Comprehensive Annual Ice Sheet Velocity Mapping Using Landsat-8, Sentinel-1, and RADARSAT-2 Data

Satellite remote sensing data including Landsat-8 (optical), Sentinel-1, and RADARSAT-2 (synthetic aperture radar (SAR) missions) have recently become routinely available for large scale ice velocity mapping of ice sheets in Greenland and Antarctica. These datasets are too large in size to be proces...

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Vydáno v:Remote sensing (Basel, Switzerland) Ročník 9; číslo 4; s. 364
Hlavní autoři: Mouginot, Jeremie, Rignot, Eric, Scheuchl, Bernd, Millan, Romain
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 01.04.2017
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ISSN:2072-4292, 2072-4292
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Abstract Satellite remote sensing data including Landsat-8 (optical), Sentinel-1, and RADARSAT-2 (synthetic aperture radar (SAR) missions) have recently become routinely available for large scale ice velocity mapping of ice sheets in Greenland and Antarctica. These datasets are too large in size to be processed and calibrated manually as done in the past. Here, we describe a methodology to process the SAR and optical data in a synergistic fashion and automatically calibrate, mosaic, and integrate these data sets together into seamless, ice-sheet-wide, products. We employ this approach to produce annual mosaics of ice motion in Antarctica and Greenland with all available data acquired on a particular year. We find that the precision of a Landsat-8 pair is lower than that of its SAR counterpart, but due to the large number of Landsat-8 acquisitions, combined with the high persistency of optical surface features in the Landsat-8 data, we obtain accurate velocity products from Landsat that integrate well with the SAR-derived velocity products. The resulting pool of remote sensing products is a significant advance for observing changes in ice dynamics over the entire ice sheets and their contribution to sea level. In preparation for the next generation sensors, we discuss the implications of the results for the upcoming NASA-ISRO SAR mission (NISAR).
AbstractList Satellite remote sensing data including Landsat-8 (optical), Sentinel-1, and RADARSAT-2 (synthetic aperture radar (SAR) missions) have recently become routinely available for large scale ice velocity mapping of ice sheets in Greenland and Antarctica. These datasets are too large in size to be processed and calibrated manually as done in the past. Here, we describe a methodology to process the SAR and optical data in a synergistic fashion and automatically calibrate, mosaic, and integrate these data sets together into seamless, ice-sheet-wide, products. We employ this approach to produce annual mosaics of ice motion in Antarctica and Greenland with all available data acquired on a particular year. We find that the precision of a Landsat-8 pair is lower than that of its SAR counterpart, but due to the large number of Landsat-8 acquisitions, combined with the high persistency of optical surface features in the Landsat-8 data, we obtain accurate velocity products from Landsat that integrate well with the SAR-derived velocity products. The resulting pool of remote sensing products is a significant advance for observing changes in ice dynamics over the entire ice sheets and their contribution to sea level. In preparation for the next generation sensors, we discuss the implications of the results for the upcoming NASA-ISRO SAR mission (NISAR).
Author Millan, Romain
Rignot, Eric
Scheuchl, Bernd
Mouginot, Jeremie
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  surname: Scheuchl
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  givenname: Romain
  orcidid: 0000-0002-7987-1305
  surname: Millan
  fullname: Millan, Romain
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Snippet Satellite remote sensing data including Landsat-8 (optical), Sentinel-1, and RADARSAT-2 (synthetic aperture radar (SAR) missions) have recently become...
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SubjectTerms Calibration
Data acquisition
Datasets
Detection
Ice
Ice sheets
Landsat
Landsat satellites
Mapping
Missions
Mosaics
Optical data processing
Radar
Radarsat
Remote observing
Remote sensing
Sea level
Sensors
Sheets
Synthetic aperture radar
Velocity
Title Comprehensive Annual Ice Sheet Velocity Mapping Using Landsat-8, Sentinel-1, and RADARSAT-2 Data
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