Application of Lidar Altimetry and Hyperspectral Imaging to Ice Sheet and Snow Monitoring
The Greenland Ice Sheet (GrIS) is of tremendous importance for climate change projections. The GrIS has contributed an estimated 10.8 mm to sea level rise since 1992, and that contribution is expected to increase in the coming decades. It is therefore essential to make routine measurements of ice, m...
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| Other Authors: | , , , |
| Format: | Thesis |
| Language: | English |
| Published: |
2021
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| Subjects: | |
| Online Access: | https://hdl.handle.net/2027.42/169872 https://doi.org/10.7302/2917 |
| Summary: | The Greenland Ice Sheet (GrIS) is of tremendous importance for climate change projections. The GrIS has contributed an estimated 10.8 mm to sea level rise since 1992, and that contribution is expected to increase in the coming decades. It is therefore essential to make routine measurements of ice, meltwater, and snow over the GrIS using satellite and airborne observations. Two prominent methods for ice sheet monitoring include lidar altimetry and hyperspectral imaging. Lidar altimetry is typically used to make fine-scale estimates of ice sheet surface height, whereas hyperspectral imaging is commonly utilized to infer snow or ice surface composition. In this dissertation, I use data from the Ice, Clouds, and land Elevation Satellite-2 (ICESat-2) and the Next Generation Airborne Visible/Infrared Imaging Spectrometer (AVIRIS-NG) to examine light transmittance over the Greenland Ice Sheet. I first utilize ICESat-2 photon-counting data for the development of a retrieval algorithm for supraglacial lake depth, with validation from the Operation IceBridge airborne mission. This work was performed to support other depth retrieval efforts that struggle with attenuation in deep water. I then use hyperspectral radiative transfer models to perform a sensitivity analysis on snow grain size retrievals. Snow grain size is an important metric for snowpack evolution, but there are limited efforts to quantify potential errors in an existing inversion algorithm. Lastly, I used a combination of Operation IceBridge altimetry and AVIRIS-NG hyperspectral data to assess the impacts of snow grain size on surface heights derived from lidar altimetry. Results from the three studies indicate that lidar signals and ice reflectance in the near-infrared are highly sensitive to changes in surface media. Because it operates at 532 nm, the ICESat-2 laser penetrates through liquid water with minimal signal loss, but volumetric scattering within a snowpack may induce significant errors in surface heights derived from Operation IceBridge, ... |
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