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NASA NTRS2016

Development of X-Ray Microcalorimeter Imaging Spectrometers for the X-Ray Surveyor Mission Concept

Four astrophysics missions are currently being studied by NASA as candidate large missions to be chosen inthe 2020 astrophysics decadal survey.1 One of these missions is the X-Ray Surveyor (XRS), and possibleconfigurations of this mission are currently under study by a science and technology definition team (STDT). Oneof the key instruments under study is an X-ray microcalorimeter, and the requirements for such an instrument arecurrently under discussion. In this paper we review some different detector options that exist for this instrument,and discuss what array formats might be possible. We have developed one design option that utilizes eithertransition-edge sensor (TES) or magnetically coupled calorimeters (MCC) in pixel array-sizes approaching 100kilo-pixels. To reduce the number of sensors read out to a plausible scale, we have assumed detector geometriesin which a thermal sensor such a TES or MCC can read out a sub-array of 20-25 individual 1 pixels. In thispaper we describe the development status of these detectors, and also discuss the different options that exist forreading out the very large number of pixels.

multiplexing↗
NASA NTRS2015

Modulation Transfer Function of Infrared Focal Plane Arrays

Modulation transfer function (MTF) is the ability of an imaging system to faithfully image a given object. The MTF of an imaging system quantifies the ability of the system to resolve or transfer spatial frequencies. In this presentation we will discuss the detail MTF measurements of 1024x1024 pixels mid -wavelength and long- wavelength quantum well infrared photodetector, and 320x256 pixels long- wavelength InAs/GaSb superlattice infrared focal plane arrays (FPAs). Long wavelength Complementary Barrier Infrared Detector (CBIRD) based on InAs/GaSb superlattice material is hybridized to recently designed and fabricated 320x256 pixel format ROIC. The n-type CBIRD was characterized in terms of performance and thermal stability. The experimentally measured NE delta T of the 8.8 micron cutoff n-CBIRD FPA was 18.6 mK with 300 K background and f/2 cold stop at 78K FPA operating temperature. The horizontal and vertical MTFs of this pixel fully delineated CBIRD FPA at Nyquist frequency are 49% and 52%, respectively.

long-wave infrared↗
NASA NTRS2017

Development of Multi-Sensor Global Cloud and Radiance Composites for Earth Radiation Budget Monitoring from DSCOVR

The Deep Space Climate Observatory (DSCOVR) enables analysis of the daytime Earth radiation budget via the onboard Earth Polychromatic Imaging Camera (EPIC) and National Institute of Standards and Technology Advanced Radiometer (NISTAR). Radiance observations and cloud property retrievals from low earth orbit and geostationary satellite imagers have to be co-located with EPIC pixels to provide scene identification in order to select anisotropic directional models needed to calculate shortwave and longwave fluxes. A new algorithm is proposed for optimal merging of selected radiances and cloud properties derived from multiple satellite imagers to obtain seamless global hourly composites at 5-kilometer resolution. An aggregated rating is employed to incorporate several factors and to select the best observation at the time nearest to the EPIC measurement. Spatial accuracy is improved using inverse mapping with gradient search during reprojection and bicubic interpolation for pixel resampling. The composite data are subsequently remapped into EPIC-view domain by convolving composite pixels with the EPIC point spread function (PSF) defined with a half-pixel accuracy. PSF-weighted average radiances and cloud properties are computed separately for each cloud phase. The algorithm has demonstrated contiguous global coverage for any requested time of day with a temporal lag of under 2 hours in over 95 percent of the globe.

