Mapping the Amount of Snow on Arctic Sea Ice

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Scientists have long monitored Arctic sea ice using remote sensing data, given the importance in sea ice in regulating the Earth’s climate and global circulation. Many tools, including drones and multi-spectral and panchromatic satellite are used in this endeavor. Nevertheless, ground-based monitoring have offered generally the most accurate data. Even with ground-based monitoring, what has been a challenge is measuring and mapping the amount of snow on Arctic sea ice, given the wide spatial extent of the Arctic and polar regions in general. Recently, scientists have gained better remote sensing data to enable more sophisticated monitoring of our fragile sea ice in the Arctic and potential polar regions using more recent and older satellite systems together.

Snow on top of sea ice plays an important role in the rate in which ice can melt in polar regions. Snow on top of sea ice can limit the growth of sea ice during winter, as it acts to insulate the ice. In the warmer months, however, snow that melts begins to collect on the surface of ice in the polar regions. If there is a lot of sun and warmth, that melt water can absorb that heat and accelerate overall ice melt as water melts through. Therefore, scientists have attempted to know how much snow there is on sea ice, given its role in ice growth and melt. For the first time, two types of satellites were used to compliment each other in measuring snow depth. The first is NASA’s ICESat-2, which uses a laser altimeter to measure the height of the snow’s surface. The European Space Agency’s CryoSat-2 satellite uses radar that can penetrate snow. This allows the radar to go well below the surface and snow and then bounce from the ice’s surface. The space between the ice and top of the snow then gives snow depth. The two satellites, thus, can be used together to give top and bottom measurements. While this discovery is greatly beneficial for scientists in monitoring snow cover on ice in the Arctic region in particular, the problem is the orbit of these two satellites does not always make it possible to make comparable measurements near in time to each other. Additionally, the CryoSat-2 satellite is older, being about a decade old, which means it could become decommissioned far sooner than the 2018 ICESat-2. For now, scientists will likely use both satellites but may also plan to launch another complimentary satellite to help ICESat-2 measure the Arctic region.[1]

Maps showing snow depths across Arctic sea ice from October 2018 to April 2019. Source: NASA
Maps showing snow depths across Arctic sea ice from October 2018 to April 2019. Source: NASA

There have been previous developments that enabled some measurements of snow depth on ice to be possible. For instance, a combination of microwave, global navigation satellite system reflectometry (GNSS-R), and ground-base station data can be combined using artificial neural network derived data, which uses base measurements for training, to provide relatively accurate estimates of snow depth on ice. While this method has proven useful, it does now seem to potentially be less reliable than the new method of combining the ICESat-2 and CrySat-2 satellite data together.[2] The use of microwave and GNSS-R data is similar to the use of data from Copernicus Imaging Microwave Radiometer (CIMR) which can also be captured fed into a neural network model to provide estimated snow depth measurements. This method is also effective in producing fairly accurate data in the absence of continuous spatial and temporal coverage given the lack of concurrent orbit for ICESat-2 and CryoSat-2.[3] Other methods have simply depended on field-collected data that then deploy kriging and spatial regression estimates across networked measuring points. Similar to other estimates, this introduces potentially more error than that of the two-satellite laser and radar systems as the kriging and regression methods are interpolations that depends on the density of the network coverage for accuracy. A combination of continuous laser and radar measurements has generally more accurate properties.[4]

The recent, somewhat accidental, discovery that ICESat-2 and CrySat-2 work well together to estimate snow on sea ice is a positive development for scientists monitoring the health of global climate change and potential change to sea ice. However, this excitement might be tempered by the fact that because these satellites were not planned to work together, these satellite do not follow an optimal orbit to enable continuous monitoring. New systems will likely have to be developed to allow such monitoring. Nevertheless, the new developments means that scientists might now have enough data to better inform future satellite development that can use both laser and radar sensors together.


[1] For more on ICESat-2 and CryoSat-2 and how they can be used to measure snow on Arctic sea ice, see: Additionally, an peer-reviewed article on this topic can be found here: Kwok, R., Kacimi, S., Webster, M. A., Kurtz, N. T., & Petty, A. A. (2020). Arctic Snow Depth and Sea Ice Thickness From ICESat‐2 and CryoSat‐2 Freeboards: A First Examination. Journal of Geophysical Research: Oceans, 125(3).

[2] For more on how ground-based and satellite data could be used, along with deep-learning methods, to estimate snow depth, see: Wang, J., Yuan, Q., Shen, H., Liu, T., Li, T., Yue, L., Shi, X., & Zhang, L. (2020). Estimating snow depth by combining satellite data and ground-based observations over Alaska: A deep learning approach. Journal of Hydrology, 585, 124828.

[3] For more on CIMR data and neural network estimation, see: Braakmann-Folgmann, A., & Donlon, C. (2019). Estimating Snow Depth on Arctic Sea Ice using Satellite Microwave Radiometry and a Neural Network [Preprint]. Snow/Remote Sensing.

[4] For more on using ground-based measurements and spatial methods together to estimate snow depth, see: Collados‐Lara, A., Pardo‐Igúzquiza, E., & Pulido‐Velazquez, D. (2020). Optimal design of snow stake networks to estimate snow depth in an alpine mountain range. Hydrological Processes, 34(1), 82–95.


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