Analysis of ice-sheet temperature profiles from low-frequency airborne remote sensing

Abstract Ice internal temperature and basal geothermal heat flux (GHF) are analyzed along a study line in northwestern Greenland. The temperatures were obtained from a previously reported inversion of airborne microwave brightness-temperature spectra. The temperatures vary slowly through the upper ice sheet and more rapidly near the base increasing from ~259 K near Camp Century to values near the melting point near NorthGRIP. The flow-law rate factor is computed from temperature data and analytic expressions. The rate factor increases from ~1 × 10−8 to 8 × 10−8 kPa−3 a−1 along the line. A laminar flow model combined with the depth-dependent rate factor is used to estimate horizontal velocity. The modeled surface velocities are about a factor of 10 less than interferometric synthetic aperture radar (InSAR) surface velocities. The laminar velocities are fitted to the InSAR velocities through a factor of 8 enhancement of the rate factor for the lower 25% of the column. GHF values retrieved from the brightness temperature spectra increase from ~55 to 84 mW m−2 from Camp Century to NorthGRIP. A strain heating correction improves agreement with other geophysical datasets near Camp Century and NEEM but differ by ~15 mW m−2 in the central portion of the profile

The sensitivity of satellite microwave observations to liquid water in the Antarctic snowpack

Surface melting on the Antarctic Ice Sheet has been monitored by satellite microwave radiometry for over 40 years. Despite this long perspective, our understanding of the microwave emission from wet snow is still limited, preventing the full exploitation of these observations to study supraglacial hydrology. Using the Snow Microwave Radiative Transfer (SMRT) model, this study investigates the sensitivity of microwave brightness temperature to snow liquid water content at frequencies from 1.4 to 37 GHz. We first determine the snowpack properties for eight selected coastal sites by retrieving profiles of density, grain size and ice layers from microwave observations when the snowpack is dry during wintertime. Second, a series of brightness temperature simulations is run with added water. The results show that (i) a small quantity of liquid water (≈0.5 kg m−2) can be detected, but the actual quantity cannot be retrieved out of the full range of possible water quantities; (ii) the detection of a buried wet layer is possible up to a maximum depth of 1 to 6 m depending on the frequency (6–37 GHz) and on the snow properties (grain size, density) at each site; (iii) surface ponds and water-saturated areas may prevent melt detection, but the current coverage of these waterbodies in the large satellite field of view is presently too small in Antarctica to have noticeable effects; and (iv) at 1.4 GHz, while the simulations are less reliable, we found a weaker sensitivity to liquid water and the maximal depth of detection is relatively shallow (&lt;10 m) compared to the typical radiation penetration depth in dry firn (≈1000 m) at this low frequency. These numerical results pave the way for the development of improved multi-frequency algorithms to detect melt intensity and the depth of liquid water below the surface in the Antarctic snowpack.</p

Study of the snow melt–freeze cycle using multi-sensor data and snow modelling

The melt cycle of snow is investigated by combining ground-based microwave radiometric measurements with conventional and meteorological data and by using a hydrological snow model. Measurements at 2000 m a.s.l in the basin of the Cordevole river in the eastern Italian Alps confirm the high sensitivity of microwave emission at 19 and 37 GHz to the snow melt–freeze cycle, while the brightness at 6.8 GHz is mostly related to underlying soil. Simulations of snowpack changes performed by means of hydrological and electromagnetic models, driven with meteorological and snow data, provide additional insight into these processes and contribute to the interpretation of the experimental data

Exploiting the ANN Potential in Estimating Snow Depth and Snow Water Equivalent From the Airborne SnowSAR Data at X- and Ku-Bands

