Spatio-temporal variability of X-band radar backscatter and coherence over the Lena River Delta, Siberia

Julia.Boike [ at ]


Satellite-based monitoring strategies for permafrost remain under development and are not yet operational. Remote sensing allows indirect observation of permafrost, a subsurface phenomenon, by mapping surface features or measuring physical parameters that can be used for permafrost modeling. We have explored high temporal resolution time series of TerraSAR-X backscatter intensity and interferometric coherence for the period between August 2012 and September 2013 to assess their potential for detecting major seasonal changes to the land surface in a variety of tundra environmentswithin the Lena River Delta, Siberia. The TerraSAR-X signal is believed to be strongly affected by the vegetation layer, and its viability for the retrieval of soil moisture, for example, is therefore limited. In our study individual events, such as rain and snow showers, that occurred at the time of TerraSAR-X acquisition, or a refrozen crust on the snowpack during the springmeltwere detected based on backscatter intensity signatures. The interferometric coherence showed marked variability; the snowcover onset and snow melt periods were identified by significant reduction in coherence. Principal component analysis provided a good spatial overview of the essential information contained in backscatter and coherence time series and revealed latent relationships between both time series and the surface temperature. The results of these investigations suggest that although X-band SAR has limitations with respect to monitoring seasonal land surface changes in permafrost areas, high-resolution time series of TerraSAR-X backscatter and coherence can provide new insights into environmental conditions.

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ISI/Scopus peer-reviewed
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DOI 10.1016/j.rse.2016.05.003

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Antonova, S. , Kääb, A. , Heim, B. , Langer, M. and Boike, J. (2016): Spatio-temporal variability of X-band radar backscatter and coherence over the Lena River Delta, Siberia , Remote Sensing of Environment, 182 , pp. 169-191 . doi: 10.1016/j.rse.2016.05.003



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