International audience

Recent updates in the use of 0.5-2 GHz microwave radiometry for remotely sensing Earth’s cryosphere are reported. These updates concern measurements and modeling of the spectral properties of brightness temperatures for geophysical regions such as ice sheets, sea ice, ice shelves, and others. The results are intended to support the continued development of 0.5-2 GHz passive microwave mission concepts for future deployment in space.

Recent applications of passive L-band observations from space are summarized for ocean, land surface and cryosphere applications. The main limitation of the measurements performed by the current generation of sensors is the spatial resolution. The need of a mission ensuring the continuation of L-band measurements from space with high spatial resolution (10-15 km) is discussed.

A ground-based ultra-wideband radiometer operating at 540, 900, 1380, and 1740 MHz was used to measure microwave thermal emissions from an Arctic sea ice floe as part of the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) Expedition. The instrument was deployed on a drifting ice floe near 86°N, 120°E in leg 1 of the expedition (December 2019) and observed second-year ice (potentially with refrozen melt ponds) that experienced new ice growth at its base over a ten-day period. Measured circularly polarized brightness temperatures were compared with the predictions of a radiative transfer (RT) model for a layered medium consisting of ocean, growing new ice, desalinated remnant second-year ice/refrozen melt pond, and snow layers. Characteristics of the sea ice composition used in the model were determined fromin-situmeasurements. Comparisons of the measured and modeled wideband brightness temperatures showed good agreement consistently over the observation period and for various off-nadir observation angles. The results demonstrate the capabilities of 0.5–2 GHz microwave radiometry for observing sea ice properties and also show the impact of a saline ice layer at the ice bottom on the measured brightness temperature.

Arctic sea-ice thickness and salinity retrievals are simulated to explore the performance of nadir-observing microwave radiometry operating with up to 16 frequency channels in the 0.5–2-GHz frequency range. A radiative transfer model is used to create lookup tables of the circularly polarized thermal emissions of first-year (FY) and multiyear (MY) sea ice, and the performance of two distinct retrieval methods is examined. The first method retrieves only sea-ice thicknesses, while the second retrieves both ice thickness and ice salinity. Retrieval errors are simulated for both FY and MY sea ice as a function of ice thickness, salinity, and temperature to investigate the impact of radiometric uncertainty, the frequency channels used, and any errors in ancillary information. To gain further insight into Arctic scale retrieval performance, a simulated brightness temperature dataset is produced for Arctic sea ice for the period October 2020–March 2021 using sea-ice thicknesses from the SMOS-CryoSat-2 algorithm. Compared to existing sea-ice thickness retrievals obtained from 1.4-GHz microwave radiometers, the results demonstrate that 0.5–2-GHz radiometry can achieve higher sensitivity to a sea-ice thickness within the range 0.5–1.5 m for FY sea ice and enable the retrieval of multiple sea-ice parameters (thickness and salinity) simultaneously.

Sea ice thickness and salinity are important quantities in understanding and forecasting the evolution of the cryosphere. However, the retrieval of multiple sea ice parameters through microwave remote sensing is a challenging task. In this study, the potential advantages of using a multi-channel wideband microwave radiometer are demonstrated through simulations of the retrieval of both sea ice thickness and salinity for multiple ice types and for different numbers of radiometer channels.

Brightness temperature data from wave Imaging Radiometer using Aperture Synthesis (MIRAS) on board the European Space Agency's (ESA) Soil Moisture and Ocean Salinity (SMOS) mission have been used to derive the thickness of thin sea ice for the Arctic freeze-up period. To control the long-term geophysical quality for level 3 SMOS sea ice thickness products we derive a regional extent parameter that can be compared to independent standard ice extent products such as the NSIDC sea ice index. This metric allows to identify first-order quality problems such as data gaps and to observe the evolution of the Arctic sea ice growth in key regions. The regionalized SMOS sea ice thickness extent corresponds in general well with the corresponding NSIDC Sea Ice Index. The occurrence of severe RFI problems has so far mainly been limited to the initial period of the SMOS measurements during the season 2010/2011. Otherwise the comparison does not reveal any significant quality problems of the SMOS sea ice thickness data.