Within the framework of European Space Agency (ESA) activities, several campaigns were carried out in the last decade with the purpose of exploiting the capabilities of multifrequency synthetic aperture radar (SAR) data to retrieve snow information. This article presents the results obtained from the ESA SnowSAR airborne campaigns, carried out between 2011 and 2013 on boreal forest, tundra and alpine environments, selected as representative of different snow regimes. The aim of this study was to assess the capability of X- and Ku-bands SAR in retrieving the snow parameters, namely snow depth (SD) and snow water equivalent (SWE). The retrieval was based on machine learning (ML) techniques and, in particular, of artificial neural networks (ANNs). ANNs have been selected among other ML approaches since they are capable to offer a good compromise between retrieval accuracy and computational cost. Two approaches were evaluated, the first based on the experimental data (data driven) and the second based on data simulated by the dense medium radiative transfer (DMRT). The data driven algorithm was trained on half of the SnowSAR dataset and validated on the remaining half. The validation resulted in a correlation coefficient R ≃ 0.77 between estimated and target SD, a root-mean-square error (RMSE) ≃ 13 cm, and bias = 0.03 cm. ANN algorithms specific for each test site were also implemented, obtaining more accurate results, and the robustness of the data driven approach was evaluated over time and space. The algorithm trained with DMRT simulations and tested on the experimental dataset was able to estimate the target parameter (SWE in this case) with R = 0.74, RMSE = 34.8 mm, and bias = 1.8 mm. The model driven approach had the twofold advantage of reducing the amount of in situ data required for training the algorithm and of extending the algorithm exportability to other test sites

Analyzing and modeling the SMOS spatial variations in the East Antarctic Plateau

International audienceThe SMOS brightness temperature (TB) collected on the East Antarctic Plateau revealed spatial signatures at L-band that have never before been observed when only higher-frequency passive microwave observations were available, and this has opened up a new field of research. Because of the much greater penetration depth, modeling the microwave ice sheet emission requires taking into account not only snow conditions on the surface, but should also include glaciological information. Even if the penetration depth of the L-band is not well known due to the uncertainty on the imaginary part of the ice permittivity, it is likely to be of the order of several hundreds of meters, which means that the temperature of the ice over a depth of nearly 1000 m influences the emission. Over such a depth, the temperature is related to both the surface conditions and to the ice sheet thickness, which in turn depends on the bedrock topography and on other glaciological variables. The present paper aims to provide a thorough theoretical explanation of the observed TB spatial variation close to the Brewster angle at vertical polarization, in order to limit the effect of surface and vertical density variability in the firn. In order to provide reliable inputs to the microwave emission models used for simulating TB data, an in-depth analysis of the temperature profiles was performed by means of glaciological models. The comparison between simulated and observed data over three transects totalling 2000 km in East Antarctica pointed out that, whereas the emission models are capable of explaining the TB spatial variations of several kelvins (0.7 and 2.9 K), they are unable to predict its absolute value correctly. This study also shows that the main limiting factor in simulating low-frequency microwave data is the uncertainty in the currently available imaginary part of the ice permittivity

Melt in Antarctica derived from Soil Moisture and Ocean Salinity (SMOS) observations at L band

International audienceMelt occurrence in Antarctica is derived from L-band observations from the Soil Moisture and Ocean Salinity (SMOS) satellite between the austral summer 2010-2011 and 2017-2018. The detection algorithm is adapted from a threshold method previously developed for 19 GHz passive microwave measurements from the special sensor microwave imager (SSM/I) and special sensor microwave imager sounder (SSMIS). The comparison of daily melt occurrence retrieved from 1.4 and 19 GHz observations shows an overall close agreement, but a lag of few days is usually observed by SMOS at the beginning of the melt season. To understand the difference, a theoretical analysis is performed using a microwave emission radiative transfer model. It shows that the sensitivity of 1.4 GHz signal to liquid water is significantly weaker than at 19 GHz if the water is only present in the uppermost tens of centimetres of the snowpack. Conversely, 1.4 GHz measurements are sensitive to water when spread over at least 1 m and when present in depths up to hundreds of metres. This is explained by the large penetration depth in dry snow and by the long wavelength (21 cm). We conclude that SMOS and higher-frequency radiometers provide interesting complementary information on melt occurrence and on the location of the water in the snowpack