ALBERT

Description: The microwave interferometric radiometer of the European Space Agency's Soil Moisture and Ocean Salinity (SMOS) mission measures at a frequency of 1.4 GHz in the L-band. In contrast to other microwave satellites, low frequency measurements in L-band have a large penetration depth in sea ice and thus contain information on the ice thickness. Previous ice thickness retrievals have neglected a snow layer on top of the ice. Here, we implement a snow layer in our emission model and investigate how snow influences L-band brightness temperatures and whether it is possible to retrieve snow thickness over thick Arctic sea ice from SMOS data. We find that the brightness temperatures above snow-covered sea ice are higher than above bare sea ice and that horizontal polarisation is more affected by the snow layer than vertical polarisation. In accordance with our theoretical investigations, the root mean square deviation between simulated and observed horizontally polarised brightness temperatures decreases from 20.0 K to 4.4 K, when we include the snow layer in the simulations. Under cold Arctic conditions we find brightness temperatures to increase with increasing snow thickness. Because dry snow is almost transparent in L-band, this brightness temperature's dependence on snow thickness origins from the thermal insulation of snow and its dependence on the snow layer thickness. This temperature effect allows us to retrieve snow thickness over thick sea ice. For the best simulation scenario and snow thicknesses up to 35 cm, the average snow thickness retrieved from horizontally polarised SMOS brightness temperatures agrees within 0.7 cm with the average snow thickness measured during the IceBridge flight campaign in the Arctic in spring 2012. The corresponding root mean square deviation is 6.3 cm, and the correlation coefficient is r2 = 0.55.

Description: In preparation for the European Space Agency's (ESA) Soil Moisture and Ocean Salinity (SMOS) mission we investigated the potential of L-band (1.4 GHz) radiometery to measure sea ice thickness. Sea ice brightness temperature was measured at 1.4 GHz and ice thickness were measured along nearly coincident flight tracks during the SMOS Sea-Ice campaign in the Bay of Bothnia in March 2007. A research aircraft was equipped with the L-band Radiometer EMIRAD and coordinated with helicopter based electromagnetic induction (EM) ice thickness measurements. We developed a three layer (ocean-ice-atmosphere) dielectric slab model for the calculation of ice thickness from brightness temperature. The dielectric properties depend on the relative brine volume which is a function of the bulk ice salinity and temperature. The model calculations suggest a thickness sensitivity of up to 1.5 m for low-salinity (multi-year or brackish) sea ice. For Arctic first year ice the modeled thickness sensitivity is roughly half a meter. It reduces to a few centimeters for temperatures approaching the melting point. Although the campaign was conducted under such unfavorable melting conditions and despite limited spatial overlap between the L-band and EM-measurements was small we demonstrate a large potential for retrieving the ice thickness in the range of 0.2 to 1.5 m. Furthermore, we show that the ice thickness derived from SMOS measurements would be complementary to ESA's CryoSat-2 mission in terms of the error characteristics and the spatio-temporal coverage.

Description: Following the launch of ESA's Soil Moisture and Ocean Salinity (SMOS) mission, it has been shown that brightness temperatures at a low microwave frequency of 1.4 GHz (L-band) are sensitive to sea ice properties. In the first demonstration study, sea ice thickness up to 50 cm has been derived using a semi-empirical algorithm with constant tie-points. Here, we introduce a novel iterative retrieval algorithm that is based on a thermodynamic sea ice model and a three-layer radiative transfer model, which explicitly takes variations of ice temperature and ice salinity into account. In addition, ice thickness variations within the SMOS spatial resolution are considered through a statistical thickness distribution function derived from high-resolution ice thickness measurements from NASA's Operation IceBridge campaign. This new algorithm has been used for the continuous operational production of a SMOS-based sea ice thickness data set from 2010 on. The data set is compared to and validated with estimates from assimilation systems, remote sensing data, and airborne electromagnetic sounding data. The comparisons show that the new retrieval algorithm has a considerably better agreement with the validation data and delivers a more realistic Arctic-wide ice thickness distribution than the algorithm used in the previous study (Kaleschke et al., 2012).

Description: The microwave interferometric radiometer of the European Space Agency's Soil Moisture and Ocean Salinity (SMOS) mission measures at a frequency of 1.4 GHz in the L-band. In contrast to other microwave satellites, low frequency measurements in L-band have a large penetration depth in sea ice and thus contain information on the ice thickness. Previous ice thickness retrievals have neglected a snow layer on top of the ice. Here, we implement a snow layer in our emission model and investigate how snow influences L-band brightness temperatures and whether it is possible to retrieve snow thickness over thick Arctic sea ice from SMOS data. We find that the brightness temperatures above snow-covered sea ice are higher than above bare sea ice and that horizontal polarisation is more affected by the snow layer than vertical polarisation. In accordance with our theoretical investigations, the root mean square deviation between simulated and observed horizontally polarised brightness temperatures decreases from 20.9 K to 4.7 K, when we include the snow layer in the simulations. Although dry snow is almost transparent in L-band, we find brightness temperatures to increase with increasing snow thickness under cold Arctic conditions. The brightness temperatures' dependence on snow thickness can be explained by the thermal insulation of snow and its dependence on the snow layer thickness. This temperature effect allows us to retrieve snow thickness over thick sea ice. For the best simulation scenario and snow thicknesses up to 35 cm, the average snow thickness retrieved from horizontally polarised SMOS brightness temperatures agrees within 0.1 cm with the average snow thickness measured during the IceBridge flight campaign in the Arctic in spring 2012. The corresponding root mean square deviation is 5.5 cm, and the coefficient of determination is r2 = 0.58.

