Microwave Remote Sensing Laboratory

Understanding the Cryosphere, Earth's surface covered by snow and ice, and predicting future changes in its ice volume and mass are critical to track the climate and the water cycle on our planet. Because of the extreme environmental conditions, high costs of sparse in-situ measurements, and concerns about increasing human footprint associated with these regions, remote sensing instruments are preferred to monitor the Cryosphere. Among these instruments, microwave radiometers, i.e., passive receivers measuring microwave radiations from their targets, have many advantages since they can provide data independent of cloud conditions and solar illumination, and their measurements are highly sensitive to important ice properties such as thickness, temperature, density, and grain size. Provided with enough bandwidth, these instruments are, hypothetically, capable of profiling these properties from the surface to the deep ice. However, radiometer operations to observe the Cryosphere are currently far from ideal. First, they are limited to a few narrow frequency bands to avoid interference from active sources such as radars and wireless communication systems. Second, the electrical properties of ice, which determine the amount of electromagnetic radiation it emits, are not fully characterized versus frequency and temperature. This CAREER research, by modeling the electrical properties of ice across wide ranges of frequencies and temperatures and developing efficient interference mitigation algorithms, will enable the next generation of microwave radiometers capable of utilizing wide microwave frequency bands to fully probe Earth's ice bodies. Furthermore, with an education plan integrated with the research activities, this project will grow the investigator as a prominent scientist-educator and provide an applied, hands-on electromagnetics training for students at his institution.

Electromagnetic penetration depths vary with frequency in ice; thus, wideband microwave radiometers can be used to profile thermal and physical properties of ice bodies versus depth. The overarching goal of this career development project is to enable next generation of such instruments for the remote sensing of the Cryosphere. In pursuit of this goal, the complex permittivity of ice will be measured across wide frequency (0-50 GHz) and temperature (200-273 K) ranges for its electrical characterization. Measured permittivity values will be verified by comparing simulated microwave radiations over the Antarctic and the Arctic to the measurements of polar-orbiting space-borne microwave radiometers. Furthermore, multi-dimensional, machine learning based radio frequency interference detection and mitigation algorithms for microwave radiometers will be developed to allow their operations across wide frequency bands occupied and shared by active services. Lastly, a digital wideband radiometer prototype will be developed at the grantee's institution to implement these algorithms and validate the project outcomes through snow remote sensing measurements at fully characterized ground sites. The research activities will be incorporated into the engineering courses as a part of the investigator's applied electromagnetics education philosophy to educate future engineers and scientist in the field of microwave remote sensing.

Smart sensors of the future will be designed to extract maximum value information while minimizing the resources required to acquire, downlink and process data. Such intelligent instruments will reduce mission costs by avoiding taking measurements of uninteresting or unnecessary features while still capturing useful and beneficial data. However, many sensors like microwave radiometers can make calibrated measurements only after reaching near thermal equilibrium; this approach leads to wasted power, excess data, and delays in acquiring useful information. Furthermore, available spacecraft power and/or thermal requirements can lead to the need for power cycling a radiometer. Turning power off to an instrument stops its data acquisition but also leads to a loss of data when the instrument is powered back on until its electronics are sufficiently stable to make a calibrated measurement. Rapid power cycling can also be used to reduce the average power draw of the instrument, while providing near-continuous data at the cost of increased measurement uncertainty. Both techniques were employed by IceCube, which is described below. Minimizing resource utilization is key to realizing smart sensor technology. This research proposal aims to develop machine learning applications to calibrate across the sensor transient response due to power cycling. The outcome will be a machine learning calibration algorithm that draws upon the ability of neural networks to learn and utilize the characteristics of a sensor’s transient response and ancillary telemetry data to produce calibrated measurements with minimum uncertainty. Additionally, this will reduce sensor power, data volume and turn-on time required to obtain useful information.

Constellations of CubeSat radiometers offer enormous potential for Earth and Space observations as low-cost platforms with significant advantages over single, large and expensive monolithic systems such as real-time measurements with large coverage, resiliency of a distributed observation system, and graceful degradation with low-cost replenishment. On the other hand, to obtain stable and accurate calibration of individual sensors in the constellation, as well as precise calibration of the entire constellation to ensure uniform, consistent, and spatiotemporally continuous measurements are some of the main challenges in utilizing such constellations. To address these challenges, the proposed research will develop a framework called “Adaptive Calibration of CUbesat Radiometer Constellations (ACCURACy)” to calibrate CubeSat radiometer constellations in real-time via a novel approach by considering constellations as single systems to calibrate in their entirety.

