Monitoring Sea Ice with Space-borne Synthetic Aperture Radar
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1 Monitoring Sea Ice with Space-borne Synthetic Aperture Radar Torbjørn Eltoft UiT- the Arctic University of Norway CIRFA A Centre for Research-based Innovation cirfa.uit.no
2 Sea ice & climate Some basic facts Sea ice covers the polar oceans in the Arctic and the Antarctic The area covered by ice varies seasonally; the Arctic sea ice extent in winter is about 3 x larger than in summer (in 2012: factor 4...) Sea ice is from a few cm to several meters thick Ridging and rafting due to dynamics can lead to very thick ice Two main classes: First-year ice and multiyear ice The ice gets covered by snow, which when melting forms melt ponds
3 (R.G.Onsott & R. Schuchman) Sea Ice Types
4 Courtesy A. Renner
5 What do we want to know about sea ice - Sea ice segmentation - Sea ice type classification - Physical characterization of sea ice - Retrieval of geophysical parameters Concentration, thickness Roughness Ice floe size distribution Lead & ridge fraction Melt pond fraction Sea ice drift estimation
6 Sea Ice and Arctic Operations Oil & gas Shipping Tourism
7 Challenges related to Arctic industrial activities Low temperatures Icing Remoteness Sea Ice Darkness Low visibility Polar lows and arctic storms
8 Arctic surveillance: Requirements Independent of weather and light conditions Image at multiple scales High spatial resolution Frequent imaging of Areas of Interest Provide large information contents Combine sensors! Combine platforms! Synthtic Aperture Radar!
9 Some current & future radar Satellites Current Radarsat 2 TerraSAR X ALOS Palsar 2 Sentinel 1 COSMO SkyMed Radarsat Constallation Sentinel family
10 New opportunities Multiple polarizations Multiple frequencies Combined Radar/optical
11 Penetration of microwave signals in vegetation, soils and ice (after NASA, 1987)
12 ALOS 2 Scenes from the N-ICE campaign
13 Radarsat 2 Scenes from the N-ICE campaign
14 Pauli-decomposition RS2: C band 26. mai, 2015 ALOS 2: L band 26. mai, 2015 Copyright raw data MDA, Processed by KSAT Copyright raw data JAXA, 2015
15 NNED decomposition Copyright raw data MDA, Processed by KSAT Copyright raw data JAXA, 2015
16 Polarimetric Entropy
17 Sea ice classification Copyright raw data MDA, Processed by KSAT Moen, M. A.N., A.P. Doulgeris, S.N. Anfinsen, A.H.H. Renner, N. Hughes, S. Gerland and T. Eltoft: Comparison of automatic segmentation of full polarimetric SAR sea ice images with manually drawn ice charts, The Cryosphere, vol. 7, no. 6, pp , November, 2013.
18 Sea ice velocity estimation from image time series Correlation methods: Estimate accuracy with drifting buoys Copyright raw data MDA, Processed by KSAT
19 ALOS-2: Example of sea ice segmentation Copyright raw data JAXA, 2015
20 ALOS problems? Copyright raw data JAXA, 2015
21 ALOS problems? Meyer, F.J., J. Nicoll and A.P. Doulgeris: Characterization and correction of residual RFI signatures in operationally processed ALOS PALSAR imagery, Proc. EUSAR 2012, Nuremberg, Germany, pp , April, 2012 (Best Paper Award).
22 Centre for Integrated Remote Sensing and Forecasting for Arctic Operations (CIRFA) Objectives: Long term research on integrated remote sensing from satellites and airborne platforms for Arctic operations Requests for ALOS data: Satellite data is needed in algorithm development and in dedicated validation campaigns related to the various work packages
23 Work packages WP1: Ocean Remote Sensing PI: Norut 1.1 Retrieval of ocean winds 1.2 Retrieval of ocean surface currents 1.3 Detection and tracking of polar lows WP2: Sea Ice, Iceberg and Growler Remote Sensing PI: UiT 2.1 Classification & characterization of sea ice 2.2 Estimation of sea ice drift velocity field 2.3 Iceberg detection & characterization in open waters 2.4 Iceberg detection & characterization in sea ice 2.5 Tracking methodologies WP5: Drift Modelling and Prediction PI: Met.no 5.1: Data assimilation 5.2 Sea ice drift modelling and prediction 5.3: Oils spill drift modelling and prediction 5.4 Iceberg drift and deterioration WP6: Field Work PI: NPI 6.1 Yearly cruises to collect in situ data 6.2 Yearly helicopter flights with EM sounder 6.3 Freeze in of Lance winter 2015 WP3: Oil Spill Remote Sensing PI: UiT 3.1: Detection & characterization in open water 3.2: Detection & characterization in sea ice WP4: RPAS Technology PI: Norut 4.1: Technology for RPAS operation 4.2: Sensors for sea ice and iceberg mapping 4.3: Sensors for oil spill mapping 4.3: Technology for geo tagging of icebergs WP7: Pilot Service Demonstration PI: KSAT & Met.no 7.1 Arctic surveillance services design 7.2: System and data integration 7.3: Pilot service demonstration Iceberg services Sea ice services Oil spill services Polar Lows services
24 Thank you for your attention cirfa.uit.no
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