Khlopenkov, Konstantin↗
NASA NTRS2017

Modeling and Measuring Charge-Sharing in Hard X-ray Imagers Using HEXITEC CdTe Detectors

The Rutherford Appleton Laboratory's HEXITEC ASIC has been designed to provide fine pixelated X-ray spectroscopic imaging in combination with a CdTe or CZT detector layer. Although HEXITEC's small pixels enable higher spatial resolution as well as higher spectral resolution via the small-pixel effect, they also increase the probability of charge sharing, a process which degrades spectral performance by dividing the charge induced by a single photon among multiple pixels. In this paper, we investigate the effect of this process on a continuum X-ray spectrum below the Cd and Te fluorescence energies (23 keV). This is done by comparing laboratory measurements with simulations performed with a custom designed model of the HEXITEC ASIC. We find that the simulations closely match the observations implying that we have an adequate understanding of both charge sharing and the HEXITEC ASIC itself. These results can be used to predict the distortion of a spectrum measured with HEXITEC and will help determine to what extent it can be corrected. They also show that models like this one are important tools in developing and interpreting observations from ASICs like HEXITEC.

Ryan, Daniel F.↗
NASA NTRS2017

Improved Reference Sampling and Subtraction: A Technique for Reducing the Read Noise of Near-Infrared Detector Systems

Near-infrared array detectors, like the James Webb Space Telescope (JWST) NIRSpec's Teledyne's H2RGs, often provide reference pixels and a reference output. These are used to remove correlated noise. Improved reference sampling and subtraction (IRS(exp 2)) is a statistical technique for using this reference information optimally in a leastsquares sense. Compared with the traditional H2RG readout, IRS(exp 2) uses a different clocking pattern to interleave many more reference pixels into the data than is otherwise possible. Compared with standard reference correction techniques, IRS(exp 2) subtracts the reference pixels and reference output using a statistically optimized set of frequency dependent weights. The benefits include somewhat lower noise variance and much less obvious correlated noise. NIRSpec's IRS(exp 2) images are cosmetically clean, with less 1/f banding than in traditional data from the same system. This article describes the IRS(exp 2) clocking pattern and presents the equations needed to use IRS(exp 2) in systems other than NIRSpec. For NIRSpec, applying these equations is already an option in the calibration pipeline. As an aid to instrument builders, we provide our prototype IRS(exp 2) calibration software and sample JWST NIRSpec data. The same techniques are applicable to other detector systems, including those based on Teledyne's H4RG arrays. The H4RG's interleaved reference pixel readout mode is effectively one IRS(exp 2) pattern.

Methods-statistical↗
NASA NTRS2018

Hawkeye Ocean Color Instrument – Performance Summary

Hawkeye is an ocean color instrument that is part of the SeaHawk satellite developed for SOCON, the Sustained Ocean Color Observations using Nanosatellites program funded by the Gordon and Betty Moore Foundation and managed by the University of North Carolina – Wilmington (UNC-W). HawkEye has spectral characteristics similar to SeaWiFS, but with 8 times finer resolution and a smaller field of view more appropriate for lakes, rivers, and near-shore terrestrial environments. With a volume of only 10 X 10 X 10 cm (a CubeSat 1U), it can produce 8 bands of image data in a single pass, each with 1800 x 6000 pixels, with a resolution of 120 meters per pixel. This paper will present a short summary of instrument design, the spacecraft interface, and “lessons learned” during this effort. Scientists considering using linear arrays in a pushbroom mode for remote sensing will find this useful. Much of the discussion will center on optical performance, such as flat field calibration, polarization effects, stray light, out-of-band response, and exposure linearity. Images from field tests will be shown.The Hawkeye instrument is an ocean color measuring instrument designed to fly on the SeaHawk satellite developed for SOCON, the Sustained Ocean Color Observations using Nanosatellites program funded by the Gordon and Betty Moore Foundation and managed by the University of North Carolina – Wilmington (UNC-W). The Hawkeye instrument measures ocean color in 8 spectral bands, similar to SeaWiFS, except Band 7, which is shifted to a slightly lower wavelength to avoid the oxygen absorption feature that a wider band overlapped on SeaWiFS. The instrument is approximately 1/3rd the volume of the entire satellite, which is a 3U Cubesat manufactured by Clydespace in Glasgow, Scotland. The purpose of this instrument is to ascertain the quality of ocean color data possible with such a small, inexpensive instrument and bus. The nominal orbit is 540 km, and the nominal pixel geometric instantaneous field of view (GIFOV) 120 meters on a side. Each band will produce an image 1800 x 6000 pixels in size, for a total field of view of 216 X 720 km.2) DESIGN CONCEPTThe Hawkeye instrument uses linear arrays in pushbroom mode to collect data over a two dimensional area. The instrument has 4 linear CCD arrays, the Onsemi KLI-4104, to collect the 8 bands of data. Figure 1 illustrates the optical design for two bands, sharing a single array.

