AN EXAMINATION OF MULTI-FREQUENCY MICROWAVE RADIOMETRY FOR PROBING SUBSURFACE ICE SHEET TEMPERATURES

Mustafa Aksoy1, Joel T. Johnson1, Kenneth C. Jezek2, Michael Durand2, Mark R. Drinkwater3, Giovanni

Macelloni4, and Leung Tsang5

1 ElectroScience Laboratory, The Ohio State University, Columbus, OH 2 School of Earth Sciences & Byrd Polar Research Center, The Ohio State University, Columbus, OH

3 European Space Agency, Noordwijk, The Netherlands 4 IFAC-CNR, Firenze, Italy

5 Department of Electrical Engineering, University of Washington

ABSTRACT Many quantities describing ice sheet dynamics can be measured via remote sensing. However, subsurface ice sheet temperature, a very important parameter which determines in part internal deformation and ice flow, is not currently measured remotely. Direct knowledge is available only through measurements from a small number of boreholes. This paper presents the concept of utilizing microwave radiometry in the 0.5-2GHz frequency band to probe subsurface ice sheet temperatures. Ice sheet geophysical properties and the resulting microwave emission are reviewed, and an initial retrieval algorithm for subsurface ice sheet temperatures is described. Finally an ultra-wideband radiometer design to realize the proposed measurements is summarized.

Index Terms— Microwave Radiometry, Ice Temperature

1. INTRODUCTION Understanding the behavior of polar ice sheets and predicting future changes in their volume and mass are critical to improve understanding of weather, climate, and the water cycle on Earth. Because of the extreme environmental conditions associated with polar ice sheets, airborne and satellite sensors are the most suitable tools for observing geophysical quantities characterizing ice sheets and for the monitoring their temporal and spatial variations. Currently many of these quantities such as ice sheet surface topography, ice surface velocity, mass change, ice sheet thickness, surface accumulation rate, internal stratigraphy and basal geology can be retrieved, with an acceptable accuracy, from data acquired by airborne and spaceborne remote sensing instruments. Internal ice sheet temperature is absent from this list although it is a primary factor in determining internal deformation and ice flow, and thus is crucial for better understanding polar ice sheet dynamics.

Recent studies have demonstrated that microwave radiometry offers the potential to remotely sense internal ice sheet temperatures [1]-[2]. Ice sheets are typically represented as a layered dense medium, with each layer having electrical and physical properties determined by the ice sheet geophysical properties. Using models for ice sheet internal temperatures, electromagnetic permittivity [3], and other physical parameters such as density and particle grain size, the microwave emission from an ice sheet can be computed. The dense media radiative transfer theory for multi-layered media (DMRT-ML, [4]) is an openly available algorithm to calculate the resulting thermal emission as a combination of emission, scattering, and attenuation effects determined by the electrical and physical properties of each layer. Although most previous studies have focused on microwave emission at higher frequencies (i.e. 10-37 GHz), the DMRT-ML approach remains applicable for lower frequencies 0.1-3 GHz as well.

In [5], it is shown that the penetration depth for pure ice (Figure 1) may approach 10km or more at low frequencies and temperatures. Inhomogeneities decrease penetration due to scattering loss, but as the effects of scattering are reduced at lower frequencies, this may still yield effective penetration over depths of up to several km . These results suggest that microwave radiometry at

Figure 1 – Penetration depth for pure ice as a function of

frequency and temperature [5]. Note that penetration depths in pure ice can exceed 10 km at frequencies < 1 GHz.

frequencies ~0.5 - ~ 2 GHz can probe sub-emissions from depths up to a few km, widepth reduced as the frequency is increasedin concept to atmospheric temperature promulti-frequency measurements of brightnenear an atmospheric gas absorption resonasense temperatures at altitudes that vary frequency from the absorption line. While temperatures is complicated by the abseabsorption resonance as well as thinhomogeneities, the clear potential temperature information at varying depthfrequency measurements motivates the anal

2. DMRT MULTI-LAYER SIMUL DMRT-ML studies indicate that brightnessice sheets significantly change with frequencase is presented below based on the theoryFigure 2 plots modeled ice sheet temperatufixed 216oK surface temperature and varyirates from 1 to 6 cm/yr and ice sheet thickn3700 m. These modeled temperature profilethicker ice in East Antarctica given ttemperature selected to be typical of thregion. The thinner ice cases are used primhow physical temperature will influetemperature.