The lunar surface consists of a regolith layer which covers the underlying bedrock. Understanding thermal, physical, and chemical properties of lunar regolith and bedrock is very important to reveal geologic features of the Moon, discover potential natural resources that humans can exploit through future lunar missions, and obtain information regarding the history of the solar system. Because of the high cost, logistical challenges and extreme environmental conditions associated with lunar exploration, remote sensing is the most suitable approach for surveying such properties. More specifically, microwave radiometers, due to high sensitivity of their measurements to thermal, physical and chemical properties of lunar regolith and bedrock (such as subsurface temperature and heat flux, density, and FeO and TiO2 abundance), can be utilized to accurately estimate important regolith and bedrock parameters.

Remote sensing of Lunar regolith and bedrock through microwave radiometry, however, requires development of an accurate forward microwave emission model. Several models for microwave emissions from the lunar surface have been developed and are available in the literature. However, many important factors such as volume scatterings, roughness of bedrock and regolith layers, density fluctuations within the regolith, and coherent wave interferences have been ignored in these models. This research, therefore, aims to develop a new forward microwave emission model based on the Dense Media Radiative Transfer theory which takes many of the aforementioned factors into account by leveraging extensive studies conducted for Earth remote sensing applications. The model will be tuned using measurements performed by Chinese Chang'E-1 and Chang'E-2 microwave radiometers, in-situ data collected during Apollo missions, and auxiliary data provided by other Lunar remote sensing instruments such as Lunar Reconnaissance Orbiter and Clementine. Finally, the developed model will be coded as a software tool and released for public use.

The overarching goal of this project is to characterize Antarctic firn layers in terms of their thickness, physical temperature, density, and grain size through multi-frequency microwave radiometer measurements from space. Electromagnetic penetration depth changes with frequency in ice; thus, multi-frequency radiometers are able to profile firn layer properties versus depth. To achieve its objective, the project will utilize the Global Precipitation Measurement (GPM) satellite constellation as a single multi-frequency microwave radiometer system with 11 frequency channels observing the Antarctic Ice Sheet. Archived in-situ measurements of Antarctic firn density, grain size, temperature, and layer thickness will be collected and separated into training and test datasets. Microwave emissions simulated using the training data will be compared to GPM constellation measurements to evaluate and improve state-of-the-art forward microwave emission models. Based on these models, the project will develop numerical retrieval algorithms for the thermal and physical properties of Antarctic firn. Results of retrievals will be validated using the test dataset, and uncertainty and error analyses will be conducted. Lastly, changes in the thermal and physical characteristics of Antarctic firn will be examined through long-term retrieval studies exploiting GPM constellation measurements.

The research aimed to develop a computational basis for the Ensemble Detection Theory which is a transformative, cross-cutting information technology that addresses measurement and understanding of naturally occurring random events. It is a noise-assisted data analysis technique whereby ensemble sets for random processes are produced by mixing their realizations with calibrated noise, and these sets are admissible to statistical analyses that are otherwise not possible to implement with single realizations. This is particularly important to circumvent the engineering and scientific challenges posed by non-stationary processes for which current statistical tools, such as spectrograms, Allan variances, wavelet-based approaches, and Hilbert-Huang Transforms, do not provide sufficient numerical descriptions.

Results: Calibration measurements during radiometer calibration provide an ensemble set of the random process defining radiometer gain. During this project, the “Ensemble Detection” technique was developed and it can be used to extract radiometer gain properties. In stationary Gaussian radiometer gain, the ensemble set reveals an analytical relationship between the statistical properties of the radiometer and the observable uncertainty in the calibrated antenna temperature. For non-stationary gain, the ensemble set can be used to model the gain as a Gaussian process and to find its standard deviation. It was found that statistical properties of the equivalent Gaussian gain process depend on the calibration structure and times for when the calibration reference targets are observed (1). Please see the publications below for more details.

Publications

1. M. Aksoy, P. E. Racette and J. W. Bradburn, "Analysis of Non-Stationary Radiometer Gain Via Ensemble Detection," IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan, 2019, pp. 8893-8896.
2. M. Aksoy, H. Rajabi, P. E. Racette and J. Bradburn, "Analysis of Nonstationary Radiometer Gain Using Ensemble Detection," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 13, pp. 2807-2818, 2020, doi: 10.1109/JSTARS.2020.2993765.

