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

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.

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

2020 ◽  
Author(s):  
Georg Pointner ◽  
Annett Bartsch ◽  
Yury A. Dvornikov ◽  
Alexei V. Kouraev
Keyword(s):  
Synthetic Aperture Radar ◽  
Synthetic Aperture ◽  
The Arctic ◽  
Alos Palsar ◽  
Lake Ice ◽  
Gas Emissions ◽  
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 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.

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

2013 ◽  
Vol 7 (6) ◽  
pp. 1741-1752 ◽  
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.

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

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.

scholarly journals Spaceborne L-Band Synthetic Aperture Radar Data for Geoscientific Analyses in Coastal Land Applications: A Review

2020 ◽  
Vol 12 (14) ◽  
pp. 2228
Author(s):  
Marco Ottinger ◽  
Claudia Kuenzer
Keyword(s):  
Synthetic Aperture Radar ◽  
Anthropogenic Impacts ◽  
Continuous Mapping ◽  
Synthetic Aperture ◽  
Comprehensive Overview ◽  
Synthetic Aperture Radar Data ◽  
L Band ◽  
Sar Data ◽  
Coastal Land ◽  
Aperture Radar

The coastal zone offers among the world’s most productive and valuable ecosystems and is experiencing increasing pressure from anthropogenic impacts: human settlements, agriculture, aquaculture, trade, industrial activities, oil and gas exploitation and tourism. Earth observation has great capability to deliver valuable data at the local, regional and global scales and can support the assessment and monitoring of land- and water-related applications in coastal zones. Compared to optical satellites, cloud-cover does not limit the timeliness of data acquisition with spaceborne Synthetic Aperture Radar (SAR) sensors, which have all-weather, day and night capabilities. Hence, active radar systems demonstrate great potential for continuous mapping and monitoring of coastal regions, particularly in cloud-prone tropical and sub-tropical climates. The canopy penetration capability with long radar wavelength enables L-band SAR data to be used for coastal terrestrial environments and has been widely applied and investigated for the following geoscientific topics: mapping and monitoring of flooded vegetation and inundated areas; the retrieval of aboveground biomass; and the estimation of soil moisture. Human activities, global population growth, urban sprawl and climate change-induced impacts are leading to increased pressure on coastal ecosystems causing land degradation, deforestation and land use change. This review presents a comprehensive overview of existing research articles that apply spaceborne L-band SAR data for geoscientific analyses that are relevant for coastal land applications.

scholarly journals Estimating Forest Stand Height in Savannakhet, Lao PDR Using InSAR and Backscatter Methods with L-Band SAR Data

2021 ◽  
Vol 13 (22) ◽  
pp. 4516
Author(s):  
Helen Blue Parache ◽  
Timothy Mayer ◽  
Kelsey E. Herndon ◽  
Africa Ixmucane Flores-Anderson ◽  
Yang Lei ◽  
...  
Keyword(s):  
Synthetic Aperture Radar ◽  
Forest Stand ◽  
Forest Monitoring ◽  
Synthetic Aperture ◽  
Use Case ◽  
Important Indicator ◽  
L Band ◽  
Sar Data ◽  
Stand Height ◽  
Aperture Radar

Forest stand height (FSH), or average canopy height, serves as an important indicator for forest monitoring. The information provided about above-ground biomass for greenhouse gas emissions reporting and estimating carbon storage is relevant for reporting for Reducing Emissions from Deforestation and Forest Degradation (REDD+). A novel forest height estimation method utilizing a fusion of backscatter and Interferometric Synthetic Aperture Radar (InSAR) data from JAXA’s Advanced Land Observing Satellite Phased Array type L-band Synthetic Aperture Radar (ALOS PALSAR) is applied to a use case in Savannakhet, Lao. Compared with LiDAR, the estimated height from the fusion method had an RMSE of 4.90 m and an R2 of 0.26. These results are comparable to previous studies using SAR estimation techniques. Despite limitations of data quality and quantity, the Savannakhet, Lao use case demonstrates the applicability of these techniques utilizing L-band SAR data for estimating FSH in tropical forests and can be used as a springboard for use of L-band data from the future NASA-ISRO SAR (NISAR) mission.

scholarly journals High spatial and temporal resolution L- and C-band Synthetic Aperture Radar data analysis from the yearlong MOSAiC expedition

2021 ◽  
Author(s):  
Malin Johansson ◽  
Suman Singha ◽  
Gunnar Spreen ◽  
Stephen Howell ◽  
Shin-ichi Sobue ◽  
...  
Keyword(s):  
Sea Ice ◽  
Synthetic Aperture Radar ◽  
Temporal Resolution ◽  
Synthetic Aperture ◽  
The Arctic ◽  
Significant Shift ◽  
Synthetic Aperture Radar Data ◽  
The Mean ◽  
L Band ◽  
Aperture Radar

