3 research outputs found

    Snow microstructure on sea ice: Importance for remote sensing applications

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    European Geosciences Union (EGU) General Assembly, 19-30 Apr 2021.-- 2 pagesSnow plays a key role in interpreting satellite remote sensing data from both active and passive sensors in the high Arctic and therefore impacts retrieved sea ice variables from these systems ( e.g., sea ice extent, thickness and age). Because there is high spatial and temporal variability in snow properties, this porous layer adds uncertainty to the interpretation of signals from spaceborne optical sensors, microwave radiometers, and radars (scatterometers, SAR, altimeters). We therefore need to improve our understanding of physical snow properties, including the snow specific surface area, snow wetness and the stratigraphy of the snowpack on different ages of sea ice in the high Arctic. The MOSAiC expedition provided a unique opportunity to deploy equivalent remote sensing sensors in-situ on the sea ice similar to those mounted on satellite platforms. To aid in the interpretation of the in situ remote sensing data collected, we used a micro computed tomography (micro-CT) device. This instrument was installed on board the Polarstern and was used to evaluate geometric and physical snow properties of in-situ snow samples. This allowed us to relate the snow samples directly to the data from the remote sensing instruments, with the goal of improving interpretation of satellite retrievals. Our data covers the full annual evolution of the snow cover properties on multiple ice types and ice topographies including level first-year (FYI), level multi-year ice (MYI) and ridges. First analysis of the data reveals possible uncertainties in the retrieved remote sensing data products related to previously unknown seasonal processes in the snowpack. For example, the refrozen porous summer ice surface, known as surface scattering layer, caused the formation of a hard layer at the multiyear ice/snow interface in the winter months, leading to significant differences in the snow stratigraphy and remote sensing signals from first-year ice, which has not experienced summer melt, and multiyear ice. Furthermore, liquid water dominates the extreme coarsening of snow grains in the summer months and in winter the temporally large temperature gradients caused strong metamorphism, leading to brine inclusions in the snowpack and large depth hoar structures, all this significantly influences the signal response of remote sensing instrumentsPeer reviewe

    Remote Sensing of Sea Ice on the MOSAiC Ice Floe - An Overview

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    MOSAiC Science Conference/Workshop, 25-29 April 2022, Potsdam, GermanyDuring MOSAiC several remote sensing instruments designed for observing the sea ice and its snow cover were installed on the ice floe next to Polarstern and on the vessel itself. Satellite measurements constitute a few of the most important climate data records for polar regions. The MOSAiC experiments will help to improve their quality and better assess their uncertainties. In particular the following measurements were performed during MOSAiC: (i) 0.5-89 GHz microwave radiometers, (ii) L to Ka-band microwave radar scatterometers, (iii) reflected GNSS measurements, and (iv) infrared, visual, and hyperspectral cameras. The remote sensing measurements were accompanied by extensive measurements of snow and ice properties. By having these coincident multi-frequency remote sensing and in-situ observations, factors influencing the emission, reflection, and scattering of microwaves in sea ice and snow can be better understood. New remote sensing methods can be developed and contribute to new and upcoming satellite missions. Here we will present an overview of the measurement program and first results from simultaneously measuring instruments during two events in November 2019 (winter) and September 2020 (summer)Peer reviewe

    Remote Sensing of Sea Ice on the MOSAiC Ice Floe

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    Talk delivered in American Geophysical Union Fall Meeting online, 1-17 December 2020The one-year long MOSAiC (Multidisciplinary drifting Observatory for the Study of Arctic Climate) drift expedition presents an excellent opportunity to evaluate current satellite remote sensing observations and develop new remote sensing methods. While the research icebreaker Polarstern is drifting with the sea ice from October 2019 to September 2020 several remote sensing instruments designed for observing the sea ice and snow on top were installed on the ice floe next to Polarstern and on the vessel itself. This, for the first time, will allow to monitoring the freeze-up to melt onset cycle and different ice types. Most of the instruments have counterparts in space, and their measurements are designed to obtain a better process understanding of the interaction of electromagnetic waves with snow and sea ice. Satellite measurements constitute a few of the most important climate data records for polar regions. The MOSAiC experiments will help to improve them and better assess their uncertainties. In particular the following measurements were performed during MOSAiC: (i) microwave radiometer observations at 0.5驴2, 1.4, 7, 11, 19, 37, and 89 GHz frequencies in dual polarization, (ii) fully-polarimetric, microwave radar scatterometer observations at L-, C-, X-, Ku-, and Ka-band frequencies, (iii) reflected GNSS measurements from snow and ice, and (iv) infrared, visual, and hyperspectral cameras. The instruments on the ice floe were oriented to observe similar snow and ice conditions. The remote sensing measurements were accompanied by extensive measurements of snow and ice properties in the vicinity of the measurement field. By having these coincident multi-frequency remote sensing and in-situ observations as well as the environmental conditions measured by other MOSAiC teams, factors influencing the emission, reflection, and scattering of microwaves in sea ice and snow can be better understood so that new remote sensing methods can be developed and contribute to new satellite missions. Here we will present an overview of the measurement program and first results from simultaneously measuring instruments
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