Mapping potential signs of gas emissions in ice of lake Neyto, Yamal, Russia using synthetic aperture radar and multispectral remote sensing data

Abstract. Regions of anomalously low backscatter in C-band Synthetic Aperture Radar (SAR) imagery of lake ice of lake Neyto in northwestern Siberia have been suggested to be caused by emissions of gas (methane from hydrocarbon reservoirs) through the lake's sediments before. However, to assess this connection, only analyses of data from boreholes in the vicinity of lake Neyto and visual comparisons to medium-resolution optical imagery have been provided so far due to lack of in situ observations of the lake ice itself. These observations are impeded due to accessibility and safety issues. Geospatial analyses and innovative combinations of satellite data sources are therefore proposed to advance our understanding of this phenomenon. In this study, we assess the nature of the backscatter anomalies in Sentinel-1 C-band SAR images in combination with Very High Resolution (VHR) WorldView-2 optical imagery. We present methods to automatically map backscatter anomaly regions from the C-band SAR data (40 m pixel-spacing) and holes in lake ice from the VHR data (0.5 m pixel-spacing), and examine their spatial relationships. The reliability of the SAR method is evaluated through comparison between different acquisition modes. The results show that the majority of mapped holes in the VHR data are clearly related to anomalies in SAR imagery acquired a few days earlier and also more than a month before, supporting the hypothesis of gas emissions as the cause of the backscatter anomalies. Further, a significant expansion of backscatter anomaly regions in spring is documented and quantified in all analysed years 2015 to 2019. Our study suggests that the backscatter anomalies might be caused by expanding cavities in the lake ice, formed by strong emissions of gas, which could also explain outcomes of polarimetric analyses of auxiliary L-band ALOS PALSAR-2 data. C-band SAR data is considered to be valuable for the identification of lakes showing similar phenomena across larger areas in the Arctic in future studies.

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Mapping potential signs of gas emissions in ice of Lake Neyto, Yamal, Russia, using synthetic aperture radar and multispectral remote sensing data

The Cryosphere ◽

10.5194/tc-15-1907-2021 ◽

2021 ◽

Vol 15 (4) ◽

pp. 1907-1929

Author(s):

Georg Pointner ◽

Annett Bartsch ◽

Yury A. Dvornikov ◽

Alexei V. Kouraev

Keyword(s):

Synthetic Aperture Radar ◽

Synthetic Aperture ◽

The Arctic ◽

Lake Ice ◽

Gas Emissions ◽

L Band ◽

Optical Imagery ◽

Sar Data ◽

Sar Imagery ◽

Aperture Radar

Abstract. Regions of anomalously low backscatter in C-band synthetic aperture radar (SAR) imagery of lake ice of Lake Neyto in northwestern Siberia have been suggested to be caused by emissions of gas (methane from hydrocarbon reservoirs) through the lake’s sediments. However, to assess this connection, only analyses of data from boreholes in the vicinity of Lake Neyto and visual comparisons to medium-resolution optical imagery have been provided due to a lack of in situ observations of the lake ice itself. These observations are impeded due to accessibility and safety issues. Geospatial analyses and innovative combinations of satellite data sources are therefore proposed to advance our understanding of this phenomenon. In this study, we assess the nature of the backscatter anomalies in Sentinel-1 C-band SAR images in combination with very high resolution (VHR) WorldView-2 optical imagery. We present methods to automatically map backscatter anomaly regions from the C-band SAR data (40 m pixel spacing) and holes in lake ice from the VHR data (0.5 m pixel spacing) and examine their spatial relationships. The reliability of the SAR method is evaluated through comparison between different acquisition modes. The results show that the majority of mapped holes (71 %) in the VHR data are clearly related to anomalies in SAR imagery acquired a few days earlier, and similarities to SAR imagery acquired more than a month before are evident, supporting the hypothesis that anomalies may be related to gas emissions. Further, a significant expansion of backscatter anomaly regions in spring is documented and quantified in all analysed years 2015 to 2019. Our study suggests that the backscatter anomalies might be caused by lake ice subsidence and consequent flooding through the holes over the ice top leading to wetting and/or slushing of the snow around the holes, which might also explain outcomes of polarimetric analyses of auxiliary L-band Advanced Land Observing Satellite (ALOS) Phased Array type L-band Synthetic Aperture Radar-2 (PALSAR-2) data. C-band SAR data are considered to be valuable for the identification of lakes showing similar phenomena across larger areas in the Arctic in future studies.