Hawkeye↗
NASA NTRS2020

Detector Channel Combining Results from a High Photon Efficiency Optical Communications Link Test Bed

The National Aeronautics and Space Administration (NASA) Glenn Research Center (GRC) is developing a low cost, scalable, photon-counting receiver prototype for space-to-ground optical communications links. The receiver is being tested in a test bed that emulates photon-starved space-to-ground optical communication links. The receiver uses an array of single-pixel fiber-coupled superconducting nanowire single-photon detectors. The receiver is designed to receive the high photon efficiency serially concatenated pulse position modulation (SCPPM) waveform specified in the Consultative Committee for Space Data Systems (CCSDS) Optical Communications Coding and Synchronization Blue Book Standard. The optical receiver consists of an array of single-pixel superconducting nanowire detectors, analog phase shifters for channel alignment, digitizers for each detector channel, and digital processing of the received signal. An overview of the test bed and arrayed receiver system is given. Simulation and system characterization results are presented. The data rate increase of using a four-channel arrayed detector system over using one single pixel nanowire detector is characterized. Results indicate that a single-pixel detector is capable of receiving data at a rate of 40 Mbps and a four-channel arrayed detector system is capable of receiving data at a rate of 130 Mbps.

Downey, Jennifer N.↗
NASA NTRS2020

Detector Channel Combining Results from a High Photon Efficiency Optical Communications Link Test Bed

The National Aeronautics and Space Administration (NASA) Glenn Research Center (GRC) is developing a low cost, scalable, photon counting receiver prototype for space-to-ground optical communications links. The receiver is being tested in a test bed that emulates photon-starved space-to-ground optical communication links. The receiver uses an array of single-pixel fiber-coupled superconducting nanowire single-photon detectors. The receiver is designed to receive the high photon efficiency serially concatenated pulse position modulation (SCPPM) waveform specified in the Consultative Committee for Space Data Systems (CCSDS) Optical Communications Coding and Synchronization Blue Book Standard. The optical receiver consists of an array of single-pixel superconducting nanowire detectors, analog phase shifters for channel alignment, digitizers for each detector channel, and digital processing of the received signal. An overview of the test bed and arrayed receiver system is given. Simulation and system characterization results are presented. The data rate increase of using a four-channel arrayed detector system over using one single pixel nanowire detector is characterized. Results indicate that a single-pixel detector is capable of receiving data at a rate of 40 Mbps and a four-channel arrayed detector system is capable of receiving data at a rate of 130 Mbps.

Downey, Jennifer N.↗
NASA NTRS2020

A Machine Learning-Based Cloud Detection and Thermodynamic Phase Classification Algorithm using Passive Spectral Observations