Figure 2 - Sample modeled ice sheet tempfor a fixed surface temperature of (216oK

accumulation rates and ice sheet thicknessmarks 00C.

Ice sheets having these temperaturemodeled as parallel layered media of layerThe ice density in each layer was computedin [6]. The ice grain radius was assumeincreasing between 0.75mm and 3mm in ththe surface, constant between 10m and below 100m as only lower frequencies at wminimal are expected to penetrate to suchpermittivity model of [7] was used, and the

-surface ice sheet ith the sounding-d. This is similar ofiling, in which ess temperatures ance are used to with distance in sensing ice-sheet nce of a strong

he presence of for obtaining

s through multi-lyses performed.

LATIONS

s temperatures of ncy. An example

y described in [1]. ure profiles for a ing accumulation ness from 1500 to es are realistic for the low surface he Lake Vostok

marily to illustrate ence brightness

perature profiles K) and varying . The green line

e profiles were r thickness 10m.

d using the model ed to be linearly he first 10m from

100m and zero which scattering is h depths. The ice e basal layer was

modeled as a flat soil surface at deepest ice layer and with a according to [8]. Figure 3 depicts temperatures at nadiral incidence ve

The higher brightness temperatindicate that at these frequencies layers contribute to the overall therfrequency bands, the absorption ibegins to contribute, so that deepeand the resulting brightness temperplot of Figure 3 compares modewithout inhomogeneities. As expecsignificant only for frequencies abovThe combination of low and higprovides information both on deepescattering effects in shallower lacontribute to observed brightnesses.

Figure 3 - (left) Ice sheet brightnefrequency (at nadir) from DMRT-M

profiles from Figure 1. A 3mm iassumed (3 mm ice grains in air fo

mm air voids in ice for higher densivolumes vary from 40% at the su

depth. (right) Comparison of brightfrequency with and without

3. RETRIEVAL ST

A discrete set of 1600 ice sheet tgenerated by changing the paramtemperature model in [1], as well aand grain size values within phyOther assumptions explained in thpreserved. The brightness temperatu0.1, 0.2, …,3 GHz for each profilcreate a set of 1600 “truth” datase1600 physical temperature profilesbrightness temperature vs. frequenc One hundred simulated radiomeprofile were then created by distoprofile with independent 1K standerror at each frequency. A simselected the “truth” frequency profil

the temperature of the permittivity computed the resulting brightness

ersus frequency. tures at lower frequencies deeper and warmer ice

rmal emission. At higher is greater and scattering er layer effects diminish, rature is lower. The right el predictions with and cted, scattering becomes ve approximately 1 GHz.

gh frequencies therefore er layers as well as on the ayers that may partially .

ess temperatures versus

ML model for temperature inhomogeneity size is or densities

Figure 4 - (left) 1600 Physical ice temperature profiles and

(right) the corrsponding brightness temperatures versus frequency in the 0.1-3GHz frequency band. A single Tb vs

Frequency profile and its distortion is also depicted.

squares sense to each of the 100 simulated measured profiles. This initial retrieval study essentially is a classification problem, but nevertheless provides some initial insight into the expected ability to retrieve internal temperature information.

The results showed that an average of 74% of the trials resulted in selection of the correct frequency profile, and therefore physical temperature profile, of the ice sheet. Figure 5 is a histogram over the 1600 truth cases of the percentage of profiles classified correctly in each case. Further studies with larger and more complete datasets are currently being conducted to expand on this initial effort and to have more reliable results. These studies will also provide more insight into the cases for which correct classification was found difficult.

