamps, Signal to noise ratio, Infrared sensors, Electronics, Diffusers

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Landsat-8 has been operating on-orbit for 5+ years. Its two sensors, the Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS), are continuing to produce high quality data. The OLI has been radiometrically stable at the better than 0.3% level on a band average basis for all but the shortest wavelength (443 nm) band, which has degraded about 1.3% since launch. All on-board calibration devices continue to perform well and consistently. No gaps in across track coverage exist as 100% operability of the detectors is maintained. The variability over time of detector responsivity within a band relative to the average is better than 0.05% (1 sigma), though there are occasional detectors that jump up to 1.5% in response in the Short-Wave InfraRed (SWIR) bands. Signal-to-Noise performance continues at 2-3x better than requirements, with a small degradation in the 443 nm band commensurate with the loss in sensitivity. Pre-launch error analysis, combined with the stability of the OLI indicates that the absolute reflectance calibration uncertainty is better than 3%; comparisons to ground measurements and comparisons to other sensors are consistent with this. The Landsat-8 TIRS is similarly radiometrically stable, showing changes of at most 0.3% over the mission. The uncertainty in the absolute calibration as well as the detector to detector variability are largely driven by the stray light response of TIRS. The current processing corrects most of the stray light effects, resulting in absolute uncertainties of ~1% and reduced striping. Efforts continue to further reduce the striping. Noise equivalent delta temperature is about 50 mK at typical temperatures and 100% detector operability is maintained. Landsat-9 is currently under development with a launch no earlier than December 2020. The nearly identical OLI-2 and upgraded TIRS-2 sensors have completed integration and are in the process of instrument level performance characterization including spectral, spatial, radiometric and geometric testing. Component and assembly level measurements of the OLI-2, which include spectral response, radiometric response and stray light indicate comparable performance to OLI. The first functional tests occurred in July 2018 and spatial performance testing in vacuum is scheduled for August 2018. Similarly, for TIRS-2, partially integrated instrument level testing indicated spectral and spatial responses comparable to TIRS, with stray light reduced by approximately an order of magnitude from TIRS.

Proceedings Article | 7 September 2018 Presentation + Paper

Landsat 9 Thermal Infrared Sensor 2 pre-launch characterization: initial imaging and spectral performance results

Aaron Pearlman, Joel McCorkel, Matthew Montanaro, Boryana Efremova, Brian Wenny, Allen Lunsford, Amy Simon, Jason Hair, Dennis Reuter

Proceedings Volume 10764, 1076406 (2018) https://doi.org/10.1117/12.2321084

KEYWORDS: Stray light, Signal detection, Sensors, Earth observing sensors, Landsat, Calibration, Infrared sensors, Spectral calibration, Spatial analysis, Image resolution

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SPIE Journal Paper | 27 October 2017 Open Access

Leveraging intercalibration techniques to support stray-light removal from Landsat 8 Thermal Infrared Sensor data

Aaron Gerace, Matthew Montanaro, Ryan Connal

JARS, Vol. 12, Issue 01, 012007, (October 2017) https://doi.org/10.1117/12.10.1117/1.JRS.12.012007

KEYWORDS: Sensors, Earth observing sensors, Landsat, MODIS, Algorithm development, Atmospheric modeling, Data acquisition, Calibration, Detection and tracking algorithms, Image sensors

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SPIE Journal Paper | 6 October 2017 Open Access

Development of a simulation environment to support intercalibration studies over the Algodones Dunes system

Rehman Eon, Aaron Gerace, Matthew Montanaro, Brittany Ambeau, Joel McCorkel

JARS, Vol. 12, Issue 01, 012008, (October 2017) https://doi.org/10.1117/12.10.1117/1.JRS.12.012008

KEYWORDS: Sensors, MODIS, Earth observing sensors, Landsat, Calibration, Satellites, Bidirectional reflectance transmission function, Atmospheric modeling, RGB color model, Environmental sensing

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Proceedings Article | 5 September 2017 Paper

Landsat-8 TIRS thermal radiometric calibration status

Julia Barsi, Brian Markham, Matthew Montanaro, Aaron Gerace, Simon Hook, John Schott, Nina Raqueno, Ron Morfitt

