Recent Advances in Using Synthetic Aperture Radar for the Monitoring of Lake Ice with Respect to Subcap Gas Seepage in Western Siberia
Abstract
Millions of lakes cover the Arctic tundra. Many of them can be considered as major sources of methane (CH4) to the atmosphere. Methane is among the most important greenhouse gases to be studied for future climate predictions. In general, two main types of methane seeps in sediments of Arctic lakes can be distinguished: Superficial and subcap seeps. Superficial seepage refers to seepage of ecosystem CH4 continuously formed and emitted without storage over geological time periods. Subcap seeps, on the other hand, release CH4 that has been previously sealed by a cryosphere cap and can originate, e.g., from natural gas reservoirs, buried organics associated with glacial sequences or methane hydrates. Superficial seepage has been studied relatively well, especially for Alaskan lakes, and can be incorporated in global climate models. In contrast, subcap seepage is characterized by high spatial and temporal variability and thus cannot be currently incorporated in global climate models, although emissions of subcap seeps are significantly higher and can enhance total CH4 emissions drastically on regional scales. Especially in Western Siberia, the evidence for unquantified, wide-spread subcap seepage through sediments of lakes has increased during recent years.
Here, we present methods to map, on a local level, potential signs of subcap gas emissions that likely manifest in backscatter anomalies characterized by distinct shapes and temporal dynamics in C-band Synthetic Aperture Radar (SAR) imagery of winter and spring ice of lakes in Western Siberia. We establish the likely connection between backscatter anomalies and subcap seepage through a comparison to features in Very High Resolution (VHR) Optical imagery, quantify the spatio-temporal dynamics of the anomalies and discuss the use of auxiliary remote sensing data that could help to advance our understanding of the phenomenon. Additionally, the potential benefits of using big spatio-temporal data processing on cloud computing infrastructures to study the anomalies over regional to pan-Arctic scales are discussed.- Publication:
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AGU Fall Meeting Abstracts
- Pub Date:
- December 2020
- Bibcode:
- 2020AGUFMH011.0020P
- Keywords:
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- 1819 Geographic Information Systems (GIS);
- HYDROLOGY;
- 1855 Remote sensing;
- HYDROLOGY;
- 1856 River channels;
- HYDROLOGY;
- 1857 Reservoirs (surface);
- HYDROLOGY