MEASUREMENTS AND MODELING OF LWIR SPECTRAL EMISSIVITY OF CONTAMINATED QUARTZ SAND

John Kerekes, Michael Gartley, Christopher De Angelis, Carl Salvaggio

Digital Imaging and Remote Sensing Laboratory, Chester F. Carlson Center for Imaging Science Rochester Institute of Technology, Rochester, New York USA

Christopher Gittins1, Michael Costolo, Bogdan Cosofret Physical Sciences, Inc., Andover, Massachusetts USA

1 C. Gittins is now with UTC Aerospace Systems in Westford, Massachusetts.

ABSTRACT The fundamental understanding of effects of liquid contaminants on the longwave infrared spectral emissivity of surfaces contaminated is desirable. This research describes modeling and longwave infrared spectral emissivity measurements for samples of SiO2 (sand) with and without 0.3% (by weight) of SF96 (poly dimethyl siloxane) oil. Two different sand particle size ranges were considered. The modeling was performed using a micro-scattering code and the empirical emissivity measurements were made outdoors using a D&P Instruments Model 102F MicroFTIR non-imaging spectrometer. The data were calibrated and processed to retrieve the spectral emissivity. General observations included a significant increase in emissivity in the 8 to 9 and 12.5 to 13 micron regions due to the presence of the SF96. The comparison between the modeled and measured emissivities shows a consistent trend and significant separability between the spectral emissivity of sand with and without the SF96 present.

Index Terms— infrared spectral emissivity, contaminated surfaces, longwave hyperspectral imaging

1. INTRODUCTION The fundamental understanding of the effects of liquid contaminants on the longwave infrared (LWIR) spectral emissivity of surfaces contaminated is desirable. Measurements of the LWIR emissivity of materials have most often been done in the pristine environment of a laboratory. While there have been efforts at field measurements of materials contaminated with different liquids and solids (e.g., [1]), these have just scratched the surface of the range of situations of interest. This research was undertaken to expand the understanding of how liquid contaminants affect the LWIR emissivity of sand in particular.

The paper is organized as follows. First we describe the materials considered and the sample preparation. Then we describe the empirical emissivity measurements. This is followed by a discussion of the modeling approach, and a comparison between the modeling and measurement results. We conclude with a summary and a discussion of future work.

2. SAMPLE PREPARATION Four types of samples were prepared for measurement of their LWIR spectral emissivity. Figure 1 shows a microphotograph of the two sizes of pristine samples.

• Plain (pristine) sand with particles ranging in size from 425 – 1000 microns

• Plain sand with particles ranging in size from 1000 – 1400 microns

• Sand with particles ranging in size from 425 – 1000 microns mixed with 0.3% (by weight) of SF96 (poly dimethyl siloxane) oil

• Sand with particles ranging in size from 1000 – 1400 microns mixed with 0.3% (by weight) of SF96 (poly dimethyl siloxane) oil

Figure 1. Microphotograph of plain (pristine) sand particles.

3. EMPIRICAL MEASUREMENTS

The instrument used by RIT for the measurements was a D&P Instruments Model 102F MicroFTIR. While the instrument collects data from 2 to 16 microns, the data in the midwave infrared (MWIR) are typically of low quality in a passive collection mode, and for this experiment, the emphasis was on the longwave infrared (7 to 13 microns).

The samples were poured into low profile cardboard containers and formed into a thin layer (1/8” to 1/4” deep). These sample containers were then placed on a small hot plate to raise their temperature to approximately 308 K and enhance the contrast with the atmospheric background. A total of eight emissivity measurements were made comprised of the four sample mixtures collected at nadir and 45º off-nadir viewing angles. The instrument aperture was approximately 26” above the samples during the nadir measurements. Given the 4.8º field of view of the instrument, this resulted in a spot size of approximately 2” at nadir, which expanded to 3” for the 45º off-nadir configuration, well within the extent of the sand in the cardboard containers. Figure 2 shows the measurement configuration on the roof of the Chester F. Carlson Center for Imaging Science building.

The processing of the data proceeded as follows. First the raw spectra were calibrated to spectral radiance using the blackbody measurements. Then the emissivity of the sample εsamp was derived using the following equation.

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εsamp =Lmeas − Ldwr

LBB (Tsamp ) − Ldwr

Figure 2. Nadir measurement configuration showing samples and the D&P instrument.

Here, Lmeas is the measured spectral radiance for the sample, Ldwr is the measured reflected downwelling spectral radiance, and LBB(Tsamp) is the blackbody radiance for the retrieved temperature of the sample. The retrieved temperature was found by stepping through a probable range of sample temperatures and finding the temperature that maximized the smoothness of the retrieved emissivity over a small spectral range. For these results the spectral range 8.2 to 8.5 microns was used. The temperature emissivity separation (TES) algorithm used in this processing was developed from references [2] and [3]. The calibration and TES processing was performed using in-house IDL codes.

Figure 3 shows the spectral emissivity curves measured in the nadir-viewing configuration for the different size particles with and without the SF96 present. Results for the 45º off-nadir configuration were very similar.

Figure 3. Spectral emissivity measurements using the nadir configuration for the small (a) and large (b) particles.

4. MODELING APPROACH The RIT code used to predict the emissivities is a version of the first-principles physics-based ray tracing image simulation tool DIRSIG [4]. This version is known as microDIRSIG. It uses microscale geometric structure and material scattering characteristics to predict spectral bidirectional reflectance distribution functions (BRDFs).