Proceedings Volume 10402, 104021G (2017) https://doi.org/10.1117/12.2276045

KEYWORDS: Calibration, Earth observing sensors, Landsat, Imaging systems, Satellites, Sensors, Stray light, Algorithm development, Radiometry, Infrared sensors

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Proceedings Article | 3 May 2017 Presentation + Paper

Estimating top-of-atmosphere thermal infrared radiance using MERRA-2 atmospheric data

Tania Kleynhans, Matthew Montanaro, Aaron Gerace, Christopher Kanan

Proceedings Volume 10178, 101780R (2017) https://doi.org/10.1117/12.2262571

KEYWORDS: Data modeling, MODIS, Thermal modeling, Temperature metrology, Skin, Directed energy weapons, Atmospheric modeling, Infrared imaging, Infrared radiation, Clouds, Earth's atmosphere, Infrared sensors, Cloud physics

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Proceedings Article | 19 September 2016 Presentation + Paper

The characterization of a DIRSIG simulation environment to support the inter-calibration of spaceborne sensors

Brittany Ambeau, Aaron Gerace, Matthew Montanaro, Joel McCorkel

Proceedings Volume 9972, 99720M (2016) https://doi.org/10.1117/12.2238317

KEYWORDS: Remote sensing, Landsat, Digital imaging, MODIS, Bidirectional reflectance transmission function, Sensors, Earth observing sensors, Backscatter, Data modeling, Image sensors

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Proceedings Article | 19 September 2016 Presentation + Paper

Performance of the proposed stray light correction algorithm for the Thermal Infrared Sensor (TIRS) onboard Landsat 8

Matthew Montanaro, Aaron Gerace

Proceedings Volume 9972, 99720F (2016) https://doi.org/10.1117/12.2238554

KEYWORDS: Stray light, Signal detection, Landsat, Calibration, Infrared sensors, Sensors, Detection and tracking algorithms, Earth observing sensors, MODIS, Scattering

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Proceedings Article | 19 September 2016 Presentation + Paper

Landsat 9: status and plans

Brian Markham, Del Jenstrom, Jeffrey Masek, Phil Dabney, Jeffrey Pedelty, Julia Barsi, Matthew Montanaro

Proceedings Volume 9972, 99720G (2016) https://doi.org/10.1117/12.2238658

KEYWORDS: Landsat, Sensors, Imaging systems, Earth observing sensors, Aerospace engineering, Computer programming, Staring arrays, Electronics, Calibration, Signal to noise ratio

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Proceedings Article | 17 May 2016 Paper

Calibration of UAS imagery inside and outside of shadows for improved vegetation index computation

Elizabeth Bondi, Carl Salvaggio, Matthew Montanaro, Aaron Gerace

Proceedings Volume 9866, 98660J (2016) https://doi.org/10.1117/12.2227214

KEYWORDS: Vegetation, Calibration, Reflectivity, Clouds, Satellite imaging, Satellites, Sensors, Spatial resolution, Calibration, Reflectivity, Digital imaging, Infrared radiation

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Proceedings Article | 12 October 2015 Paper

Landsat 8: status and on-orbit performance

Brian Markham, Julia Barsi, Ron Morfitt, Mike Choate, Matthew Montanaro, Terry Arvidson, James Irons

Proceedings Volume 9639, 963908 (2015) https://doi.org/10.1117/12.2194905

KEYWORDS: Earth observing sensors, Landsat, Sensors, Calibration, Lamps, Data acquisition, Stray light, Diffusers, Signal to noise ratio, Computer programming

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Proceedings Article | 8 September 2015 Paper

Requirement sensitivity studies for a future Landsat sensor

Zhaoyu Cui, Matthew Montanaro, Aaron Gerace, John Schott, Brian Markham

Proceedings Volume 9607, 96070S (2015) https://doi.org/10.1117/12.2188482

KEYWORDS: Landsat, Sensors, Modulation transfer functions, Remote sensing, Tolerancing, Manufacturing, Imaging systems, Satellites, Point spread functions, Earth observing sensors

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Proceedings Article | 8 September 2015 Paper

TIRS stray light correction: algorithms and performance

Aaron Gerace, Matthew Montanaro, Tim Beckmann, Kaitlin Tyrrell, Alexandra Cozzo, Trevor Carney, Vicki Ngan