We trained two Random Forest (RF) machine-learning models for cloud mask and cloud thermodynamic phase detection using spectral observations from VIIRS on Suomi NPP (SNPP). Observations from CALIOP were carefully selected to provide reference labels. The two RF models were trained for all-day and daytime-only conditions using a 4-year collocated VIIRS/CALIOP dataset from 2013 to 2016. Due to the orbit difference, the collocated CALIOP and SNPP VIIRS training samples cover a broad viewing zenith angle range, which is a great benefit to overall model performance. The all-day model uses 3 VIIRS infrared (IR) bands (8.6,11, and 12 μm) and the daytime model uses 5 Near-IR (NIR) and Shortwave-IR (SWIR) bands (0.86, 1.24, 1.38, 1.64 and 2.25 μm) together with the 3 IR bands to detect clear, liquid water, and ice cloud pixels. Up to 7 surface types, namely, ocean/water, forest, cropland, grassland, snow/ice, barren/desert, and shrubland, were considered separately to enhance performance for both models. Detection of cloudy pixels and thermodynamic phase with the two RF models were compared against collocated CALIOP products from 2017. It is shown that, with a conservative screening process that excludes the most challenging cloudy pixels for passive remote sensing, the two RF models have high accuracy rates in comparison with the CALIOP reference for both cloud detection and thermodynamic phase. Other existing SNPP VIIRS and Aqua MODIS cloud mask and phase products are also evaluated, with results showing that the two RF models and the MODIS MYD06 optical property phase product are the top 3 algorithms with respect to lidar observations during the daytime. During the nighttime, the RF all-day model works best for both cloud detection and phase, in particular for pixels over snow/ice surfaces. The present RF models can be extended to other similar passive instruments if training samples can be collected from CALIOP or other lidars. However, the quality of reference labels and potential sampling issues that may impact model performance would need further attention.

cloud detection↗
NASA NTRS2020

Version 2 Ozone Monitoring Instrument SO2 Product (OMSO2 V2): New Anthropogenic SO2 Vertical Column Density Dataset

The Ozone Monitoring Instrument (OMI) has been providing global observations of SO2 pollution since 2004. Here we introduce the new anthropogenic SO2 vertical column density (VCD) dataset in the version 2 OMI SO2 product (OMSO2 V2). As with the previous version (OMSO2 V1.3), the new dataset is generated with an algorithm based on principal component analysis of OMI radiances, but features several updates. The most important among those is the use of expanded lookup tables and model a priori profiles to estimate SO2 Jacobians for individual OMI pixels, in order to better characterize pixel-to-pixel variations in SO2 sensitivity, including over snow and ice. Additionally, new data screening and spectral fitting schemes have been implemented to improve the quality of the spectral fit. As compared with the planetary boundary layer SO2 dataset in OMSO2 V1.3, the new dataset has substantially better data quality, especially over areas that are relatively clean or affected by the south Atlantic anomaly. The updated retrievals over snow/ice yield more realistic seasonal changes in SO2 at high latitudes and offer enhanced sensitivity to sources during wintertime. An error analysis has been conducted to assess uncertainties in SO2 VCDs from both the spectral fit and Jacobian calculations. The uncertainties from spectral fitting are reflected in SO2 slant column densities (SCDs) and largely depend on the signal-to-noise ratio of the measured radiances, as implied by the generally smaller SCD uncertainties over clouds or for smaller solar zenith angles. The SCD uncertainties for individual pixels are estimated to be~0.15-0.3 DU (Dobson Units) between ~40°S and ~40°N and to be~0.2-0.5 DU at higher latitudes. The uncertainties from the Jacobians are approximately ~50-100% over polluted areas, and primarily attributed to errors in SO2 a priori profiles and cloud pressures, as well as the lack of explicit treatment for aerosols. Finally, the daily mean and median SCDs over the presumably SO2-free equatorial East Pacific have increased by only~0.0035 DU and ~0.003 DU respectively over the entire 15-year OMI record; while the standard deviation of SCDs has grown by only~0.02 DU or ~10%. Such remarkable long-term stability makes the new dataset particularly suitable for detecting regional changes in SO2 pollution.

