Snow Property Controls on Modeled Ku-Band Altimeter Estimates of First-Year Sea Ice Thickness: Case Studies From the Canadian and Norwegian Arctic

Uncertainty in snow properties impacts the accuracy of Arctic sea ice thickness estimates from radar altimetry. On first-year sea ice (FYI), spatiotemporal variations in snow properties can cause the Ku-band main radar scattering horizon to appear above the snow/sea ice interface. This can increase the estimated sea ice freeboard by several centimeters, leading to FYI thickness overestimations. This article examines the expected changes in Ku-band main scattering horizon and its impact on FYI thickness estimates, with variations in snow temperature, salinity, and density derived from ten naturally occurring Arctic FYI Cases encompassing saline/nonsaline, warm/cold, simple/complexly layered snow (4–45 cm) overlying FYI (48–170 cm). Using a semi-empirical modeling approach, snow properties from these Cases are used to derive layer-wise brine volume and dielectric constant estimates, to simulate the Ku-band main scattering horizon and delays in radar propagation speed. Differences between modeled and observed FYI thickness are calculated to assess sources of error. Under both cold and warm conditions, saline snow covers are shown to shift the main scattering horizon above from the snow/sea ice interface, causing thickness retrieval errors. Overestimates in FYI thicknesses of up to 65% are found for warm, saline snow overlaying thin sea ice. Our simulations exhibited a distinct shift in the main scattering horizon when the snow layer densities became greater than 440 kg/m 3 , especially under warmer snow conditions. Our simulations suggest a mean Ku-band propagation delay for snow of 39%, which is higher than 25%, suggested in previous studies

Snow microstructure on sea ice: Importance for remote sensing applications

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

Estimating melt onset over Arctic sea ice from time series multi-sensor Sentinel-1 and RADARSAT-2 backscatter

Information on the timing of melt onset over sea ice is important for understanding the Arctic's changing climate. The daily temporal resolution of passive microwave brightness temperatures provides the most widely utilized observations to detect melt onset but are limited to a spatial resolution of 25 km. Wide-swath synthetic aperture radar (SAR) imagery provides a much higher spatial resolution (20–100 m) but melt onset detection remains challenging because of i) insufficient temporal resolution to facilitate accurate melt onset detection, ii) inconsistent viewing geometries and iii) limited image availability across the Arctic. Here, we construct high temporal resolution composite gamma nought backscatter products (1 day, 1–2 day and 2–4 day) using Sentinel-1 and RADARSAT-2 over a close-to-seamless revisit region located in northern Canadian Arctic and Greenland for estimating melt onset over Arctic sea ice in 2016 and 2017. We employ the necessary radiometric terrain flattening and local resolution weighting techniques to generate normalised backscatter over the entire study region, removing restrictions limiting analysis to a single sensor or track's swath width by integrating both ascending and descending passes into the composite products. Results indicate that higher temporal resolution multi-sensor composite gamma nought products (1 day) that make use of the most imagery provide a robust temporal evolution of the backscatter. This allows for more representative estimates of melt onset as it is easier to separate the melt onset threshold from winter variability that is otherwise a considerable challenge for SAR based melt onset algorithms because of inconsistent temporal resolution. Multi-sensor composite gamma naught melt onset detection is in good agreement with melt onset estimates derived from the Advance Scatterometer (ASCAT) backscatter values and passive microwave brightness temperatures over homogenous sea ice regions but very noticeable improvement was found within narrow channels and regions with more heterogeneous sea ice. In anticipation of the availability of data from even more SAR satellites with the launch of the RADARSAT Constellation Mission, the multi-sensor composite gamma nought approach presented here may offer the most robust approach to estimate the timing of melt onset over sea ice across the Arctic using high spatiotemporal resolution SAR

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

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