Proceedings Volume 9607, 96070Q (2015) https://doi.org/10.1117/12.2188862

KEYWORDS: Stray light, Calibration, Landsat, MODIS, Sensors, Detection and tracking algorithms, Data modeling, Infrared sensors, Image enhancement, Earth observing sensors

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Proceedings Article | 21 May 2015 Paper

The development of a DIRSIG simulation environment to support instrument trade studies for the SOLARIS sensor

Aaron Gerace, Adam Goodenough, Matthew Montanaro, Jie Yang, Joel McCorkel

Proceedings Volume 9472, 947214 (2015) https://doi.org/10.1117/12.2177507

KEYWORDS: Sensors, Bidirectional reflectance transmission function, RGB color model, Data modeling, Calibration, Atmospheric modeling, MODIS, Device simulation, Environmental sensing, Image sensors

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Proceedings Article | 2 October 2014 Paper

Performance of the Thermal Infrared Sensor on-board Landsat 8 over the first year on-orbit

Matthew Montanaro, Julia Barsi, Allen Lunsford, Scott Rohrbach, Brian Markham

Proceedings Volume 9218, 921817 (2014) https://doi.org/10.1117/12.2063457

KEYWORDS: Calibration, Stray light, Sensors, Signal detection, Data modeling, Landsat, Staring arrays, Infrared sensors, Temperature metrology, Earth observing sensors

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Proceedings Article | 23 September 2013 Paper

Preliminary on-orbit performance of the Thermal Infrared Sensor (TIRS) on board Landsat 8

Matthew Montanaro, Zelalem Tesfaye, Allen Lunsford, Brian Wenny, Dennis Reuter, Brian Markham, Ramsey Smith, Kurtis Thome

Proceedings Volume 8866, 88661D (2013) https://doi.org/10.1117/12.2025650

KEYWORDS: Sensors, Calibration, Signal detection, Landsat, Temperature metrology, Quantum well infrared photodetectors, Infrared sensors, Black bodies, Space telescopes, Earth observing sensors

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Proceedings Article | 19 November 2012 Paper

Simulation of the performance and image quality characteristics of the Landsat OLI and TIRS sensors using DIRSIG

John Schott, Aaron Gerace, Matthew Montanaro

Proceedings Volume 8533, 85330L (2012) https://doi.org/10.1117/12.971226

KEYWORDS: Sensors, Landsat, Data modeling, Device simulation, Image quality, Image sensors, Infrared sensors, Calibration, Neodymium, Earth observing sensors

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The Digital Imaging and Remote Sensing (DIRS) Image Generation (DIRSIG) model has been significantly upgraded to support the Landsat Data Continuity Mission (LDCM). The DIRSIG improvements simulate the LDCM Thermal Infrared Sensor (TIRS) and the Operational Land Imager (OLI) sensor’s characteristics in support of the NASA and USGS image quality assessment programs. These improvements allow for simulation of spacecraft orbits using standard NORAD two line element (TLE) orbital descriptors. Sensor improvements include individual detector element lines of site, relative spectral response (RSR), bias, gain, non-linear response, and noise. Using DIRSIG’s existing source-targetsensor radiative transfer, atmospheric propagation, scene simulation, and thermal models, simulated Landsat 8 imagery was generated. These tools were developed to enable assessment of design trades during instrument development and build, and evaluation of expected performance during instrument test, as test data is used to refine the modeled instrument performance. Current efforts are aimed at refining predicted performance models, simulating on-orbit calibration maneuvers and generation of data to test data processing and analysis algorithms. Initial studies are aimed at assessing the impact of RSR variation on banding and striping in both OLI and TIRS and the use of side slither (90° Yaw) as a possible method to characterize and potentially compensate for non-linearity effects. Ongoing work aimed at simulating targets to support image based registration of the OLI and TIRS instruments is also presented. In general, the use of advanced simulation and modeling tools to support instrument design trades, image quality prediction, on-orbit image quality assessment and operational trades is reviewed. The overall effort is designed to provide simulated imagery incorporating all aspects of the instrument acquisition physics and scene phenomenology in support of instrument developers, operators, and Earth observation scientists.