An ultra-wideband radiometer was built and deployed in the Arctic to monitor the microwave emissions of sea ice from a stationary platform on an ice floe. The experiment was performed as part of the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition in the far north of the Laptev Sea. Brightness temperature data were acquired over the spectrum 0.5-2 GHz during the first phase of the expedition. The outcome of the experiment is expected to provide new insights into the advantages of wideband radiometry for the remote sensing of Arctic sea ice properties.

An ultrawideband radiometer was used to measure microwave brightness temperature spectra over Arctic sea ice in the Lincoln Sea near the north coast of Greenland. Spectra over the range of 0.5-2 GHz were compared to thermal infrared images collected during the airborne campaign and also compared to nearly concurrent Sentinel-1 C-band synthetic aperture radar (SAR) data. Based on those comparisons, spectral signatures were associated with thick multiyear ice and thin ice. A radiative transfer (RT) model consisting of a homogeneous slab of sea ice bounded by sea water and air was then used to invert the spectra for sea ice thickness and salinity. Inferred thicknesses were consistent with ice thickness climatology for ice floes in the Lincoln Sea. Salinities are higher than expected which may be a consequence of neglecting surface and volume scattering contributions in the models.

The Ultra-Wideband Software Defined Microwave Radiometer (UWBRAD) measures scene brightness temperatures from 0.5-2 GHz. UWBRAD was deployed in a September 2017 airborne campaign in Greenland, and observed brightness temperatures of the ice sheet as well as sea ice, the ocean surface, and land regions during the transit to and from Calgary, Canada (the aircraft base of operations). This presentation will review the campaign and datasets collected. Spectral features of thermal emissions from the ice sheet and other geophysical regions are also examined to obtain insight into the utility of 0.5-2 GHz thermal emission measurements for remote sensing applications.

After almost 9 years in orbit L band satellite radiometry has demonstrated its impacts and values for a wide range of science and applications. In some cases it has demonstrated its uniqueness for assessing key environmental variables and in many others its high impact.

Earth's cold regions are key elements of the planet's climate system: they have strong feedbacks with global change and they have a direct impact on human activities. Despite their importance, at present they are not adequately monitored by state-of-the-art instruments. In order to fill this gap, a dedicated spaceborne mission called Cryorad has been proposed in the framework of the ESA Earth Explorer 10 call. The mission would comprise a 0.4-2 GHz nadir-looking radiometer installed on a polar-orbit satellite. Scientific and technical studies are underway, as well as experimental campaigns in Greenland and Antarctica.

A new space-borne mission based on a multi-channel microwave radiometer in the frequency range 0.5-2 GHz as been recently approved by the Italian Space Agency for a preliminary concept study. The innovative instrument will be devoted to the study of the Cryosphere and in particular on sea ice volume, ice sheet temperature and soil status. Scientific objectives and first mission concept are presented here and in depth discussed at the conference.

We investigate the potential of a synergetic combination of data from ESA's SMOS and CryoSat-2 mission for sea ice thickness retrieval. SMOS and CryoSat-2 provide complementary information because of their different spatio-temporal sampling and resolution, and because of the complementary uncertainty due to the fundamental difference of the radiometric and altimetric measurement principle. The main limitations of the ice thickness retrieval depend on the emission e-folding depth and the vertical resolution of the effective radar pulse-length, respectively. It is shown that the combination of SMOS and CryoSat-2 considerably reduces the uncertainty with respect to the products derived from the single sensors. The RMS error is reduced from 76 to 66 cm and the squared correlation coefficient increases from 0.47 to 0.61 in comparison to validation data of NASA's Operation IceBridge campaign, 2013. Furthermore, we demonstrate the applicability of the Optimal Interpolation method for the generation of a combined product based on weekly CryoSat-2 averages.