Presentations

1. Bradburn, J.; Aksoy, M.; Racette, P., “Microwave Radiometer Gain Characterization via Ensemble Analysis,” abstract presented at the 16th Specialist Meeting on Microwave Radiometry and Remote Sensing of the Environment, 2020.
2. Aksoy, M.; Racette, P.E., “Ensemble Detection Analysis for Characterizing Non-Stationary Processes,” abstract presented at URSI National Radio Science Meeting, Jan 2018, Boulder, CO.
3. Aksoy, M.; Racette, P.E., “Ensemble Detection Analysis in Space-borne Doppler Measurements,”abstract presented at URSI National Radio Science Meeting, Jan 2018, Boulder, CO.

properties of the Greenland Ice Sheet (GIS) such as subsurface temperature, density, and grain size can be profiled versus depth using wideband microwave radiometry.

Results: The following publications were published on the subject of this project and our lab members also presented at the conferences below. We are also conducting further studies on this topic in our current project, Characterization of the Antarctic Firn, where we use a similar approach to profile the properties of the polar firn and retrieve them.

Publications

1. Jezek, K.; Johnson, J.; Tan, S.; Tsang, L.; Andrews, M.; Brogioni, M.; Macelloni, G.; Durand, M.; Chen, C.; Belgiovane, D.; Duan, Y.; Yardim, C.; Li, H.; Bringer, A.; Leuski, V.; Aksoy, M., “500-2000 MHz Brightness-Temperature Spectra of the Northwestern Greenland Ice Sheet,” Geoscience and Remote Sensing, IEEE Transactions on, vol. 56, no. 3, pp.1485-1496, Mar. 2018. doi: 10.1109/TGRS.2017.2764381
2. M. Aksoy, R. Kar, P. Sugumar and P. Atrey, "Multi-Frequency Passive Remote Sensing of ICE Sheets from L-Band to W-Band,"IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium, 2020, pp. 2995-2998, doi: 10.1109/IGARSS39084.2020.9324374.
3. Tan, S.; Aksoy, M.; Brogioni, M.; Macelloni, G.; Durand, M.; Jezek, K.; Wang, T.; Tsang, L.; Johnson, J.T.; Drinkwater, M.; Brucker, L., “Physical Models of Layered Polar Firn Brightness Temperatures from 0.5GHz to 2GHz ,” Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of , vol. 8, no. 7, pp. 3681-3691, July 2015. doi: 10.1109/JSTARS.2015.2403286
4. Aksoy, M., “Retrieval of Near-Surface Ice Sheet Properties Using the Global Precipitation Measurement (GPM) Radiometer Constellation,” 2018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, 2018, pp. 5161-5164. doi: 10.1109/IGARSS.2018.8518913

Presentations

1. Aksoy, M.; Atrey, P.; Sugumar, P.; Bradburn J., “Passive Microwave Response of the Antarctic and Greenland Ice Sheets,” abstract presented at the 16th Specialist Meeting on Microwave Radiometry and Remote Sensing of the Environment, 2020
2. Kar, R.; Aksoy, M.; Devadason, J. A.; Atrey, P., “Potential of the Global Precipitation Measurement Constellation for Characterizing the Polar Firn,” abstract presented at URSI National Radio Science Meeting, Jan 2021, Boulder, CO.

Kar, R.; Aksoy, M.; Kaurejo, D.; Atrey, P.; Devadason, J.A. Antarctic Firn Characterization via Wideband Microwave Radiometry. Remote Sens. 2022, 14, 2258. https://doi.org/10.3390/rs14092258