OMI, SO2, Remote Sensing↗
NASA NTRS2021

Detection of Anomalies in the UV/Vis Reflectances from the Ozone Monitoring Instrument

Various instrumental or geophysical artifacts, such as saturation, stray light, or obstruction of light (either coming from the instrument or related to solar eclipses), negatively impact satellite measured ultraviolet and visible Earthshine radiance spectra and downstream retrievals of atmospheric and surface properties derived from these spectra. In addition, excessive noise such as from cosmic ray impacts, prevalent within the South Atlantic Anomaly, can also degrade satellite radiance measurements. Saturation specifically pertains to observations of very bright surfaces such as sun glint over open water or thick clouds. When saturation occurs, additional photoelectric charge generated at the saturated pixel may overflow to pixels adjacent to a saturated area and be reflected as a distorted image in the final sensor output.When these effects cannot be corrected to an acceptable level for science quality retrievals, flagging of the affected pixels is indicated. Here, we introduce a straightforward detection method that is based on the correlation, r, between the observed Earthshine radiance and solar irradiance spectraover a 10 nm-spectral range; our Decorrelation Index (DI for brevity) is simply defined as DI=1-r. DI increases with anomalous additive effects or excessive noise in either radiances, the most likely cause indata from theOzone Monitoring Instrument (OMI),or irradiances. DI is relatively straight-forward to use and interpret and can be 20computed for different wavelength intervals. We developed a set of DIs for two spectral channelsof the OMI, a hyperspectral pushbroom imaging spectrometer. For each OMI spatial measurement, we define 14 wavelength-dependent DIs within the OMI visible channel (350-498 nm) and 6 DIs in its ultraviolet 2 (UV2) channel (310-370 nm). As defined, DIs reflect a continuous range of deviations of observed spectra from the reference irradiance spectrum that are complementary to the binary Saturation Possibility Warning (SPW) flags currently provided for each individual spectral/spatial pixel in the OMI radiance data set. Smaller values of DI are also caused by a number of geophysical factors; this allows one to obtain interesting physical results on the global distribution of spectral variations.

Space sensor↗
NASA NTRS2020

Dust Aerosol Retrieval Over the Oceans With the MODIS/VIIRS Dark Target Algorithm: 2. Nonspherical Dust Model

The Dark‐target (DT) aerosol algorithm retrieves spectral Aerosol Optical Depth (AOD) and other aerosol properties from Moderate‐resolution Imaging Spectrometer (MODIS) reflectance observations. Over the ocean, the DT algorithm is known to contain scattering‐angle‐dependent biases in its retrievals of AOD, Angstrom Exponent (AE), and Fine Mode Fraction (FMF) for dust aerosols. Following a two‐step strategy to improve the DT retrieval of dust over ocean, for which the first step is to identify dusty pixels (reported in “Part 1”), in this “Part 2,” we report on construction of a new dust model lookup table (LUT) and the strategy for applying it within the existing DT algorithm. In particular, we evaluate different characterizations of dust optical properties from a variety of frameworks and databases, and compare them with the current DT retrieval assumptions. Substituting the standard operational LUT with a spheroid dust model with identified dusty pixels shows significant improvement when compared with collocated AERONET‐identified dusty pixels. Specifically, the application of the new dust model to dusty pixels reduces their AOD bias from 0.06 to 0.02 while improving the fraction of retrievals within expected error from 64% to 82%. At the same time, the overall bias in AE is reduced from 0.13 to 0.06, and the scattering‐angle‐dependent AE bias is largely eliminated. In testing on two full months of data (April and July), the new retrieval will reduce the monthly mean AOD by up to 0.1 and 0.2 in the north Atlantic and Arabian seas, respectively. The average AE and FMF are also reduced in these dust heavy regions