<p>In the yearlong MOSAIC expedition (2019-2020) R/V Polarstern drifted with sea ice through the Arctic Ocean, with the goal to continually monitor changes in the coupled ocean-ice-atmosphere system throughout the seasons. A substantial amount of synthetic aperture radar (SAR) satellite images overlapping the campaign was collected. Here, we investigate the change in polarimetric features over sea ice from the freeze up to the advanced melt season using fully polarimetric L-band images from the ALOS-2 PALSAR-2 and fully polarimetric C-band images from the RADARSAT-2 satellite SAR sensors.</p><p>Three different sea ice types are investigated, young ice, level first year ice and deformed first and second-year ice. Areas of deformed and level sea ice were observed in the vicinity of R/V Polarstern and these areas are included whenever possible in the yearlong time series.</p><p>Comparing the different sea ice types, we observe that during the freezing season there is a larger difference in the co-polarization channels between smooth and deformed ice in L-band compared to C-band. Similar to earlier findings we observe larger differences between young ice and deformed ice backscatter values in the L-band data compared to the C-band data. Moreover, throughout the year the HV-backscatter values show larger differences between level and deformed sea ice in L-band than C-band. The L-band data variability is significantly smaller for the level sea ice compared to the deformed sea ice, and this variability was also smaller than that observed for the overlapping C-band data. Thus L-band data could be more suitable to reliable separate deformed from level sea ice areas.   </p><p>Within the L-band images a noticeable shift towards higher backscatter values in early melt season compared to the freezing season for all polarimetric channels is observed, though no such strong trend is found in the C-band data. The change in backscatter values is first noticeable in the C-band images and later followed by a change in the L-band images, probably caused by their different penetration depth and volume scattering sensitivities. This change also results in a smaller backscatter variability.</p><p>The polarization difference (PD; VV-HH on a linear scale) show a seasonal dependency for the smooth and deformed sea ice within the L-band data, whereas for the C-band data no such trend is observed. For the L-band data were the PD variability for all ice classes reasonably small for the freezing season, with a significant shift towards larger variability during the early melt season, though during this time period the mean PD values remained similar. However, once the temperatures reached above 0°C both the variability and the mean values increased significantly.</p><p>Overall, our results demonstrate that the C- and L-band data are complementary to one another and that through their slightly different dependencies on season and sea ice types, a combination of the two frequencies can aid improved sea ice classification. The availability of a high spatial and temporal resolution dataset combined with in-situ information ensures that seasonal changes can be fully explored.</p>

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

2018 ◽  
Vol 10 (12) ◽  
pp. 1873 ◽  
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.

DETECTION AND MAPPING OF FLOATING OIL EMULSIONS WITH SYNTHETIC APERTURE RADAR (SAR) IMAGERY

2014 ◽  
Vol 2014 (1) ◽  
pp. 300657 ◽  
Author(s):  
Oscar Garcia-Pineda ◽  
Ian MacDonald ◽  
Chuanmin Hu ◽  
Jan Svejkovsky ◽  
Mark Hess ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Synthetic Aperture Radar ◽  
Ocean Surface ◽  
Synthetic Aperture ◽  
Water Detection ◽  
L Band ◽  
Sar Imagery ◽  
Remote Sensing Techniques ◽  
Oil Emulsions ◽  
Aperture Radar

Detection of floating oil on the ocean surface, and particularly thick layers, is crucial for emergency response to accidental oil discharges. While detection of oil presence on the ocean surface is relatively easy under most conditions with a variety of remote sensing techniques, estimation of the thickness of oil layers is technically challenging. In this paper we use Synthetic Aperture Radar (SAR) imagery collected during the DeepWater Horizon (DWH) oil spill and the Texture Classifier Neural Network Algorithm (TCNNA) to identify SAR image signatures that may correspond to regions of very thick emulsified oil. These locations were generally consistent with sea level observations and optical and thermal remote sensing instruments. Oil emulsions form after crude oil is discharged in the ocean and is subjected to weathering and coagulation processes that increase thicknesses of floating oil layers. The method of detection identifies regions of increased radar backscattering within larger regions of oil-covered water. Detection is dependent on SAR incident angles and the type of SAR beam mode configuration. L-band SAR was found to have the largest window of incidence angles (16 – 38o off-nadir) that could be used to detect oil emulsions. C-band SAR showed a narrower window (18 – 32o off-nadir) than L-band, while X-band SAR had the narrowest window (20 – 31o off-nadir). The results suggest that in case of future spills in the ocean, SAR data may be used to find locations of thick oil to help make management decisions.

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

2013 ◽  
Vol 7 (3) ◽  
pp. 2061-2088 ◽  
Author(s):  
M. Engram ◽  
K. W. Anthony ◽  
F. J. Meyer ◽  
G. Grosse
Keyword(s):  
Synthetic Aperture Radar ◽  
Fish Habitat ◽  
Synthetic Aperture ◽  
Lake Ice ◽  
Local Climate ◽  
Backscatter Intensity ◽  
Arctic Alaska ◽  
L Band ◽  
Aperture Radar

Abstract. Synthetic aperture radar (SAR) backscatter from floating lake ice is high, in contrast to low backscatter values from lake ice that is frozen completely to the lake bed (grounded ice). Knowledge of floating vs. grounded lake ice is useful for determining winter water supply, fish habitat, heat transfer to permafrost, and to observe changes in perennial lake ice status that could correlate with variations in local climate. Here, we compare calibrated L-band (23.6 cm wavelength) single- and L-band quadrature-polarized SAR return to the backscatter intensity of C-band (5.6 cm wavelength) SAR from floating and grounded lake ice over two regions in Alaska. Our primary goal was to determine if C or L-band is more useful to distinguish floating from grounded lake ice. C-band SAR showed far greater contrast between floating and grounded lake ice, making it the preferred wavelength for identifying lake ice regimes. L-band SAR backscatter was much lower from floating ice than C-band and it was different for our two study regions. Furthermore, since L-band is sensitive to ebullition bubbles trapped by lake ice (bubbles increase backscatter), this study helps to elucidate potential confounding factors of bubbles in efforts to detect floating vs. grounded ice using L-band SAR.

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

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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