We compare passive microwave (PM)-derived sea ice concentrations (SIC) with more than 21 600 ship-based observations (OBS) of SIC acquired around Antarctica. PM SIC are derived from SSM/I-SSMIS and AMSR-E measurements in 1991-2009 and 2002-2010, respectively, with the ARTIST Sea Ice (ASI), Comiso Bootstrap (BST), NASA-Team (NT), enhanced NASA-Team (NT2), and EUMETSAT OSI-SAF (OSI) algorithm. We compare correlation coefficients (CC), RMSDs, and biases, separately for SSM/I-SSMIS data for algorithms ASI, BST, OSI, and NT, and for AMSR-E data for algorithms ASI, BST, and NT2. With OBS SIC and PM SIC being on fundamentally different spatiotemporal scales, we develop a new colocation approach using daily-average along-ship-track SIC values. CC between OBS SIC and PM SIC agree within their uncertainty for all algorithms and sensors. Year-round CC values are around 0.85 (AMSR-E) and 0.82 (SSM/I); CC values are similar during summer, but drop significantly during winter. Year-round RMSD values range from 13% (BST and OSI) to 17% (NT) for SSM/I and from 12% (BST) to 16% (NT2) for AMSR-E. RMSD values are similar during summer, but decrease for winter (BST: 8% for AMSR-E, 10% for SSM/I). For AMSR-E, biases are below 0.5% for BST and ASI, but between 5% (winter) and 9% (summer) for NT2. For SSM/I, biases are smaller during summer, -0.7% for BST to -7.8% for NT, than winter, -3.6% for BST to -13.9% for NT. Overall, best agreement between OBS and PM SIC is found for BST.

We compare passive microwave-derived sea ice concentrations with ship-based observations of sea ice concentration (OBS). We focus on different retrieval algorithms that are based on Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) measurements. OBS are collected according to the Antarctic Sea Ice Processes and Climate (ASPeCt) program. We assess the quality of the satellite data by calculating correlation coefficient, root-mean-square-deviation (RMSD) and bias with respect to ASPeCt observations of sea ice concentration. All retrieval algorithms compared in this study, Comiso Bootstrap (BST), NASA-Team 2 (NT2) and Artist Sea Ice (ASI), show correlation coefficients above 0.8 for all dates considered (N=313). BST and ASI do not have a significant bias, but NT2 has a bias of 8 %. RMSD values are different for different seasons, but generally, Comiso BST has the lowest RMSD (<;13 %).

The introduced MODIS melt pond fractions and it's corresponding sea ice concentration data set are valuable sources for multiple applications in Climate sciences. Knowledge about the occurrence of melt ponds is essential to properly document the energy balance on the sea ice surface during summer. Additionally, melt pond fractions can be used to quantify the uncertainties in sea ice concentration data derived from passive microwave imagery. As shown in Figure 1, the MODIS melt pond fractions can be used to estimate the influence of melt ponds on the sea ice concentration determination from microwave sensors like AMSR-E. In this example, all AMSR-E algorithms are clearly underestimating MODIS sea ice concentration by around 20-30%. In the final paper a detailed analysis of the uncertainties caused by melt ponds and an assessment of the different sea ice concentration retrieval methods will be given.

A helicopter-based Doppler scatterometer (Multi$^{3}$Scat) is described. It allows simultaneous measurements of the surface radar backscatter at five different frequencies at co- and cross-polarization at incidence angles of 20$^{\circ}$–65$^{\circ}$from an altitude of 30–300 m. Video and infrared (IR) cameras simultaneously sense the surface in the scatterometers' footprint. The Multi$^{3}$Scat is calibrated using measurements carried out over corner reflectors. The stability of the Multi$^{3}$Scat's signal is found to be, on average, better than 0.5 dB. Typical signal-to-noise-ratio values for sigma-0 range between 10 and 20 dB for cross-polarization and between 15 and 25 dB for copolarization over snow and ice surfaces. The potential of the Multi$^{3}$Scat to acquire multifrequency multipolarization radar backscatter data and coincident video and IR temperature observations at different incidence angles over remote terrain such as the Arctic Ocean or the Alps is demonstrated.

A method to retrieve the surface emissivity of sea ice at the window channels of the Advanced Microwave Sounding Unit (AMSU) radiometers in the polar region is presented. The instruments are on the new-generation satellites of the U.S. National Oceanic and Atmospheric Administration (NOAA-15, NOAA-16, and NOAA-17). The method assumes hypothetical surfaces with emissivities zero and one and simulates brightness temperatures at the top of the atmosphere using profiles of atmospheric parameters, e.g., from the European Centre for Medium-Range Weather Forecasts (ECMWF) model runs, as input for a radiative transfer model. The retrieval of surface emissivity is done by combining simulated brightness temperatures with the satellite-measured brightness temperature. The AMSU window channels differ in surface penetration depths and, thus, in the surface microphysical parameters that they depend on. Lowest layer air temperatures from ECMWF are used to infer temperatures of emitting layers at different frequencies of sea ice. A complete yearly cycle of monthly average emissivities in two selected regions (first- and multiyear ice) is giving insight into the variation of emissivities in various development stages of sea ice.

