Next Article in Journal
Real-Time Detection of Daytime and Night-Time Fire Hotspots from Geostationary Satellites
Next Article in Special Issue
Monitoring the Spatiotemporal Dynamics of Aeolian Desertification Using Google Earth Engine
Previous Article in Journal
Robust Multipath-Assisted SLAM with Unknown Process Noise and Clutter Intensity
Previous Article in Special Issue
Spatio-Temporal Changes of Vegetation Net Primary Productivity and Its Driving Factors on the Qinghai-Tibetan Plateau from 2001 to 2017
Article

Mapping Arctic Lake Ice Backscatter Anomalies Using Sentinel-1 Time Series on Google Earth Engine

by 1,2,3,* and 1,2
1
b.geos, 2100 Korneuburg, Austria
2
Austrian Polar Research Institute, c/o Universität Wien, 1010 Vienna, Austria
3
Department of Geoinformatics—Z_GIS, DK GIScience, Paris Lodron University of Salzburg, 5020 Salzburg, Austria
*
Author to whom correspondence should be addressed.
Academic Editor: Koreen Millard
Remote Sens. 2021, 13(9), 1626; https://doi.org/10.3390/rs13091626
Received: 25 March 2021 / Revised: 16 April 2021 / Accepted: 16 April 2021 / Published: 21 April 2021
Seepage of geological methane through sediments of Arctic lakes might contribute conceivably to the atmospheric methane budget. However, the abundance and precise locations of such seeps are poorly quantified. For Lake Neyto, one of the largest lakes on the Yamal Peninsula in Northwestern Siberia, temporally expanding regions of anomalously low backscatter in C-band SAR imagery acquired in late winter and spring have been suggested to be related to seepage of methane from hydrocarbon reservoirs. However, this hypothesis has not been verified using in-situ observations so far. Similar anomalies have also been identified for other lakes on Yamal, but it is still uncertain whether or how many of them are related to methane seepage. This study aimed to document similar lake ice backscatter anomalies on a regional scale over four study regions (the Yamal Peninsula and Tazovskiy Peninsulas; the Lena Delta in Russia; the National Petroleum Reserve Alaska) during different years using a time series based approach on Google Earth Engine (GEE) that quantifies changes of σ0 from the Sentinel-1 C-band SAR sensor over time. An algorithm for assessing the coverage that takes the number of acquisitions and maximum time between acquisitions into account is presented, and differences between the main operating modes of Sentinel-1 are evaluated. Results show that better coverage can be achieved in extra wide swath (EW) mode, but interferometric wide swath (IW) mode data could be useful for smaller study areas and to substantiate EW results. A classification of anomalies on Lake Neyto from EW Δσ0 images derived from GEE showed good agreement with the classification presented in a previous study. Automatic threshold-based per-lake counting of years where anomalies occurred was tested, but a number of issues related to this approach were identified. For example, effects of late grounding of the ice and anomalies potentially related to methane emissions could not be separated efficiently. Visualizations of Δσ0 images likely reflect the temporal expansions of anomalies and are expected to be particularly useful for identifying target areas for future field-based research. Characteristic anomalies that clearly resemble the ones observed for Lake Neyto could be identified solely visually in the Yamal and Tazovskiy study regions. All data and algorithms produced in the framework of this study are openly provided to the scientific community for future studies and might potentially aid our understanding of geological lake seepage upon the progression of related field-based studies and corresponding evaluations of formation hypotheses. View Full-Text
Keywords: arctic; lake ice; SAR; change detection; methane; Yamal; permafrost; Google Earth Engine arctic; lake ice; SAR; change detection; methane; Yamal; permafrost; Google Earth Engine
Show Figures

Graphical abstract

  • Externally hosted supplementary file 1
    Doi: 10.5281/zenodo.4694533
    Link: https://zenodo.org/record/4694533
    Description: Geospatial raster data and vector data created in the frame of the study "Mapping Arctic Lake Ice Backscatter Anomalies using Sentinel-1 Time Series on Google Earth Engine" and Python code to reproduce the results.
MDPI and ACS Style

Pointner, G.; Bartsch, A. Mapping Arctic Lake Ice Backscatter Anomalies Using Sentinel-1 Time Series on Google Earth Engine. Remote Sens. 2021, 13, 1626. https://doi.org/10.3390/rs13091626

AMA Style

Pointner G, Bartsch A. Mapping Arctic Lake Ice Backscatter Anomalies Using Sentinel-1 Time Series on Google Earth Engine. Remote Sensing. 2021; 13(9):1626. https://doi.org/10.3390/rs13091626

Chicago/Turabian Style

Pointner, Georg, and Annett Bartsch. 2021. "Mapping Arctic Lake Ice Backscatter Anomalies Using Sentinel-1 Time Series on Google Earth Engine" Remote Sensing 13, no. 9: 1626. https://doi.org/10.3390/rs13091626

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

Article Access Map by Country/Region

1
Back to TopTop