Description: Following the launch of ESA's Soil Moisture and Ocean salinity (SMOS) mission it has been shown that brightness temperatures at a low microwave frequency of 1.4 GHz (L-band) are sensitive to sea ice properties. In a first demonstration study, sea ice thickness has been derived using a semi-empirical algorithm with constant tie-points. Here we introduce a novel iterative retrieval algorithm that is based on a sea ice thermodynamic model and a three-layer radiative transfer model, which explicitly takes variations of ice temperature and ice salinity into account. In addition, ice thickness variations within a SMOS footprint are considered through a statistical thickness distribution function derived from high-resolution ice thickness measurements from NASA's Operation IceBridge campaign. This new algorithm has been used for the continuous operational production of a SMOS based sea ice thickness data set from 2010 on. This data set is compared and validated with estimates from assimilation systems, remote sensing data, and airborne electromagnetic sounding data. The comparisons show that the new retrieval algorithm has a considerably better agreement with the validation data and delivers a more realistic Arctic-wide ice thickness distribution than the algorithm used in the previous study.

Description: In preparation for the European Space Agency's (ESA) Soil Moisture and Ocean Salinity (SMOS) mission, we investigated the potential of L-band (1.4 GHz) radiometry to measure sea-ice thickness. Sea-ice brightness temperature was measured at 1.4 GHz and ice thickness was measured along nearly coincident flight tracks during the SMOS Sea-Ice campaign in the Bay of Bothnia in March 2007. A research aircraft was equipped with the L-band Radiometer EMIRAD and coordinated with helicopter based electromagnetic induction (EM) ice thickness measurements. We developed a three layer (ocean-ice-atmosphere) dielectric slab model for the calculation of ice thickness from brightness temperature. The dielectric properties depend on the relative brine volume which is a function of the bulk ice salinity and temperature. The model calculations suggest a thickness sensitivity of up to 1.5 m for low-salinity (multi-year or brackish) sea-ice. For Arctic first year ice the modelled thickness sensitivity is less than half a meter. It reduces to a few centimeters for temperatures approaching the melting point. The campaign was conducted under unfavorable melting conditions and the spatial overlap between the L-band and EM-measurements was relatively small. Despite these disadvantageous conditions we demonstrate the possibility to measure the sea-ice thickness with the certain limitation up to 1.5 m. The ice thickness derived from SMOS measurements would be complementary to ESA's CryoSat-2 mission in terms of the error characteristics and the spatiotemporal coverage. The relative error for the SMOS ice thickness retrieval is expected to be not less than about 20%.

Description: Following the launch of ESA's Soil Moisture and Ocean Salinity (SMOS) mission, it has been shown that brightness temperatures at a low microwave frequency of 1.4 GHz (L-band) are sensitive to sea ice properties. In the first demonstration study, sea ice thickness up to 50 cm has been derived using a semi-empirical algorithm with constant tie-points. Here, we introduce a novel iterative retrieval algorithm that is based on a thermodynamic sea ice model and a three-layer radiative transfer model, which explicitly takes variations of ice temperature and ice salinity into account. In addition, ice thickness variations within the SMOS spatial resolution are considered through a statistical thickness distribution function derived from high-resolution ice thickness measurements from NASA's Operation IceBridge campaign. This new algorithm has been used for the continuous operational production of a SMOS-based sea ice thickness data set from 2010 on. The data set is compared to and validated with estimates from assimilation systems, remote sensing data, and airborne electromagnetic sounding data. The comparisons show that the new retrieval algorithm has a considerably better agreement with the validation data and delivers a more realistic Arctic-wide ice thickness distribution than the algorithm used in the previous study (Kaleschke et al., 2012).

Description: Following the launch of ESA's Soil Moisture and Ocean salinity (SMOS) mission it has been shown that brightness temperatures at a low microwave frequency of 1.4 GHz (L-band) are sensitive to sea ice properties. In a first demonstration study, sea ice thickness has been derived using a semi-empirical algorithm with constant tie-points. Here we introduce a novel iterative retrieval algorithm that is based on a sea ice thermodynamic model and a three-layer radiative transfer model, which explicitly takes variations of ice temperature and ice salinity into account. In addition, ice thickness variations within a SMOS footprint are considered through a statistical thickness distribution function derived from high-resolution ice thickness measurements from NASA's Operation IceBridge campaign. This new algorithm has been used for the continuous operational production of a SMOS based sea ice thickness data set from 2010 on. This data set is compared and validated with estimates from assimilation systems, remote sensing data, and airborne electromagnetic sounding data. The comparisons show that the new retrieval algorithm has a considerably better agreement with the validation data and delivers a more realistic Arctic-wide ice thickness distribution than the algorithm used in the previous study.