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Artificial Neural Network Modeling of High Arctic Phytomass Using Synthetic Aperture Radar and Multispectral Data

10.32920/14637897.v1 ◽

2021 ◽

Author(s):

Adam Collingwood ◽

Paul Treitz ◽

Francois Charbonneau ◽

David M. Atkinson

Keyword(s):

Synthetic Aperture Radar ◽

High Arctic ◽

Synthetic Aperture ◽

The Arctic ◽

Optical Data ◽

Neural Network Modeling ◽

Artificial Neural ◽

Sar Data ◽

The Relationship ◽

Aperture Radar

Vegetation in the Arctic is often sparse, spatially heterogeneous, and difficult to model. Synthetic Aperture Radar (SAR) has shown some promise in above-ground phytomass estimation at sub-arctic latitudes, but the utility of this type of data is not known in the context of the unique environments of the Canadian High Arctic. In this paper, Artificial Neural Networks (ANNs) were created to model the relationship between variables derived from high resolution multi-incidence angle RADARSAT-2 SAR data and optically-derived (GeoEye-1) Soil Adjusted Vegetation Index (SAVI) values. The modeled SAVI values (i.e., from SAR variables) were then used to create maps of above-ground phytomass across the study area. SAVI model results for individual ecological classes of polar semi-desert, mesic heath, wet sedge, and felsenmeer were reasonable, with r2 values of 0.43, 0.43, 0.30, and 0.59, respectively. When the outputs of these models were combined to analyze the relationship between the model output and SAVI as a group, the r2 value was 0.60, with an 8% normalized root mean square error (% of the total range of phytomass values), a positive indicator of a relationship. The above-ground phytomass model also resulted in a very strong relationship (r2 = 0.87) between SAR-modeled and field-measured phytomass. A positive relationship was also found between optically derived SAVI values and field measured phytomass (r2 = 0.79). These relationships demonstrate the utility of SAR data, compared to using optical data alone, for modeling above-ground phytomass in a high arctic environment possessing relatively low levels of vegetation.

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Characterization of L-band synthetic aperture radar (SAR) backscatter from floating and grounded thermokarst lake ice in Arctic Alaska

The Cryosphere ◽

10.5194/tc-7-1741-2013 ◽

2013 ◽

Vol 7 (6) ◽

pp. 1741-1752 ◽

Cited By ~ 15

Author(s):

M. Engram ◽

K. W. Anthony ◽

F. J. Meyer ◽

G. Grosse

Keyword(s):

Synthetic Aperture Radar ◽

Synthetic Aperture ◽

The Arctic ◽

Great Promise ◽

Late Winter ◽

Lake Ice ◽

Arctic Alaska ◽

L Band ◽

Phase Water ◽

Aperture Radar

Abstract. Radar remote sensing is a well-established method to discriminate lakes retaining liquid-phase water beneath winter ice cover from those that do not. L-band (23.6 cm wavelength) airborne radar showed great promise in the 1970s, but spaceborne synthetic aperture radar (SAR) studies have focused on C-band (5.6 cm) SAR to classify lake ice with no further attention to L-band SAR for this purpose. Here, we examined calibrated L-band single- and quadrature-polarized SAR returns from floating and grounded lake ice in two regions of Alaska: the northern Seward Peninsula (NSP) where methane ebullition is common in lakes and the Arctic Coastal Plain (ACP) where ebullition is relatively rare. We found average backscatter intensities of −13 dB and −16 dB for late winter floating ice on the NSP and ACP, respectively, and −19 dB for grounded ice in both regions. Polarimetric analysis revealed that the mechanism of L-band SAR backscatter from floating ice is primarily roughness at the ice–water interface. L-band SAR showed less contrast between floating and grounded lake ice than C-band; however, since L-band is sensitive to ebullition bubbles trapped by lake ice (bubbles increase backscatter), this study helps elucidate potential confounding factors of grounded ice in methane studies using SAR.

