Mei
M. Jeffrey
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A textural approach to improving snow depth estimates in the Weddell Sea
Estimating early-winter Antarctic sea ice thickness from deformed ice morphology
Mei
M. Jeffrey
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Article
The snow depth on Antarctic sea ice is critical to estimating the sea ice thickness distribution from laser altimetry data, such as from Operation IceBridge or ICESat-2. Snow redistributed by wind collects around areas of deformed ice and forms a wide variety of features on sea ice; the morphology of these features may provide some indication of the mean snow depth. Here, we apply a textural segmentation algorithm to classify and group similar textures to infer the distribution of snow using snow surface freeboard measurements from Operation IceBridge campaigns over the Weddell Sea. We find that texturally-similar regions have similar snow/ice ratios, even when they have different absolute snow depth measurements. This allows for the extrapolation of nadir-looking snow radar data using two-dimensional surface altimetry scans, providing a two-dimensional estimate of the snow depth with ∼22% error. We show that at the floe scale (∼180 m), snow depth can be directly estimated from the snow surface with ∼20% error using deep learning techniques, and that the learned filters are comparable to standard textural analysis techniques. This error drops to ∼14% when averaged over 1.5 km scales. These results suggest that surface morphological information can improve remotely-sensed estimates of snow depth, and hence sea ice thickness, as compared to current methods. Such methods may be useful for reducing uncertainty in Antarctic sea ice thickness estimates from ICESat-2.
Article
Satellites have documented variability in sea ice areal extent for decades, but there are significant challenges in obtaining analogous measurements for sea ice thickness data in the Antarctic, primarily due to difficulties in estimating snow cover on sea ice. Sea ice thickness (SIT) can be estimated from snow freeboard measurements, such as those from airborne/satellite lidar, by assuming some snow depth distribution or empirically fitting with limited data from drilled transects from various field studies. Current estimates for large-scale Antarctic SIT have errors as high as ∼50 %, and simple statistical models of small-scale mean thickness have similarly high errors. Averaging measurements over hundreds of meters can improve the model fits to existing data, though these results do not necessarily generalize to other floes. At present, we do not have algorithms that accurately estimate SIT at high resolutions. We use a convolutional neural network with laser altimetry profiles of sea ice surfaces at 0.2 m resolution to show that it is possible to estimate SIT at 20 m resolution with better accuracy and generalization than current methods (mean relative errors ∼15 %). Moreover, the neural network does not require specification of snow depth or density, which increases its potential applications to other lidar datasets. The learned features appear to correspond to basic morphological features, and these features appear to be common to other floes with the same climatology. This suggests that there is a relationship between the surface morphology and the ice thickness. The model has a mean relative error of 20 % when applied to a new floe from the region and season. This method may be extended to lower-resolution, larger-footprint data such as such as Operation IceBridge, and it suggests a possible avenue to reduce errors in satellite estimates of Antarctic SIT from ICESat-2 over current methods, especially at smaller scales.