dust aerosol retrieval↗
NASA NTRSDate not supplied

Ground control points refresh for MODIS and VIIRS geolocation monitoring

The Control Point Matching (CPM) program and a set of over 1200 globally distributed ground control points (GCPs ) have been successfully used to develop more than 20 years of MODIS geolocation products. In this research, we refresh current GCP library with more than 2500 new GCPs using the latest Landsat 8 Collection 2 images. The refreshed GCPs are distributed from 56 S to 80 N latitude, with more than 2000 shoreline and 500 inland GCP chips. The size of these GCPs are extended from800*800 to 1400*1400 Landsat pixels and the CPM program correspondingly increase s t h e searching distance from 0.8 pixels to 2.5 pixels, which also extends the geolocation error measurement from +/-45 to the edge of scan at +/-55 degree in scan angle. This will allow the algorithm to catch geolocation errors that are larger than one MODIS pixel. The geolocation errors measured with the refreshed GCP library are comparable to the previous results, yet with 2-3more times of matched GCPs. The daytime Aqua ascending orbits and Terra descending orbits enable us to identify a few GCP outliers which might be due to the quality of the original Landsat images. Most importantly, the refreshed GCP library will include images from both Landsat band 4 to match with VIIRS I1, and Landsat band 6 to match with VIIRS I3. This will allow us to measure and correct on orbit band-to-band registration at both track and scan directions, which will help understanding and improving future JPSS mission’s prelaunch geometric performance.

VIIRS↗
NASA NTRS2021

Infrared Imaging using Sparse Microresonator Arrays and Compressed Sensing

In this work, we present an uncooled infrared (IR) thermal imager featuring a sparse array of resonant pixels that can be monolithically integrated with the readout electronics for long-term operation in high-temperature environments. Each of the detector pixels is made of an IR-sensitive GaN microresonator developed to be operated at up to 500 0C. The pixels are employed in sparse array configuration to allow for routing the signal carrying traces and to ease the routing from pixel to circuit, as well as to reduce the power consumption at the expense of moderate post-processing time and increased complexity to recover the image. Three 128 by 128 sparse resonator arrays with the array densities of 10%, 25% and 37.5% are designed, and used in conjunction with the Compressed Sensing (CS) method to reconstruct complex test images from highly incomplete data. Capabilities and performance metrics of the recovery technique are explored.

Rais-Zadeh, Mina↗
NASA NTRS2021

Thermal Infrared Detector Sparse Array for NASA Planetary Applications

In this work, we present an uncooled infrared (IR) thermal imager featuring a sparse array of resonant pixels that can be monolithically integrated with the readout electronics for long-term operation in hightemperature environments. Each of the detector pixels is made of an IR-sensitive GaN microresonator developed to be operated at up to 500 0C. The pixels are employed in sparse array configuration to allow for routing the signal carrying traces and to ease the routing from pixel to circuit, as well as to reduce the power consumption. A 128´128 sparse resonator array with the array density of 37.5% is designed and used in conjunction with the Compressed Sensing method to reconstruct complex test images from highly incomplete data. Capabilities and performance metrics of the recovery technique are explored.

Rais-Zadeh, Mina↗
NASA NTRS2022

X-ray Polarization Detection of Cassiopeia A with IXPE

We report on a ∼5σ detection of polarized 3–6 keV X-ray emission from the supernova remnant Cassiopeia A (Cas A) with the Imaging X-ray Polarimetry Explorer (IXPE). The overall polarization degree of 1.8% ± 0.3% is detected by summing over a large region, assuming circular symmetry for the polarization vectors. The measurements imply an average polarization degree for the synchrotron component of ∼2.5%, and close to 5% for the X-ray synchrotron-dominated forward shock region. These numbers are based on an assessment of the thermal and nonthermal radiation contributions, for which we used a detailed spatial-spectral model based on Chandra X-ray data. A pixel-by-pixel search for polarization provides a few tentative detections from discrete regions at the ∼ 3σ confidence level. Given the number of pixels, the significance is insufficient to claim a detection for individual pixels, but implies considerable turbulence on scales smaller than the angular resolution. Cas A's X-ray continuum emission is dominated by synchrotron radiation from regions within ≲1017 cm of the forward and reverse shocks. We find that (i) the measured polarization angle corresponds to a radially oriented magnetic field, similar to what has been inferred from radio observations; (ii) the X-ray polarization degree is lower than in the radio band (∼5%). Since shock compression should impose a tangential magnetic-field structure, the IXPE results imply that magnetic fields are reoriented within ∼1017 cm of the shock. If the magnetic-field alignment is due to locally enhanced acceleration near quasi-parallel shocks, the preferred X-ray polarization angle suggests a size of 3 × 1016 cm for cells with radial magnetic fields.