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Flood Mapping in a Complex Environment Using Bistatic TanDEM-X/TerraSAR-X InSAR Coherence

Remote Sensing ◽

10.3390/rs10121873 ◽

2018 ◽

Vol 10 (12) ◽

pp. 1873 ◽

Cited By ~ 17

Author(s):

Chayma Chaabani ◽

Marco Chini ◽

Riadh Abdelfattah ◽

Renaud Hostache ◽

Karem Chokmani

Keyword(s):

Synthetic Aperture Radar ◽

Data Acquisition ◽

Added Value ◽

Synthetic Aperture ◽

Flood Mapping ◽

Acquisition Time ◽

Data Acquisition Time ◽

Sar Data ◽

Sar Imagery ◽

Aperture Radar

In this paper, we assess the flood mapping capabilities of the X-band Synthetic Aperture Radar (SAR) imagery acquired by the bistatic pair TanDEM-X/TerraSAR-X (TDX/TSX). The main objective is to investigate the added value of the bistatic TDX/TSX Interferometric Synthetic Aperture Radar (InSAR) coherence in addition to the SAR backscatter in the context of inundation mapping. As a classifier, we consider a Random Forest (RF) classification scheme using TDX/TSX SAR intensities and their bistatic InSAR coherence to extract the flood extent map. To evaluate the classification results and as no “ground truth” was available at the SAR data acquisition time, we set up a LISFLOOD-FP hydraulic model for simulating the temporal evolution of the flood water. The flood map simulated by the model shows good performances with an Overall Accuracy (OA) of 97.92 % and a Critical Success Index (CSI) of 94 . 01 % . The SAR-derived flood map is then compared to the LISFLOOD-FP extent map simulated at the SAR data acquisition time. As a test case, we consider the flooding event of the Richelieu River that occurred in the Montérégie region of Quebec (Canada) from April to June 2011. Experimental results highlight the potential of the bistatic InSAR coherence for more accurate flood mapping in a complex landscape with urban and vegetation areas. The classification results of the SAR-derived flood map with respect to the LISFLOOD-FP flood map reach an OA of 78.65 % and a Precision of 82.08 % when integrating the bistatic InSAR coherence. These classification OA and Precision values are 69.63 % and 64.52 % , respectively, using only the TDX/TSX SAR intensity.

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Artificial Neural Network Modeling of High Arctic Phytomass Using Synthetic Aperture Radar and Multispectral Data

10.32920/14637897 ◽

2021 ◽

Author(s):

Adam Collingwood ◽

Paul Treitz ◽

Francois Charbonneau ◽

David M. Atkinson

Keyword(s):

Synthetic Aperture Radar ◽

High Arctic ◽

Synthetic Aperture ◽

The Arctic ◽

Optical Data ◽

Neural Network Modeling ◽

Artificial Neural ◽

Sar Data ◽

The Relationship ◽

Aperture Radar

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Detection and Mapping of Active Landslides before Impoundment in the Baihetan Reservoir Area (China) Based on the Time-Series InSAR Method

Remote Sensing ◽

10.3390/rs13163213 ◽

2021 ◽

Vol 13 (16) ◽

pp. 3213

Author(s):

Jiawei Dun ◽

Wenkai Feng ◽

Xiaoyu Yi ◽

Guoqiang Zhang ◽

Mingtang Wu

Keyword(s):

