Sea ice detection with GNSS-Reflectometry data from TechDemoSat-1

Cartwright, Jessica; Banks, Chris ORCID: https://orcid.org/0000-0003-4457-0876; Srokosz, Meric ORCID: https://orcid.org/0000-0002-7347-7411. 2020 Sea ice detection with GNSS-Reflectometry data from TechDemoSat-1. In: Ocean Sciences Meeting, San Diego, California, 16-21 February 2020.

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Abstract/Summary

Observations of sea ice are essential in order to monitor the effects of a changing climate as well as understanding potential changes in the future. Due to the remote nature and large area of the sea ice areas, satellite remote sensing is the only viable approach for this task. The use of reflected navigation signals, such as those from Global Positioning System (GPS) satellites presents a cost-effective means of making sea ice observations. At present such techniques are used primarily for the monitoring of ocean winds, however when applied to sea ice, GNSS-R (Global Navigation Satellite Systems-Reflectometry) would allow a reduction in the costs associated with these measurements, thus enabling an increase in the spatio-temporal resolution and coverage. Here we present a new method for the detection of sea ice applied to 33 months of GNSS-R data from the UK TechDemoSat-1 satellite. This sea ice detection method shows the potential for a future GNSS-R polar mission, attaining an agreement of over 97% globally when compared to ESA’s CCI (European Space Agency’s Climate Change Initiative) sea ice concentration product. The algorithm uses a combination of two parameters derived from the delay-Doppler Maps (DDMs) to quantify the spread of power in delay and Doppler. Threshold application on a small Antarctic training dataset then allows sea ice to be distinguished from open water. Almost 50 different parameters are derived from the DDMs throughout this study and the two best performing parameters are then applied to the dataset as a whole. Differences between sea ice detection from this method and passive microwave, scatterometry and operational datasets are explored. We apply this method to the entire TDS-1 dataset and demonstrate how we can provide information on the seasonal and multi-year changes in sea ice distribution.

Item Type:	Publication - Conference Item (Paper)
Related URLs:	Other
Date made live:	15 Feb 2021 14:22 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/529649

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