Polarimetry↗
NASA NTRSDate not supplied

Refinement of the Transition-edge Sensor Design for ATHENA X-IFU

The X-ray Integral Field Unit (X-IFU) instrument on the Advanced Telescope for High ENergy Astrophysics (ATHENA) is baselined to have 2376 transition-edge sensor (TES) microcalorimeter pixels in a single array. The required performance for X-IFU has been demonstrated on a kilo-pixel array of square TES’s with 50 m side length, and this is considered the baseline pixel design. However, over the last few years we have explored small modifications to this design in search of a globally optimized instrument performance. We have previously reported on investigations of extending the length of the TES’s and variations in the number of X-ray absorber support stems. Here we report on investigations of TES designs with a length of 50 m but narrower width. We will discuss the consequence of this on magnetic field sensitivity, resistive transition parameters, spectral performance, and ease of multiplexing. We will also discuss how the number and position of the absorber attachments influences the vibrational modes of the pixels, and the impact this may have on performance. Finally, we will present measurements of more substantial change options of our TES design, including more extreme TES geometries, the use of metal islands or etched holes in the silicon nitride membranes, and the position of wiring to minimize current-induced magnetic field effects. The exploration of all these changes to the design are not only useful for optimizing performance on X-IFU, but also guide our fundamental understanding of the key physics of TES microcalorimeters.

Nick Wakeham↗
NASA NTRSDate not supplied

LIPA: Lunar Ice Perception Algorithm

Introduction: The highest concentration of Lu-nar water-ice stores exists within the Permanently Shadowed Regions (PSRs) of the Lunar South Pole [1-3]. As such, the ability to locate in situ water-ice stores in an accurate, systematic, and safe manner will prove vital for future Lunar activities which rely on hydrogen-based resources. Here we show how the strong absorptive properties of ice can be exploited so that surface ice located in PSRs can be easily differentiated from the surrounding frozen regolith. Testbeds which simulate an icy lunar landscape were created and then systematically imaged using a mid-wave infrared (MWIR) camera system. Testbeds were imaged under two filter modes (1) high-absorption (high-abs) mode: whereby imagery captured were confined to a single central wave-length (CWL) of 3.15 ± 0.03 μm and (2) low-absorption (low-abs) mode: whereby imagery cap-tured were confined to a single CWL of 3.80 ± 0.04 μm (Figure 1). High- and low-absorption modes are related to the absorptive properties of ice at each selected wavelength, respectively. Corresponding images from each filter mode were differenced (i.e., pixels were subtracted) to enhance contrast between ice-bearing and non-ice-bearing pixels, and then fed into a semantic segmen-tation model. The model was trained to detect and differentiate between water, ice, shadows, and lunar regolith. Results: Modeling results accurately discrimi-nated ice from other materials (such as frozen lunar regolith) and were used to visually resolve the spa-tial extent of surface ice. Further, outputs produced through semantic segmentation were used to estimate water-ice contents in collected imagery [(Pixels with Class = “Water Ice”)/(Sum of Pixels)*100]. Summary: These works prove promising for future in situ resource utilization (ISRU) missions which employ robotics in combination with infrared camera systems to advance science objectives (e.g., locate water-ice in frozen regolith) on the lunar sur-face. References: [1] Cannon K. M., Deutsch A. N., Head J. W., and Britt D. T. (2020) Geophysical Re-search Letters, 46, e2020GL088920. [2] Honniball C. I. et al. (2021) Nature Astronomy 5, no. 2, 121-127. [3] Li S. et al. (2018) Proceedings of the National Academy of Sciences, 115(36), 8907-8912.

A. Slabic↗