Synthetic Aperture Radar ◽

Google Earth ◽

Synthetic Aperture ◽

Analysis Method ◽

Alos Palsar ◽

Jinsha River ◽

Reservoir Area ◽

Sar Data ◽

Small Baseline ◽

Aperture Radar

Many potential landslides occured in the Baihetan reservoir area before impoundment. After impoundment, these landslides may still slide, affecting the safe operation of the reservoir area (e.g., causing barrier lakes and floods). Identifying the locations of landslides and their distribution pattern has attracted attention in China and globally. In addition, due to the rolling terrain of the reservoir area, synthetic aperture radar (SAR) imaging will affect the interactive synthetic aperture radar (InSAR) deformation results. Only by obtaining effective deformation information can active landslides be accurately identified. Therefore, the banks of the Hulukou Xiangbiling section of the Baihetan reservoir area before impoundment in the Jinsha River Basin were studied in this paper. Using terrain data and the satellite parameters from Sentinel-1A ascending and descending orbits and ALOS PALSAR ascending orbit, the line-of-sight visibility was quantitatively analyzed, and an analysis method was proposed. Based on the SAR data visibility analysis, the small baseline subset (SBAS) technique was used to process the SAR data to acquire effective deformation. InSAR deformation data was combined with Google Earth imagery to identify 25 active landslides. After field verification, 21 active landslides (14 new) were determined. Most of the active landslides are controlled by faults, and the strata of the other landslides are relatively weak. This InSAR analysis method based on SAR data visibility can provide a reference for identifying and analyzing active landslides in other complicated terrain.

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A study of the onset of melt over the Arctic Ocean in RADARSAT synthetic aperture radar data

Journal of Geophysical Research Atmospheres ◽

10.1029/2002jc001363 ◽

2003 ◽

Vol 108 (C11) ◽

Cited By ~ 22

Author(s):

R. Kwok

Keyword(s):

Synthetic Aperture Radar ◽

Arctic Ocean ◽

Radar Data ◽

Synthetic Aperture ◽

The Arctic ◽

Synthetic Aperture Radar Data ◽

The Arctic Ocean ◽

Aperture Radar

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Multi-Sensor InSAR Analysis of Progressive Land Subsidence over the Coastal City of Urayasu, Japan

Remote Sensing ◽

10.3390/rs10081304 ◽

2018 ◽

Vol 10 (8) ◽

pp. 1304 ◽

Cited By ~ 13

Author(s):

Yusupujiang Aimaiti ◽

Fumio Yamazaki ◽

Wen Liu

Keyword(s):

Synthetic Aperture Radar ◽

Survey Data ◽

Land Subsidence ◽

Ground Deformation ◽

Tohoku Earthquake ◽

Synthetic Aperture ◽

Alos Palsar ◽

The 2011 Tohoku Earthquake ◽

L Band ◽

Aperture Radar

In earthquake-prone areas, identifying patterns of ground deformation is important before they become latent risk factors. As one of the severely damaged areas due to the 2011 Tohoku earthquake in Japan, Urayasu City in Chiba Prefecture has been suffering from land subsidence as a part of its land was built by a massive land-fill project. To investigate the long-term land deformation patterns in Urayasu City, three sets of synthetic aperture radar (SAR) data acquired during 1993–2006 from European Remote Sensing satellites (ERS-1/-2 (C-band)), during 2006–2010 from the Phased Array L-band Synthetic Aperture Radar onboard the Advanced Land Observation Satellite (ALOS PALSAR (L-band)) and from 2014–2017 from the ALOS-2 PALSAR-2 (L-band) were processed by using multitemporal interferometric SAR (InSAR) techniques. Leveling survey data were also used to verify the accuracy of the InSAR-derived results. The results from the ERS-1/-2, ALOS PALSAR and ALOS-2 PALSAR-2 data processing showed continuing subsidence in several reclaimed areas of Urayasu City due to the integrated effects of numerous natural and anthropogenic processes. The maximum subsidence rate of the period from 1993 to 2006 was approximately 27 mm/year, while the periods from 2006 to 2010 and from 2014 to 2017 were approximately 30 and 18 mm/year, respectively. The quantitative validation results of the InSAR-derived deformation trend during the three observation periods are consistent with the leveling survey data measured from 1993 to 2017. Our results further demonstrate the advantages of InSAR measurements as an alternative to ground-based measurements for land subsidence monitoring in coastal reclaimed areas.