1. Aksoy M, Rajabi H, Atrey P, Mohamed Nazar I. "Characteristics of the Global Radio Frequency Interference in the Protected Portion of L-Band". Remote Sensing. 2021; 13(2):253. https://doi.org/10.3390/rs13020253
2. J. T. Johnson et al., "Microwave Radiometry at Frequencies From 500 to 1400 MHz: An Emerging Technology for Earth Observations," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 14, pp. 4894-4914, 2021, doi: 10.1109/JSTARS.2021.3073286
3. Mohamed Nazar, I.; Aksoy, M. Radio Frequency Interference Detection in Microwave Radiometry Using Support Vector Machines. Radio Science Letters, 2020.
4. M. Aksoy, H. Rajabi, P. E. Racette and J. Bradburn, "Analysis of Nonstationary Radiometer Gain Using Ensemble Detection," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 13, pp. 2807-2818, 2020, doi: 10.1109/JSTARS.2020.2993765.
5. K. J. Coakley, J. Splett, D. Walker, M. Aksoy and P. Racette, "Microwave Radiometer Instability Due to Infrequent Calibration," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 13, pp. 3281-3290, 2020, doi: 10.1109/JSTARS.2020.2984004.
6. Aksoy, M.; Racette, P. E.. "A Preliminary Study of Three-Point Onboard External Calibration for Tracking Radiometric Stability and Accuracy". Remote Sensing, 2019; 11(23), 2790
7. Rajabi, H.; Aksoy, M, “Characteristics of the L-Band Radio Frequency Interference Environment Based on SMAP Radiometer Observations,” Geoscience and Remote Sensing Letters, IEEE, 2019. doi: 10.1109/LGRS.2019.2907221
8. Jezek, K.; Johnson, J.; Tan, S.; Tsang, L.; Andrews, M.; Brogioni, M.; Macelloni, G.; Durand, M.; Chen, C.; Belgiovane, D.; Duan, Y.; Yardim, C.; Li, H.; Bringer, A.; Leuski, V.; Aksoy, M., “500-2000 MHz Brightness-Temperature Spectra of the Northwestern Greenland Ice Sheet,” Geoscience and Remote Sensing, IEEE Transactions on, vol. 56, no. 3, pp.1485-1496, Mar. 2018. doi: 10.1109/TGRS.2017.2764381
9. Mohammed, P.N.; Aksoy, M.; Piepmeier, J.R.; Johnson, J.T.; Bringer, A., “SMAP L-band Microwave Radiometer: RFI Mitigation Pre-Launch Analysis and First Year On-Orbit Observations,” Geoscience and Remote Sensing, IEEE Transactions on, vol. 54, no. 10, pp.6035-6047, Oct. 2016. doi: 10.1109/TGRS.2016.2580459.
10. Aksoy, M.; Johnson, J.T.; Misra, S.; Colliander, A.; O’Dwyer, I., “L-Band Radio Frequency Interference Observations during the SMAP Validation Experiment 2012,” Geoscience and Remote Sensing, IEEE Transactions on, vol. 54, no. 3, pp.1323-1335, Mar. 2016. doi: 10.1109/TGRS.2015.2477686
11. Tan, S.; Aksoy, M.; Brogioni, M.; Macelloni, G.; Durand, M.; Jezek, K.; Wang, T.; Tsang, L.; Johnson, J.T.; Drinkwater, M.; Brucker, L., “Physical Models of Layered Polar Firn Brightness Temperatures from 0.5GHz to 2GHz ,” Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of , vol. 8, no. 7, pp. 3681-3691, July 2015. doi: 10.1109/JSTARS.2015.2403286
12. Jezek, K.C.; Johnson, J.T.; Drinkwater, M.R.; Macelloni, G.; Leung Tsang; Aksoy, M.; Durand, M., “Radiometric Approach for Estimating Relative Changes in Intraglacier Average Temperature,” Geoscience and Remote Sensing, IEEE Transactions on , vol.53, no.1, pp.134,143, Jan. 2015. doi: 10.1109/TGRS.2014.2319265
13. Piepmeier, J.R.; Johnson, J.T.; Mohammed, P.N.; Bradley, D.; Ruf, C.; Aksoy, M.; Garcia, R.; Hudson, D.; Miles, L.; Wong, M., “Radio-Frequency Interference Mitigation for the Soil Moisture Active Passive Microwave Radiometer,” Geoscience and Remote Sensing, IEEE Transactions on , vol.52, no.1, pp.761,775, Jan. 2014. doi: 10.1109/TGRS.2013.2281266
14. Aksoy, M.; Johnson, J.T., “A Comparative Analysis of Low-Level Radio Frequency Interference in SMOS and Aquarius Microwave Radiometer Measurements,” Geoscience and Remote Sensing, IEEE Transactions on , vol.51, no.10, pp.4983,4992, Oct. 2013. doi: 10.1109/TGRS.2013.2266278
15. Aksoy, M.; Johnson, J.T., “A Study of SMOS RFI Over North America,” Geoscience and Remote Sensing Letters, IEEE , vol.10, no.3, pp.515,519, May 2013. doi: 10.1109/LGRS.2012.2211993