Proceedings Article | 15 October 2012 Paper

Dark and background response stability for the Landsat 8 Thermal Infrared Sensor

Kelly Vanderwerff, Matthew Montanaro

Proceedings Volume 8510, 85100A (2012) https://doi.org/10.1117/12.930139

KEYWORDS: Sensors, Calibration, Landsat, Infrared sensors, Space telescopes, Black bodies, Aerospace engineering, Signal detection, Temperature metrology, Earth observing sensors

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Proceedings Article | 24 May 2012 Paper

Tracking nonuniformity in the thermal infrared sensor through pre-launch measurements and simulated on-orbit data

Matthew Montanaro, Aaron Gerace

Proceedings Volume 8390, 83902C (2012) https://doi.org/10.1117/12.919420

KEYWORDS: Calibration, Sensors, Signal detection, Black bodies, Landsat, Infrared sensors, Digital imaging, Data modeling, Photons, Earth observing sensors

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Proceedings Article | 14 September 2011 Paper

Landsat Data Continuity Mission operational land imager and thermal infrared sensor performance

Brian Markham, Philip Dabney, Dennis Reuter, Kurtis Thome, James Irons, Julia Barsi, Matt Montanaro

Proceedings Volume 8153, 81530D (2011) https://doi.org/10.1117/12.895542

KEYWORDS: Sensors, Landsat, Infrared sensors, Calibration, Imaging systems, Reflectivity, Signal to noise ratio, Space operations, Earth observing sensors, Lamps

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Proceedings Article | 21 May 2011 Paper

Performance of the QWIP focal plane arrays for NASA's Landsat Data Continuity Mission

M. Jhabvala, K. Choi, A. Waczynski, A. La, M. Sundaram, E. Costard, C. Jhabvala, E. Kan, D. Kahle, R. Foltz, N. Boehm, M. Hickey, J. Sun, T. Adachi, N. Costen, L. Hess, H. Facoetti, M. Montanaro

Proceedings Volume 8012, 80120Q (2011) https://doi.org/10.1117/12.886274

KEYWORDS: Quantum well infrared photodetectors, Sensors, Staring arrays, Readout integrated circuits, Landsat, Optical filters, Gallium arsenide, Infrared sensors, Silicon, Earth observing sensors

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Proceedings Article | 20 May 2011 Paper

The Operational Land Imager (OLI) and the Thermal Infrared Sensor (TIRS) on the Landsat Data Continuity Mission (LDCM)

Dennis Reuter, James Irons, Allen Lunsford, Matthew Montanaro, Fernando Pellerano, Cathleen Richardson, Ramsey Smith, Zelalem Tesfaye, Kurtis Thome

Proceedings Volume 8048, 804812 (2011) https://doi.org/10.1117/12.885963

KEYWORDS: Landsat, Sensors, Calibration, Space operations, Infrared sensors, Imaging systems, Spatial resolution, Staring arrays, Observatories, Earth observing sensors

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Proceedings Article | 20 May 2011 Paper

Calibration plan for the Thermal Infrared Sensor on the Landsat Data Continuity Mission

K. Thome, A. Lunsford, M. Montanaro, D. Reuter, R. Smith, Z. Tesfaye, B. Wenny

Proceedings Volume 8048, 804813 (2011) https://doi.org/10.1117/12.886473

KEYWORDS: Calibration, Sensors, Black bodies, Landsat, Staring arrays, Mirrors, Stray light, Electronics, Monochromators, Earth observing sensors

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Proceedings Article | 20 May 2011 Paper

Spectral analysis of the primary flight focal plane arrays for the thermal infrared sensor

Matthew Montanaro, Dennis Reuter, Brian Markham, Kurtis Thome, Allen Lunsford, Murzy Jhabvala, Scott Rohrbach, Aaron Gerace

Proceedings Volume 8048, 804816 (2011) https://doi.org/10.1117/12.889265

KEYWORDS: Sensors, Black bodies, Quantum well infrared photodetectors, Calibration, Landsat, Infrared sensors, Optical filters, Digital imaging, Atmospheric sensing, Earth observing sensors

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Proceedings Article | 22 April 2009 Paper

Radiometric modeling of mechanical draft cooling towers to assist in the extraction of their absolute temperature from remote thermal imagery

Matthew Montanaro, Carl Salvaggio, Scott Brown, David Messinger, Alfred Garrett, James Bollinger

