Christopher Choi

Christopher Choi

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Remote Sensing in Polar Regions - Literature Review

May 2020 Authors: Christopher Choi, Claire Bachard, Nick Brown

Introduction

Since satellite monitoring of polar ice began in the 1970s, consistent declines in minimum summer sea ice extent, and increased glacial retreat has been observed (Meiner et al. 2007).Arctic sea ice has declined at a rate of 12.9% per decade between 1979 and 2018. Sea ice older than 5 years has also reduced by 90% in the same time (He et al. 2019). These observations are primarily driven by the effects of the atmosphere’s increase in greenhouse gas concentrations, which have led to a doubling of arctic temperatures within the last two decades (He et al. 2019). Further exacerbation of the observed changes in the polar environment due to a decreased albedo occuring when melted glacial ice and reduced sea ice exposes darker bedrock and water bodies, respectively (Naegeli et al. 2017, Curry et al. 1995).

The extent of the effects of warming polar regions are felt worldwide. Ice melt leading to sea level rise threatens low elevation communities like Venice, Bangladesh, Southern Vietnam, is predicted to increase by much as 2 meters by the year 2100 (Lindsey 2019). Permafrost serves a crucial role in our environment as a methane sink, which is the second largest contributor of greenhouse gases. Widespread disappearance of near-surface Arctic permafrost is also predicted to occur by the end of the century (He et al. 2019), which is already releasing over 17 million tonnes of methane per year, in the Siberian Arctic alone (Shakhova et al. 2007).

Surface Temperature

The observation of Surface Temperature usually refers to the Land/Ocean Air Temperature. These measurements are critical to see the effects of climate change on the ground and most Remote Sensing satellites with thermal and IR bands observe surface air temperatures, notable examples are Modis, LandSAT-8, and GRACE. Notable instruments carried on Remote Sensing satellites used for observing Surface Temperature are the Advanced Very High Resolution Radiometer (AVHRR) and the Along Track Scanner Radiometer (ATSR). To calculate Surface Temperature, you have to take the thermal data received from the satellite and apply an atmospheric correction algorithm which eliminates atmospheric effects such as clouds, gasses in the air, and other atmospheric effects (Meng 2019).

An interesting application of surface temperature measures on polar regions was the discovery of a seesaw effect between Arctic and Antarctic surface temperatures. When the temperature of the arctic is rising, the Antarctic temperatures are declining (Chylek et. al 2010). This pattern lines up with interhemispheric ocean deep and surface water circulations. This pattern has been observed since 2010 however in recent years surface temperatures in both poles have been increasing, mainly due to climate change affecting polar temperatures more than equatorial regions (NASA GISS 2020). Increased surface temperatures have also directly affected vegetation in the Arctic region (Raynolds et. al 2007). This study showed how increased Surface Temperatures had a positive effect on the NDVI of regions in Northern Russia and Alaska. It showed that a 5 degrees celsius increase corresponded to an average NDVI increase of .07. This number is misleading however because some areas had increased vegetation growth (Russia and Alaska) due to the soil type, which once the temperature increased was well-suited for vegetation. Other regions such as the northern Canadian islands all saw decreased NDVI, mainly due to being recently deglaciated because of the increased surface temperatures (Raynolds et. al 2007).