1. I. M. Nazar and M. Aksoy, "Radio Frequency Interference Detection in Microwave Radiometry Using Density Based Spatial Clustering," 2021 XXXIVth General Assembly and Scientific Symposium of the International Union of Radio Science (URSI GASS), 2021, pp. 1-4, doi: 10.23919/URSIGASS51995.2021.9560303.
2. R. Kar, M. Aksoy, J. A. Devadason and P. Atrey, "Potential of the Global Precipitation Measurement Constellation for Characterizing the Polar Firn," 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, 2021, pp. 5607-5610, doi: 10.1109/IGARSS47720.2021.9553923.
3. J. W. Bradburn, H. R. Ashley and M. Aksoy, "Accuracy: A Novel Approach to Calibrate Cubesat Radiometer Constellations," 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, 2021, pp. 996-999, doi: 10.1109/IGARSS47720.2021.9554829.
4. J. W. Bradburn, M. Aksoy and H. R. Ashley, "ACCURACy: Adaptive Calibration of CUbesat RAdiometer Constellations," 2021 United States National Committee of URSI National Radio Science Meeting (USNC-URSI NRSM), 2021, pp. 96-97, doi: 10.23919/USNC-URSINRSM51531.2021.9336481.
5. M. Aksoy and J. W. Bradburn, "Accuracy: Adaptive Calibration of Cubesat Radiometer Constellations," IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium, 2020, pp. 6357-6360, doi: 10.1109/IGARSS39084.2020.9324393.
6. M. Aksoy, R. Kar, P. Sugumar and P. Atrey, "Multi-Frequency Passive Remote Sensing of ICE Sheets from L-Band to W-Band,"IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium, 2020, pp. 2995-2998, doi: 10.1109/IGARSS39084.2020.9324374.
7. M. Aksoy and H. Rajabi, "Characteristics of Radio Frequency Interference in the Protected Portion of L-Band," IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan, 2019, pp. 4539-4542.
8. Nazar, I. M.; Aksoy, M. Radio Frequency Interference Detection in Microwave Radiometry Using Support Vector Machines. Proceedings of the XXXIIIrd URSI General Assembly in Rome, 2020, (virtual)
9. Aksoy, M.; Walter, I.; Hollibaugh Baker, D. M.; Piepmeier, J. R.. Impact of Water Ice Presence in Lunar Regolith on Surface Brightness Temperatures from 1 to 10 GHz. LPI Contributions, 2020, 2241, 5125.
10. M. Aksoy, P. E. Racette and J. W. Bradburn, "Analysis of Non-Stationary Radiometer Gain Via Ensemble Detection," IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan, 2019, pp. 8893-8896.
11. Aksoy, M., “Retrieval of Near-Surface Ice Sheet Properties Using the Global Precipitation Measurement (GPM) Radiometer Constellation,” 2018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, 2018, pp. 5161-5164. doi: 10.1109/IGARSS.2018.8518913
12. Aksoy, M., “Evolution of the Radio Frequency Interference Environment Faced by Earth Observing Microwave Radiometers in C and X Bands Over Europe,” IGARSS 2018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, 2018, pp. 1226-1229. doi: 10.1109/IGARSS.2018.8518807
13. Aksoy, M.; Racette, P.E., “Tracking Radiometer Calibration Stability Using Three-Point Onboard Calibration,” 2018 IEEE 15th Specialist Meeting on Microwave Radiometry and Remote Sensing of the Environment (MicroRad), Cambridge, MA, USA, 2018, pp. 1-4. doi: 10.1109/MICRORAD.2018.8430710
14. Aksoy, M.; Racette P. E., ”Tracking Calibration Stability in Climate Monitoring Microwave Radiometers using On-board 3-Point Calibration,” 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Fort Worth, TX, 2017, pp. 2118-2120. doi: 10.1109/IGARSS.2017.8127402
15. Johnson, J. T.; Mohammed P.N.; Piepmeier J.R.; Bringer, A.; Aksoy, M., "Soil Moisture Active Passive (SMAP) microwave radiometer radio-frequency interference (RFI) mitigation: Algorithm updates and performance assessment," 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Beijing, China, 2016, pp. 123-124. doi: 10.1109/IGARSS.2016.7729022