Recent progress in spatial resolution enhancement of sea ice concentrations obtained by microwave remote sensing has been stimulated by two new developments: First, the new sensors AMSR (Advanced Microwave Scanning Radiometer) on MIDORI-II and AMSR-E on AQUA offer horizontal resolutions of 6x4 km at 89 GHz. This is nearly three times the resolution of the standard sensor SSM/I at 85 GHz (15x13 km). The sampling distance at the high frequencies is 12.5 km at SSM/I and 5 km at the AMSR-E instrument. Second, a new algorithm enables the estimation of sea ice concentrations from the channels near 90 GHz, despite the enhanced atmospheric influence in these channels. This allows to fully exploit their horizontal resolution which is two to three times finer than the one of the channels near 19 and 37 GHz. These frequencies are used by the most widespread algorithms for sea ice retrieval, the NASA Team and Bootstrap algorithms. These two developments are combined to determine operationally sea ice concentration maps. The used ASI (Artist Sea Ice) algorithm combines a model for retrieving the sea ice concentration from SSM/I 85 GHz data proposed by Svendsen et al. (1) with an ocean mask derived from the 18-, 23-, and 37-GHz AMSR-E data using two weather filters and the Bootstrap Algorithm. The AMSR-E sea ice concentration data are projected into grids of sampling sizes down to 3 km. Hemispherical and regional maps are provided daily at www. iup.physik.uni-bremen.de.

Sea ice concentrations obtained with two algorithms from Special Sensor Microwave/Imager (SSM/I) data are compared to spaceborne visible/infrared and active microwave imagery for the Greenland Sea in spring. Both algorithms, the ARTIST Sea Ice algorithm (ASI) and the SEA LION algorithm (SLA), utilize 85-GHz SSM/I brightness temperatures with a spatial resolution of 15 km /spl times/13 km. Ice concentrations obtained from Advanced Very High Resolution Radiometer (AVHRR) infrared data in cloud-free areas are underestimated by SLA and ASI ice concentrations by 3.6% and 8.3% (correlation coefficients of 0.90 and 0.91). Ice concentrations estimated from texture classified ERS-2 synthetic aperture radar (SAR) images by assigning experience-based ice concentrations to ice-type classes are overestimated by SLA and ASI ice concentrations by 4.4% and 1.5% (correlation coefficients of 0.84 and 0.77). However, omitting low/high ice concentrations forming up to 80% (AVHRR) and 60% (SAR) of the entire dataset reveals a significantly different statistic. For instance, the correlation between AVHRR and SLA and ASI ice concentrations drops to 0.77 and 0.70, respectively. All presented techniques to obtain ice concentrations need improvement and future developments should involve larger datasets. However, with care, both algorithms can be used to obtain reasonable ice concentration maps with a 12.5 km /spl times/12.5 km grid-cell size.

The possibility to distinguish between different sea ice types in the marginal ice zone in the Greenland Sea using ERS-2 SAR is investigated. Statistical, textural and spectral features derived from ERS-2 SAR images and ice concentrations from 85 GHz SSM/I data are analyzed using learning vector quantization. The proposed method is useful for resolving ambiguities arising from frost flowers or pancake ice with very high backscatter values similar to the wind roughened sea surface.

Sea ice concentration maps retrieved from spaceborne passive microwave sensor data such as from the Special Sensor Microwave Imager (SSM/) often suffer from the coarse spatial resolution compared to synthetic aperture radar (SAR) data. However, SAR images are still difficult to interpret and therefore there is a need to improve the spatial resolution of the above-mentioned sea ice concentration maps. Under certain circumstances this can be achieved using 85 GHz SSM/I data together with the SEALION and the ASI sea ice algorithms.