Sea Ice and Glacial Reduction

The Ice, Cloud and Land Elevation Satellite 1 &2 (ICESat and ICESat-2, respectively) are among the forefront of satellites regarding the monitoring retrieving sea ice and glacial reduction in the arctic regions. ICESat-2 uses an advanced topographic laser altimeter system (ATLAS) with the propriety goal of measuring sea ice freeboard and elevation. Other data products produced from ICESat-2 include land ice elevation, (global) land water vegetation elevation, ocean elevation, and mean sea surface (NASA 2020). The satellite has a 183 day repeat pattern, producing elvational products accurate to one ince, and is adapted to polar observations since it’s flight tracks converge along 88° latitude, which is higher latitude than that of which most satellites orbit (Ramsayer 2018). ATLAS operates by firing 6 beams of greenlight wavelengths at 532nm in a 3 x 2 array, 10,000 times per second (10Hz repetition rate), resulting in a 0.7m separation between pulses (Markus et al. 2017). Currently, with the combined data from ICESat and ICESat-2, we have continuous data from 2003 to 2010 (from ICESat), and from 2018 to current (from ICESat-2). Within these periods, temporal analysis have been able to show seasonal and annual changes in polar regions’ sea ice freeboard, snow, and ice thickness (Kwok & Cunningham 2008). The ability of ICESat-2’s ability to measure sea ice freeboard, that is the distance between an ice sheet’s air-snow interface and the local sea level, allows the ability for detecting ice mass and topography (Kwok et al. 2009). Studies produced from ICESat data have also shown that from between 2003 and 2008, sea ice thickness had thinned, on average, from 3.4 meters to 2.8 meters, a 17% reduction (Kwok et al.). In addition, Pritchard et al. had also shown, using ICESat data, new features regarding the rate and method dynamic ice sheet reduction in Greenland and Antarctica occured at the peripheries, the most prominent regions of reduction (2009).

Permafrost

Because permafrost is not visible from Earth’s surface, detecting it using remote sensing is difficult. Despite this, several methods have been developed to identify and map permafrost using satellite data. Vegetation patterns consistent with underlying permafrost can be identified from space (Westermann et al., 2015). For example, as permafrost melts, soil dynamics change, causing forests to collapse into bogs (Finger et al., 2016). This change can be detected using variables such as NDVI or plant height, both of which can be measured remotely. The presence of permafrost can also be signaled by certain surface landforms. An example is pingos, which are hills formed by frost heave on the ground surface. These have been detected using optical satellite data, and can be used to infer permafrost extent (Westermann et al., 2015). Furthermore, subsidence, landslides, and thermokarst lakes suggest underlying melting permafrost (Westermann et al., 2015). SAR data can be employed to detect these changes in surface height. Perhaps the most accurate, albeit labor intensive, method to map permafrost is through airborne surveys. Airplanes can employ active MW remote sensing using frequencies in the kilohertz range, which can penetrate the soil to depths up to 100m and thus map permafrost configurations below the ground surface (Westermann et al., 2015).

The methods discussed above do not allow for widespread mapping of permafrost extent. Park et al. (2016) attempted to achieve this widespread mapping using MW remote sensing data. In identifying a method to map permafrost using MW data, the researchers hoped to overcome the limitations imposed by clouds and daylight (which are especially prevalent in polar regions) on optical and infrared data. To do so, the researchers used near-daily freeze thaw data products, which indicate if a given area is frozen. If an area was found to be frozen for more than half of the year for at least two consecutive years, then the researchers identified it as permafrost. This method is well supported by several studies which suggest that if the ground surface is frozen for at least half of the year, then the area typically experiences high frost penetration which leads to permafrost (Park et al., 2016). Ultimately, Park et al (2016) was able to roughly delineate permafrost extent, with the poor spatial resolution of MW data limiting the accuracy of predictions over mountainous terrains (Park et al., 2016). In mountainous areas, land surface and thermal inertia measurements derived from thermal infrared data have had success in mapping ice-debris landforms (Brenning et al., 2012). The higher spatial resolution allows infrared data to succeed where microwave data fails. Brenning et al. (2012) focused their research on rock glaciers and debris glaciers, but their findings can be generally applied to permafrost as well. Brenning et al. (2012) ultimately found that land surface temperature is lower and thermal inertia is higher when ice underlies the ground surface. However, because land surface temperature varies with altitude where thermal inertia does not, thermal inertia could have the potential to produce more accurate predictions, especially in locations where ground cover type is uniform, as thermal inertia varies depending on how dense the dirt/debris is (Brenning et al., 2012).

Conclusion

Since climate change has the potential to drastically alter Earth’s climate, the planet’s ecosystems and human communities, it is essential that scientists can accurately predict how Earth’s climate will change and the effects that these changes will have across the globe so that policy makers can act accordingly. One key aspect to making these predictions is understanding the hydrologic cycle, as water plays an important role in determining how the climate will change and also how ecosystems will respond. As discussed in this report, remote sensing can be used to better understand how ice (a major component of the hydrologic cycle) is responding to climate change, which can in turn be used to develop improved climate and ecosystem models from which more accurate predictions can be made.

*Works Cited:

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