16. Johnson, J. T.; Jezek, K. C.; Aksoy, M.; Bringer, A.; Yardim, C.; Andrews, M.; Chen, C. C.; Belgiovane, D.; Leuski, V.; Durand, M., Duan; Y., Macelloni, G.; Brogioni, M.; Tan, S.; Wang, T. L.; Tsang, L., "The Ultra-wideband Software-Defined Radiometer (UWBRAD) for ice sheet internal temperature sensing: Results from recent observations," 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Beijing, China, 2016, pp. 7085-7087. doi: 10.1109/IGARSS.2016.7730848
17. Duan, Y.; Durand, M.; Jezek, K.; Yardim, C.; Bringer, A.; Aksoy, M.; Johnson, J., "Testing the feasibility of a bayesian retrieval of greenland ice sheet internal temperature from ultra-wideband software-defined microwave radiometer (UWBRAD) measurements," 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Beijing, China, 2016, pp. 7092-7093. doi: 10.1109/IGARSS.2016.7730850
18. Aksoy, M.; Johnson, J.T.; Misra, S., “Radio frequency interference observations using an L-Band direct sampling receiver during the SMAPVEX12 airborne campaign,” Geoscience and Remote Sensing Symposium (IGARSS), 2014 IEEE International , vol., no., pp.219,222, 13-18 July 2014. doi: 10.1109/IGARSS.2014.6946396
19. Aksoy, M.; Johnson, J.T.; Jezek, K.C.; Durand, M.; Drinkwater, M.; Macelloni, G.; Leung Tsang, “An examination of multi-frequency microwave radiometry for probing subsurface ice sheet temperature,”Geoscience and Remote Sensing Symposium (IGARSS), 2014 IEEE International , vol., no., pp.3614,3617, 13-18 July 2014. doi: 10.1109/IGARSS.2014.6947265
20. Macelloni, G.; Brogioni, M.; Aksoy, M.; Johnson, J.T.; Jezek, K.C.; Drinkwater, M.R., “Understanding SMOS data in Antarctica,” Geoscience and Remote Sensing Symposium (IGARSS), 2014 IEEE International , vol., no., pp.3606,3609, 13-18 July 2014. doi: 10.1109/IGARSS.2014.6947263
21. Bradley, D.; Morris, J.M.; Adali, T.; Johnson, J.T.; Aksoy, M., “On the detection of RFI using the complex signal kurtosis in microwave radiometry,” Microwave Radiometry and Remote Sensing of the Environment (MicroRad), 2014 13th Specialist Meeting on , vol., no., pp.33,38, 24-27 March 2014. doi: 10.1109/MicroRad.2014.6878903
22. Misra, S.; Johnson, J.; Aksoy, M.; Jinzheng Peng; Bradley, D.; O’Dwyer, I.; Padmanabhan, S.; Dawson, D.; Chazanoff, S.; Latham, B.; Gaier, T.; Flores-Helizon, C.; Denning, R., “SMAP RFI mitigation algorithm performance characterization using airborne high-rate direct-sampled SMAPVEX 2012 data,” Geoscience and Remote Sensing Symposium (IGARSS), 2013 IEEE International , vol., no., pp.41,44, 21-26 July 2013. doi: 10.1109/IGARSS.2013.6721087
23. Aksoy, M.; Johnson, J.; Misra, S.; O’Dwyer, I., “RFI characterization for SMAP using L-band direct sampled data obtained during the SMAPVEX12 airborne campaign,” Radio Science Meeting (USNC-URSI NRSM), 2013 US National Committee of URSI National , vol., no., pp.1,1, 9-12 Jan. 2013. doi: 10.1109/USNC-URSI-NRSM.2013.6524989
24. Misra, S.; Johnson, J.; Aksoy, M.; Bradley, D.; Hsin Li; Mederios, J.; Piepmeier, J.; O’Dwyer, I., “Performance characterization of the SMAP RFI mitigation algorithm using direct-sampled SMAPVEX 2012 data,” Radio Science Meeting (USNC-URSI NRSM), 2013 US National Committee of URSI National , vol., no., pp.1,1, 9-12 Jan. 2013. doi: 10.1109/USNC-URSI-NRSM.2013.6524988
25. Aksoy, M.; Park, J.; Johnson, J.T., “Joint analysis of radio frequency interference from SMOS measurements and from airborne observations,” General Assembly and Scientific Symposium, 2011 XXXth URSI , vol., no., pp.1,4, 13-20 Aug. 2011. doi: 10.1109/URSIGASS.2011.6050795
26. Johnson, J.T.; Aksoy, M., “Studies of radio frequency interference in SMOS observations,” Geoscience and Remote Sensing Symposium (IGARSS), 2011 IEEE International , vol., no., pp.4210,4212, 24-29 July 2011. doi: 10.1109/IGARSS.2011.6050159