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RASF and DEDR-POCS Image Fusion through Multi-layer Perceptron in SAR Imagery Systems

Journal of Computational Systems and ICTs ◽

10.35429/jcsi.2019.16.5.20.26 ◽

2019 ◽

pp. 20-26

Author(s):

Israel Yañez-Vargas ◽

Joel Quintanilla-Domínguez ◽

Gabriel Aguilera-Gonzalez

Keyword(s):

Synthetic Aperture Radar ◽

Image Fusion ◽

Low Cost ◽

Synthetic Aperture ◽

Discrete Wavelet ◽

Multi Layer Perceptron ◽

Robust Adaptive ◽

Sar Imagery ◽

Design Regularization ◽

Aperture Radar

This paper presents a novel multi-layer perceptron (MLP) based image fusion technique, which fuses two synthetic aperture radar (SAR) images, obtained from the same spatial reflectivity map, acquired with a conventional low-cost fractional synthetic aperture radar (Fr-SAR) system, enhanced via two different methodologies. The first image is enhanced using the traditional descriptive experiment design regularization (DEDR) framework through the projection onto convex solution sets (POCS) method; the second image is enhanced with the DEDR framework by incorporating the robust adaptive spatial filtering (RASF) solution operator. This work describes a MLP based technique applied to the pixel level multi-focus fusion problem characterized by the use of image windows with the idea of reducing noise and determining which pixel is clearer between the two images. Experimental results show that the proposed novel method outperforms the discrete wavelet transform based most competing approach.

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The potential of using Sentinel-1 and ALOS PALSAR-2 data for characterizing West Siberian lake ice backscatter anomalies

10.5194/egusphere-egu21-11931 ◽

2021 ◽

Author(s):

Georg Pointner ◽

Annett Bartsch

Keyword(s):

Thick Layer ◽

Arctic Lakes ◽

The Arctic ◽

Late Winter ◽

Alos Palsar ◽

Lake Ice ◽

In Situ Data ◽

L Band ◽

Ice Regime ◽

Sar Imagery

Millions of lakes and ponds occupy large areas of the Arctic discontinuous and continuous permafrost zones. During most of the year, the surfaces of these lakes remain covered by a thick layer of ice. Synthetic Aperture Radar (SAR) data have shown to be useful for studying the ice on Arctic lakes, especially for monitoring lake ice phenology and the grounding state of the ice (ice frozen to the lakebed versus floating lake ice). Significant backscatter is often observed from the floating ice regime in C-band due to scattering on a rough ice-water interface.Recent research has revealed features of anomalously low backscatter in Sentinel-1 C-band SAR imagery on some of the West Siberian lakes that likely belong to the floating ice regime. These anomalies are characterized by prominent shapes and sizes and seem to expand throughout late winter and/or spring. It is currently assumed that some of these features are related to strong emissions of natural gas (methane from hydrocarbon reservoirs), making it important to assess their origin in detail and understand the associated mechanisms. However, in-situ data are still missing.Here, we assess the potential of the combined use of C-band Sentinel-1 (freely available) and L-band ALOS PALSAR-2 data&#160; (available through JAXA PI agreement #3068002) to study the backscatter anomalies. We highlight the differences between observed backscatter from the two sensors with respect to different surface types (ground-fast lake ice, floating lake ice and anomalies) and investigate backscatter differences between frozen and melting conditions. Further, polarimetric classification is performed on L-band PALSAR-2 imagery, which reveals differences in scattering mechanisms between anomalies and floating lake ice.

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