Figure 5 - Results of the Initial Retrieval Studies. Vertical

axis shows the number of the temperature profiles, horizontal axis indicates the correct classifications (out of

100 trials) for those profiles.

4. SMOS DATA OVER ANTARCTICA Additional motivation for studies of ice sheet subsurface temperature information was obtained from the data of the European Space Agency’s (ESA) Soil Moisture and Ocean Salinity (SMOS) satellite. SMOS is currently in orbit, and operates a 1.4 GHz microwave radiometer that provides global brightness temperature measurements [9]. Although only single frequency brightness temperatures are available from SMOS and the spatial resolution is ~40km, measurements over Antarctica and Greenland show

evidence for the ability of L-band microwave radiometry to provide subsurface information for ice sheets. One example can be seen over Lake Vostok (Figure 6) where a “cold” brightness temperature anomaly is observed over a region corresponding to known location and extent of Lake Vostok. Lake Vostok is a sub-surface Antarctic lake located beneath 3.7 km of ice. Although the reasons of such anomalies are being investigated, they are likely related to the subsurface temperature and layer properties of the ice sheet that are manifested in the 1.4 GHz SMOS measurements. Similar cases have been reported in studies of data from other L-band systems as well [10], and additional analysis of SMOS anomalies is reported in [11]. These datasets further encourage attempts to retrieve subsurface temperatures using a multi-frequency approach.

Figure 6 - Vertically polarized SMOS data at 55o incidence angle averaged over two months for January and February 2012. An anomaly over the Lake Vostok, Antarctica region

(indicated by black lines) is obvious.

5. UWBRAD To implement the measurements discussed in the previous sections, an ultra-wideband “software defined” microwave radiometer (UWBRAD) is under development. The radiometer will have 15x100MHz frequency channels occupying the 0.5-2GHz frequency band. Because the radiometer will operate in unprotected frequency bands, severe radio frequency interference is expected. This interference will be detected and mitigated in real time using time and frequency domain RFI detection algorithms (e.g. pulse detection and cross-frequency methods) as well as statistical techniques (e.g. kurtosis test). To enable the required RFI processing, the entire 100 MHz bandwidth in each frequency channel will be sampled in 16 bit resolution. The radiometer will measure microwave emission in circular polarization at nadir with a 1km spatial resolution and will have an NEDT in each channel of 1 K or better. Table 1 summarizes the technical properties of the proposed design.

UWBRAD has been supported by the NASA Instrument Incubator Program (IIP), and is currently in development. Airborne deployment over borehole sites in Greenland is planned for 2016 to validate temperature measurement capabilities.

200 220 240 260 280

0

1000

2000

3000

temperature(K)

dept

h(m

)Temperature Profiles used in the Retrieval

1000 2000 3000

200

210

220

230

240

Tb(

K)

frequency(Hz)

Simulated Tb vs Freq

Table 1 - Technical properties of UWBRAD

6. CONCLUSIONS

In this paper, the concept of using multi-frequency microwave radiometry to measure subsurface ice sheet temperatures has been proposed as a means to better understand the dynamics of ice sheets on Earth. Simulated results from the DMRT-ML model show the influence of frequency on the ability to detect microwave emission from different depths, and the influence of different profiles in physical properties on the brightness temperature signal. An initial retrieval study was also performed to confirm the potential for remote determination of subsurface temperature information. Moreover, data from SMOS, a currently operational spaceborne radiometer, showed further evidence of the influence of internal ice sheet temperatures. Finally, a software defined multi-frequency radiometer design was suggested to implement the required measurements, which require operation outside the protected traditional microwave radiometer bands.

Improvements and expansions on these initial steps, especially for ice sheet modeling, retrieval algorithms, and the radiometer design are in progress, with a goal of deploying UWBRAD over Greenland in 2016 for a validation campaign.