1. Nazar, I. M.; Aksoy, M., “Detection of Radio Frequency Interference in Microwave Radiometry using a Supervised Classification Method,” abstract presented at URSI National Radio Science Meeting, Jan 2021, Boulder, CO.
2. Kar, R.; Aksoy, M.; Devadason, J. A.; Atrey, P., “Potential of the Global Precipitation Measurement Constellation for Characterizing the Polar Firn,” abstract presented at URSI National Radio Science Meeting, Jan 2021, Boulder, CO.
3. Coakley, K. J.; Splett, J.; Walker, D.; Aksoy, M.; Racette, P., “Microwave radiometer instability due to infrequent calibration,” abstract presented at the Characterization and Radiometric Calibration for Remote Sensing (CALCON) annual meeting, 2020.
4. Aksoy, M.; Atrey, P.; Sugumar, P.; Bradburn J., “Passive Microwave Response of the Antarctic and Greenland Ice Sheets,” abstract presented at the 16th Specialist Meeting on Microwave Radiometry and Remote Sensing of the Environment, 2020
5. Bradburn, J.; Aksoy, M.; Racette, P., “Microwave Radiometer Gain Characterization via Ensemble Analysis,” abstract presented at the 16th Specialist Meeting on Microwave Radiometry and Remote Sensing of the Environment, 2020.
6. Aksoy, M.; Walter, I.; Baker, D. H.; Piepmeier, J. R., “Characterization of Lunar Regolith via Passive Remote Sensing in Microwave Spectrum from 1 to 10 GHz,” abstract presented at the 51st Lunar and Planetary Science Conference, 2020.
7. Duan, Y.; Durand, M. T.; Jezek, K. C.; Yardim, C.; Bringer, A.; Aksoy, M.; Johnson, J. T., “Feasibility of Estimating Ice Sheet Internal Temperatures Using Ultra-Wideband Radiometric Measurements,” abstract presented at the American Geophysical Union Fall Meeting 2019, December 2019, San Francisco, CA.
8. Duan, Y.; Durand, M T.; Jezek, K C.; Yardim, C.; Bringer, A.; Aksoy, M.; Johnson, J T., “A Bayesian Retrieval of Greenland Ice Sheet Internal Temperature from Ultra-wideband Software-defined Microwave Radiometer (UWBRAD) Measurements,” abstract presented at the American Geophysical Union Fall Meeting 2018, December 2018, Washington, DC
9. Aksoy, M., “Wideband Microwave Radiometry for Remote Sensing of Lunar Regolith and Bedrock,” abstract presented at the American Geophysical Union Fall Meeting 2018, December 2018, Washington, DC.
10. Aksoy, M.; Racette, P.E., “Tracking Radiometer Calibration Stability Using Three-Point Onboard Calibration,” abstract presented at the 15th Specialist Meeting on Microwave Radiometry and Remote Sensing of the Environment, March 2018, Cambridge, MA.
11. Aksoy, M.; Racette, P.E., “Ensemble Detection Analysis for Characterizing Non-Stationary Processes,” abstract presented at URSI National Radio Science Meeting, Jan 2018, Boulder, CO.
12. Aksoy, M.; Racette, P.E., “Ensemble Detection Analysis in Space-borne Doppler Measurements,”abstract presented at URSI National Radio Science Meeting, Jan 2018, Boulder, CO.
13. Duan, Y.; Durand, M T.; Jezek, K C.; Yardim, C.; Bringer, A.; Aksoy, M.; Johnson, J T., “A Bayesian Retrieval of Greenland Ice Sheet Internal Temperature from Ultra-wideband Software-defined Microwave Radiometer (UWBRAD) Measurements,” abstract presented at 2017 Fall Meeting, AGU, New Orleans, Louisiana, 11-15 Dec.
14. Duan, Y.; Durand, M T.; Jezek, K C.; Yardim, C.; Bringer, A.; Aksoy, M.; Johnson, J T., “A Bayesian Retrieval of Greenland Ice Sheet Internal Temperature from Ultra-wideband Software-defined Microwave Radiometer (UWBRAD) Measurements,” abstract presented at 2016 Fall Meeting, AGU, San Francisco, California, 11-15 Dec.