Proceedings Volume 7299, 729909 (2009) https://doi.org/10.1117/12.817527

KEYWORDS: Sensors, Bidirectional reflectance transmission function, Thermal modeling, Thermography, Fluctuations and noise, Image sensors, Image processing, Physics, Solid modeling, 3D modeling

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Proceedings Article | 11 April 2008 Paper

Apparent temperature dependence on localized atmospheric water vapor

Matthew Montanaro, Carl Salvaggio, Scott Brown, David Messinger, Alfred Garrett

Proceedings Volume 6966, 696618 (2008) https://doi.org/10.1117/12.774694

KEYWORDS: Sensors, Black bodies, Directed energy weapons, Atmospheric sensing, Atmospheric modeling, Infrared radiation, Long wavelength infrared, Atmospheric particles, Thermal modeling, Platinum

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The atmosphere is a critical factor in remote sensing. Radiance from a target must pass through the air column to reach the sensor. The atmosphere alters the radiance reaching the sensor by attenuating the radiance from the target via scattering and absorption and by introducing an upwelling radiance. In the thermal infrared, these effects will introduce errors in the derived apparent temperature of the target if not properly accounted for. The temperature error is defined as the difference between the target leaving apparent temperature and observed apparent temperature. The effects of the atmosphere must be understood in order to develop methods to compensate for this error. Different atmospheric components will affect the radiation passing through it in different ways. Certain components may be more important than others depending on the remote sensing application. The authors are interested in determining the actual temperature of the superstructure that composes a mechanical draft cooling tower (MDCT), hence water vapor is the primary constituent of concern. The tower generates a localized water vapor plume located between the target and sensor. The MODTRAN radiative transfer code is used to model the effects of a localized exhaust plume from a MDCT in the longwave infrared. The air temperature and dew point depression of the plume and the thickness of the plume are varied to observe the effect on the apparent temperature error. In addition, the general atmospheric conditions are varied between two standard MODTRAN atmospheres to study any effect that ambient conditions have on the apparent temperature error. The Digital Imaging and Remote Sensing Image Generation (DIRSIG) modeling tool is used to simulate the radiance reaching a thermal sensor from a target after passing through the water vapor plume. The DIRSIG results are validated against the MODTRAN results. This study shows that temperature errors of as much as one Kelvin can be attributed to the presence of a localized water vapor plume.

Proceedings Article | 7 May 2007 Paper

Radiometric modeling of cavernous targets to assist in the determination of absolute temperature for input to process models

Matthew Montanaro, Carl Salvaggio, Scott Brown, David Messinger, Adam Goodenough, Alfred Garrett, Eliel Villa-Aleman

Proceedings Volume 6565, 656511 (2007) https://doi.org/10.1117/12.718666

KEYWORDS: Bidirectional reflectance transmission function, Reflection, Reflectivity, Process modeling, Sensors, Black bodies, Infrared imaging, Solid modeling, Digital imaging, 3D modeling

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Determining the temperature of an internal surface within cavernous targets, such as the interior wall of a mechanical draft cooling tower, from remotely sensed imagery is important for many surveillance applications that provide input to process models. The surface leaving radiance from an observed target is a combination of the self-emitted radiance and the reflected background radiance. The self-emitted radiance component is a function of the temperature-dependent blackbody radiation and the view-dependent directional emissivity. The reflected background radiance component depends on the bidirectional reflectance distribution function (BRDF) of the surface, the incident radiance from surrounding sources, and the BRDF for each of these background sources. Inside a cavity, the background radiance emanating from any of the multiple internal surfaces will be a combination of the self-emitted and reflected energy from the other internal surfaces as well as the downwelling sky radiance. This scenario provides for a complex radiometric inversion problem in order to arrive at the absolute temperature of any of these internal surfaces. The cavernous target has often been assumed to be a blackbody, but in field experiments it has been determined that this assumption does not always provide an accurate surface temperature. The Digital Imaging and Remote Sensing Image Generation (DIRSIG) modeling tool is being used to represent a cavity target. The model demonstrates the dependence of the radiance reaching the sensor on the emissivity of the internal surfaces and the multiple internal interactions between all the surfaces that make up the overall target. The cavity model is extended to a detailed model of a mechanical draft cooling tower. The predictions of derived temperature from this model are compared to those derived from actual infrared imagery collected with a helicopter-based broadband infrared imaging system collected over an operating tower located at the Savannah River National Laboratory site.

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