7. ACKNOWLEDGEMENT

UWBRAD design and development is supported under the NASA 2013 Instrument Incubator Program. Theoretical studies are supported under the NASA Cryosphere Program. SMOS data were provided by ESA.

8. REFERENCES [1] K. Jezek, J. T. Johnson, M. R. Drinkwater, G. Macelloni, L. Tsang, M. Aksoy and M. Durand, “Radiometric Approach for Estimating Relative Changes in Intra-Glacier Average Temperature,” accepted by IEEE Trans. Geosc. Rem. Sens., 2014. [2] K. Jezek, J. T. Johnson and M. Aksoy, “Radiometric Approach for Estimating Relative Changes in Intra-Glacial Average Temperature,” EOS Supplement, 2012 Fall AGU meeting.

[3] S. Fujita, T. Matsouka, T. Ishida, K. Matsouka and S. Mae, “A Summary of Complex Dielectric Permittivity of Ice in the Megahertz Range and Its Applications for Radar Sounding of Polar Ice Sheets,” Physics of Ice Core Records, pp. 185-212. [4] G. Picard, L. Brucker, A. Roy, F. Dupont, M. Fily, and A. Royer, “Simulation of the microwave emission of multi-layered snowpacks using the dense media radiative transfer theory: the DMRT-ML model,” Geosci. Model Dev. Discuss., 5, 3647-3694, 2012. [5] C. Mätzler, “Thermal microwave radiation: applications for remote sensing,” Vol. 52, Institution of Engineering and Technology, ISBN 0863415733, 555p, 2006. [6] M. Drinkwater, N. Floury, and M. Tedesco, "L-band ice-sheet brightness temperatures at Dome C, Antarctica: spectral emission modelling, temporal stability and impact of the ionosphere," Annals of Glaciology, vol. 39, 391-396, 2004. [7] C. Mätzler, and U. Wegmüller, “Dielectric properties of fresh-water ice at microwave frequencies,” J. Phys. D Appl. Phys., 20, 1623–1630, 1987. [8] Wegmuller, U.; Matzler, C., "Rough bare soil reflectivity model," IEEE Transactions on Geoscience and Remote Sensing, vol.37, no.3, pp.1391,1395, May 1999. [9] Kerr Y., P. Waldteufel, J.-P. Wigneron, F. Cabot, J. Boutin, M.-J. Escorihuela, J. Font, N. Reul, C. Gruhier, S. Juglea, S. Delwart, M. Drinkwater, A. Hahne, M. Martin-Neira, S. Mecklenburg, The SMOS mission: new tool for monitoring key elements of the global water cycle, Proceedings of the IEEE, 98(5), 666-687, doi:10.1109/JPROC.2010.2043032, 2010. [10] L. Brucker, E. Dinnat, L. Koenig, “Weekly-gridded Aquarius L-band radiometer/scatterometer observations and salinity retrievals over the polar regions: applications for cryospheric studies,” Cryosphere Discussions,.7(6), 5921-5970. Nov. 2013;. [11] G. Macelloni, M. Brogioni, M. Aksoy, J. T. Johnson, K. C. jezek, M. Drinkwater, “Understanding SMOS data in Antarctica,” to be presented at IGARSS 2014.

Frequency Channels 0.5-2 GHz, 15 x 100 MHz channels Polarization Single (Right-hand circular)

Observation angle Nadir Spatial Resolution 1 km x 1 km (1 km platform altitude) Integration time 100 msec Ant Gain (dB) /Beamwidth

11 dB 30

Calibration (Internal) Reference load and Noise diode sources Calibration (External) Sky and Ocean Measurements

Noise equiv dT 0.4 K in 100 msec (each 100 MHz channel) Interference Management

Full sampling of 100 MHz bandwidth in 16 bits resolution in each channel; real time “software

defined” RFI detection and mitigation Initial Data Rate 700 Megabytes per second (10% duty cycle)

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