15. Duan, Y.; Durand, M T.; Jezek, K C.; Yardim, C.; Bringer, A.; Aksoy, M.; Johnson, J T., “A Bayesian Retrieval of Greenland Ice Sheet Internal Temperature from Ultra-wideband Software-defined Microwave Radiometer (UWBRAD) Measurements,” abstract presented at 2015 Fall Meeting, AGU, San Francisco, Calif., 14-18 Dec..
16. Bringer, A.; Johnson, J.; Jezek, K. C.; Durand, M.; Duan, Y.; Aksoy, M.; Macelloni, G.; Brogioni, M.; Brucker, L.; Tan, S.; Drinkwater, M.; Tsang, L., “Ultra-wideband Radiometry for Internal Ice Sheet Temperature Measurements: Modeling and Experiments,” abstract presented at 2015 Fall Meeting, AGU, San Francisco, Calif., 14-18 Dec.
17. Jezek, K.; Johnson, J.T.; Durand, M.; Aksoy, M.; Tsang, L.; Wang, T.; Tan. S.; Macelloni, G.; Brogioni, M.; Drinkwater, M., “Ice Sheet Thermometry Using Wideband Radiometry,” abstract presented at 2014 American Geophysical Union’s Fall Meeting, December 2014, San Francisco, CA.
18. Aksoy, M.; Johnson, J.T., “The Ultrawideband Software-Defined Microwave Radiometer,” abstract presented at 2014 Earth Science Technology Forum, October, 2014, Leesburg, VA.
19. Aksoy, M.; Johnson, J.T.; Piepmeier, J.; Mohammed, P., “A Study of Radio Frequency Interference Detection for the SMAP Radiometer,” abstract presented at the 13th Specialist Meeting on Microwave Radiometry and Remote Sensing of the Environment, March 2014, Pasadena, CA.
20. Aksoy, M.; Johnson, J.T.; Jezek, K., “Remote Sensing of Ice Sheet Subsurface Temperatures,” abstract presented at the 13th Specialist Meeting on Microwave Radiometry and Remote Sensing of the Environment, March 2014, Pasadena, CA.
21. Jezek, K.; Johnson, J.T.; Aksoy, M., “Radiometric Approach for Estimating Relative Changes in Intra-Glacier Average Temperature,” abstract presented at 2012 American Geophysical Union’s Fall Meeting, December 2012, San Francisco, CA
22. Johnson, J.T; Aksoy, M., “A Study of Radio Frequency Interference for Current and Future L Band Microwave Radiometry Missions,” abstract presented at 2012 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), July 2012, Munich, Germany.
23. Aksoy, M.; Johnson, J.T.,“Radio Frequency Interference Analysis of L-Band Microwave Radiometry Missions,” abstract presented at URSI National Radio Science Meeting, Jan 2012, Boulder, CO.
24. Aksoy, M.; Johnson, J.T., “Statistical Analysis of SMOS Radio Frequency Interference,“ abstract presented at URSI National Radio Science Meeting, Jan 2011, Boulder, CO.

1. Aksoy, M.; Johnson J. T., “Microwave Radiometry for Earth Observations: Science Goals, Instrument Design, and Spectrum Access,” National Radio Dynamic Zones Workshop. (March 2021). Invited talk
2. Aksoy, M., “A New Age of Discovery: This Time from Space,” University at Albany, The Division for Research. (November 2020). Invited talk.
3. Aksoy, M., “Adaptive Calibration of CubeSat Radiometer Constellations (ACCURACy),” NASA Langley Research Center. (September 2020). Invited talk.
4. Aksoy, M., "Microwave Radiometry for Earth and Space Observations," Union College - Electrical, Computer and Biomedical Engineering Department Seminars, Union College, Schenectady, NY. (January 2020). Invited talk.
5. Aksoy, M., "Recent Studies in Passive Microwave Remote Sensing," University at Albany Physics Department, University at Albany, SUNY, Albany, NY. (October 2018). Invited talk.