A variety of sand surface geometries were constructed and the surfaces were attributed with a spectral complex index of refraction taken from the literature [5]. It should be noted that the spectral shape of the complex index of refraction varies between authors due to silica material property variability (optical clarity, grain sizes, etc.) and the one assumed for this modeling may not precisely reflect that for the actual sand used in the empirical data collection.

Similarly, the spectral complex index of refraction for the single contaminant considered, SF96, was also leveraged from the literature [6] and utilized as an input to the microDIRSIG model. For the SF96 dosed modeling runs, we calculated the average surface film thickness required on each silica particle to achieve the target dosing level of 0.3% by weight. We determined a SF96 film thickness of

0.96 microns for the smaller grain size (750 microns) and 1.65 microns for the large grain size (1,200 microns) sand to cover the surface of each silica sphere and achieve the target total dosing of 0.3% (by weight).

Each microDIRSIG modeling run was conducted for a single wavelength from 7 to 13 microns in 50 nm increments for a total of 121 individual modeling runs per (1) view geometry, (2) silica particle size, (3) contaminant present or not present. The silica particle sizes chosen were a single value for each sand type for the modeling. The resulting output was a total of eight spectral cubes of bi-directional hemispherical reflectance maps. Hemispherical integration of the eight output spectral cubes permitted calculation of the total directional hemispherical reflectance (DHR) and also the directional hemispherical emissivity (DHE).

The nature of the current RIT modeling capability permits only Geometric Optics (GO) regime radiative transfer, leaving out diffraction effects when the particle size approaches the wavelength of light of interest. However, these modeling runs utilized particles that were two orders of magnitude above the wavelengths of interest keeping them safely in the GO regime.

5. MEASUREMENT VS. MODELING COMPARISON

This section presents plots to compare the empirical measurements with the model predictions. Figures 4 and 5 compare the spectral emissivity of the sand with and without SF96 for the two particle size ranges investigated. While there are differences between the measurements and modeling, a consistently clear trend is observed in that the addition of the SF96 to the sand leads to a higher emissivity in the Restrahlen band between 8 and 9 microns, and near 12.5 microns.

The model predictions show a more subtle difference for the two particle sizes than observed in the empirical data. This may be due to the assumed spherical shape for the modeled sand particles as compared to the irregular shapes observed in the real sand, as well as potential small errors in the emissivity data collection and processing. Differences in the overall spectral shape are most likely from the fact that the assumed silica index of refraction used in the model did not precisely reflect that for the sand used in the empirical measurements.

Figure 4. Comparison of the empirical (a) and model predicted (b) spectral emissivity for with and without SF95 for the small particles.

Figure 5. Comparison of the empirical (a) and model predicted (b) spectral emissivity for with and without SF95 for the large particles.

6. SUMMARY AND FUTURE WORK

This project has demonstrated the collection of spectral longwave infrared emissivity measurements of sand contaminated with liquid SF96 oil and generating comparable model predictions of emissivity spectra using a physics-based radiative transfer code. The measurements were challenging due to the heterogeneity of the surface and the difficulties of measuring accurate skin temperature for use in the temperature-emissivity separation algorithm.

The microDIRSIG model was seen to be capable of simulating the appropriate relative trend in the spectral emissivity of sand when contaminated with SF96 (an increase in the emissivity in the Restrahlen band between 8 and 9 microns, and another feature near 12.5 microns). However, the exact prediction is complicated due to the random geometric shape of real sand particles, which is challenging to reproduce in the simulated world, and the lack of perfect knowledge of the index of refraction for the real sand material.

The observed differences in the longwave emissivity spectra of sand with and without SF96 were of a significant level (up to 40% change in emissivity), which suggests the presence of SF96 in amounts considered here should be easily detectable by remote spectral observations.

While this project demonstrated LWIR emissivity measurements of contaminated sand, it was for a single material and contaminant type. Additional research is recommended to expand the types of substrates and contaminants and to collect data in more realistic situations such as a dirt road or parking lot.

7. ACKNOWLEDGMENTS This material is based upon work supported by U.S. Army Edgewood Contracting Division and the U.S. Army Edgewood Chemical Biological Center (ECBC) under Contract Number W911SR-12-C-0004. Any opinions, findings and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of U.S. Army. Dr. James Jensen and Dr. Janet Jensen of ECBC are gratefully acknowledged for their support of the project.

8. REFERENCES [1] J. Kerekes, K.-E. Strackerjan, and C. Salvaggio, "Spectral Reflectance and Emissivity of Man-made Surfaces Contaminated with Environmental Effects," Optical Engineering, vol. 47, no. 10, October 2008.

[2] N. Bower, Knuteson, R., and Revercomb, H., “High spectral resolution land surface temperature and emissivity measurement in the thermal infrared. Proceedings of 10th Conference on Atmospheric Radiation: A Symposium with tributes to the works of Verner E. Suomi, Madison, WI, 28 June-2 July 1999, pp. 528-531, American Meteorological Society, 1999. [3] K. Horton, J. Johnson, and P. Lucey, “Infrared measurements of pristine and disturbed soils 2. Environmental effects and field data reduction,” Remote Sensing Environment, vol. 64, pp. 47-52, 1998. [4] DIRSIG - The Digital Imaging and Remote Sensing Image Generation Model, http://www.dirsig.org. [5] R. Kitamura, L. Pilon, M. Jonasz, “Optical constants of silica glass from extreme ultraviolet to far infrared at near room temperature,” Applied Optics, vol. 46, no. 33, pp. 8118- 8133, 2007. [6] M. Querry, “Optical constants of minerals and other materials from the millimeter to the ultraviolet,” Chemical Research, Development and Engineering Center, University of Missouri-Kansas City, November 1987.