Next Article in Journal
Radiological Assessment on Interest Areas on the Sellafield Nuclear Site via Unmanned Aerial Vehicle
Previous Article in Journal
The Use of C-/X-Band Time-Gapped SAR Data and Geotechnical Models for the Study of Shanghai’s Ocean-Reclaimed Lands through the SBAS-DInSAR Technique
Article Menu

Export Article

Open AccessArticle
Remote Sens. 2016, 8(11), 903; doi:10.3390/rs8110903

Monitoring Bedfast Ice and Ice Phenology in Lakes of the Lena River Delta Using TerraSAR-X Backscatter and Coherence Time Series

1
Alfred Wegener Institute, Helmholtz Center for Polar and Marine Research, Potsdam 14473, Germany
2
Department of Geography & Environmental Management and Interdisciplinary Centre on Climate Change, University of Waterloo, Waterloo, ON N2L 3G1, Canada
3
Department of Geosciences, University of Oslo, Oslo 0316, Norway
4
Department of Geography, Humboldt-Universität zu Berlin, Berlin 10099, Germany
*
Author to whom correspondence should be addressed.
Academic Editors: Deepak R. Mishra, Xiaofeng Li and Prasad S. Thenkabail
Received: 12 September 2016 / Revised: 19 October 2016 / Accepted: 24 October 2016 / Published: 3 November 2016
View Full-Text   |   Download PDF [10347 KB, uploaded 3 November 2016]   |  

Abstract

Thermokarst lakes and ponds are major elements of permafrost landscapes, occupying up to 40% of the land area in some Arctic regions. Shallow lakes freeze to the bed, thus preventing permafrost thaw underneath them and limiting the length of the period with greenhouse gas production in the unfrozen lake sediments. Radar remote sensing permits to distinguish lakes with bedfast ice due to the difference in backscatter intensities from bedfast and floating ice. This study investigates the potential of a unique time series of three-year repeat-pass TerraSAR-X (TSX) imagery with high temporal (11 days) and spatial (10 m) resolution for monitoring bedfast ice as well as ice phenology of lakes in the zone of continuous permafrost in the Lena River Delta, Siberia. TSX backscatter intensity is shown to be an excellent tool for monitoring floating versus bedfast lake ice as well as ice phenology. TSX-derived timing of ice grounding and the ice growth model CLIMo are used to retrieve the ice thicknesses of the bedfast ice at points where in situ ice thickness measurements were available. Comparison shows good agreement in the year of field measurements. Additionally, for the first time, an 11-day sequential interferometric coherence time series is analyzed as a supplementary approach for the bedfast ice monitoring. The coherence time series detects most of the ice grounding as well as spring snow/ice melt onset. Overall, the results show the great value of TSX time series for monitoring Arctic lake ice and provide a basis for various applications: for instance, derivation of shallow lakes bathymetry, evaluation of winter water resources and locating fish winter habitat as well as estimation of taliks extent in permafrost. View Full-Text
Keywords: lake ice; bedfast ice; ice phenology; SAR; TerraSAR-X; backscatter intensity; interferometric coherence; time series; Lena River Delta; CLIMo lake ice; bedfast ice; ice phenology; SAR; TerraSAR-X; backscatter intensity; interferometric coherence; time series; Lena River Delta; CLIMo
Figures

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Supplementary material

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Antonova, S.; Duguay, C.R.; Kääb, A.; Heim, B.; Langer, M.; Westermann, S.; Boike, J. Monitoring Bedfast Ice and Ice Phenology in Lakes of the Lena River Delta Using TerraSAR-X Backscatter and Coherence Time Series. Remote Sens. 2016, 8, 903.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top