scholarly journals InSAR Monitoring of Arctic Landfast Sea Ice Deformation Using L-Band ALOS-2, C-Band Radarsat-2 and Sentinel-1

2021 ◽  
Vol 13 (22) ◽  
pp. 4570
Author(s):  
Zhaohua Chen ◽  
Benoit Montpetit ◽  
Sarah Banks ◽  
Lori White ◽  
Amir Behnamian ◽  
...  
Keyword(s):  
Time Series ◽  
Sea Ice ◽  
The Arctic ◽  
Process Time ◽  
Time Interval ◽  
L Band ◽  
Sar Imagery ◽  
Landfast Sea Ice ◽  
Small Baseline ◽  
The Stability

Arctic amplification is accelerating changes in sea ice regimes in the Canadian Arctic with later freeze-up and earlier melt events, adversely affecting Arctic wildlife and communities that depend on the stability of sea ice conditions. To monitor both the rate and impact of such change, there is a need to accurately measure sea ice deformation, an important component for understanding ice motion and polar climate. The objective of this study is to determine the spatial-temporal pattern of deformation over landfast ice in the Arctic using time series SAR imagery. We present Interferometric Synthetic Aperture Radar (InSAR) monitoring of Arctic landfast sea ice deformation using C-band Radarsat-2, Sentinel-1 and L-band ALOS-2 in this paper. The small baseline subset (SBAS) approach was explored to process time series observations for retrieval of temporal deformation changes along a line-of-sight direction (LOS) over the winter. It was found that temporal and spatial patterns of deformation observed from different sensors were generally consistent. Horizontal and vertical deformations were also retrieved by a multi-dimensional SBAS technique using both ascending and descending Sentinel-1 observations. Results showed a horizontal deformation in the range of -95-85 cm, and vertical deformation in the range of -41-63 cm in Cambridge Bay, Nunavut, Canada during February-April 2019. High coherence over ice from C-band was maintained over a shorter time interval of acquisitions than L-band due to temporal decorrelation.

scholarly journals InSAR monitoring of Arctic land fast sea ice deformation using L-band ALOS-2, C-band Radarsat-2 and Sentinel-1

2021 ◽  
Author(s):  
Zhaohua Chen ◽  
Benoit Montpetit ◽  
Sarah Banks ◽  
Lori White ◽  
Amir Behnamian ◽  
...  
Keyword(s):  
Sea Ice ◽  
Process Time ◽  
Polar Climate ◽  
Ice Surface ◽  
Ice Conditions ◽  
Small Baseline Subset ◽  
L Band ◽  
Landfast Sea Ice ◽  
Small Baseline ◽  
The Stability

Abstract. Arctic amplification is accelerating changes in sea ice regimes in the Canadian Arctic with later freeze-up and earlier melt events, adversely affecting Arctic wildlife and communities that depend on the stability of the sea ice conditions. To monitor both the rate and impact of such change, there is a need to accurately measure sea ice deformation, an important component for understanding ice motion and polar climate. This paper presents Interferometric Synthetic Aperture Radar (InSAR) monitoring of Arctic landfast sea ice deformation as a result of thickness changes measured from ice draft and surface height using C-band Radarsat-2, Sentinel-1 and L-band ALOS-2. The small baseline subset (SBAS) approach was explored to process time series observations for retrieval of temporal deformation changes over the winter. Sea ice deformation (subsidence and uplift in the range of −32–57 cm) detected from satellite SAR data in Cambridge Bay, Nunavut, Canada during the winter of 2018–2019 was found to be in a range of values corresponding to the ice draft growth (30–62 cm) measured from an in-situ ice profiler. The trends of InSAR observations from Sentinel-1 were also consistent with ice surface height changes along two ground tracks detected from ICESat-2. SAR backscatter from Sentinel-1 also corresponded to the surface height with strong correlation coefficient (0.49–0.83). High coherence over ice from C-band was maintained over a shorter acquisition interval than L-band due to temporal decorrelation.

Landfast ice zoning from SAR imagery

2020 ◽  
Author(s):  
Valeria Selyuzhenok ◽  
Denis Demchev ◽  
Thomas Krumpen
Keyword(s):  
Time Series ◽  
Sea Ice ◽  
Ice Cover ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Ice Drift ◽  
Landfast Ice ◽  
Sea Ice Drift ◽  
Ice Conditions ◽  
Sar Imagery

<p>Landfast sea ice is a dominant sea ice feature of the Arctic coastal region. As a part of Arctic sea ice cover, landfast ice is an important part of coastal ecosystem, it provides functions as a climate regulator and platform for human activity. Recent changes in sea ice conditions in the Arctic have also affected landfast ice regime. At the same time, industrial interest in the Arctic shelf seas continue to increase. Knowledge on local landfast ice conditions are required to ensure safety of on ice operations and accurate forecasting.  In order to obtain a comprehensive information on landfast ice state we use a time series of wide swath SAR imagery.  An automatic sea ice tracking algorithm was applied to the sequential SAR images during the development stage of landfast ice cover. The analysis of resultant time series of sea ice drift allows to classify homogeneous sea ice drift fields and timing of their attachment to the landfast ice. In addition, the drift data allows to locate areas of formation of grounded sea ice accumulation called stamukha. This information сan be useful for local landfast ice stability assessment. The study is supported by the Russian Foundation for Basic Research (RFBR) grant 19-35-60033.</p>

scholarly journals Mapping pan-Arctic landfast sea ice stability using Sentinel-1 interferometry

2019 ◽  
Vol 13 (2) ◽  
pp. 557-577 ◽  
Author(s):  
Dyre O. Dammann ◽  
Leif E. B. Eriksson ◽  
Andrew R. Mahoney ◽  
Hajo Eicken ◽  
Franz J. Meyer
Keyword(s):  
Sea Ice ◽  
The Arctic ◽  
Sea Ice Extent ◽  
Marginal Seas ◽  
Nares Strait ◽  
Novel Approach ◽  
Landfast Ice ◽  
Landfast Sea Ice ◽  
Fringe Patterns ◽  
The Stability

Abstract. Arctic landfast sea ice has undergone substantial changes in recent decades, affecting ice stability and including potential impacts on ice travel by coastal populations and on industry ice roads. We present a novel approach for evaluating landfast sea ice stability on a pan-Arctic scale using Synthetic Aperture Radar Interferometry (InSAR). Using Sentinel-1 images from spring 2017, we discriminate between bottomfast, stabilized, and nonstabilized landfast ice over the main marginal seas of the Arctic Ocean (Beaufort, Chukchi, East Siberian, Laptev, and Kara seas). This approach draws on the evaluation of relative changes in interferometric fringe patterns. This first comprehensive assessment of Arctic bottomfast sea ice extent has revealed that most of the bottomfast sea ice is situated around river mouths and coastal shallows. The Laptev and East Siberian seas dominate the aerial extent, covering roughly 4100 and 5100 km2, respectively. These seas also contain the largest extent of stabilized and nonstabilized landfast ice, but are subject to the largest uncertainties surrounding the mapping scheme. Even so, we demonstrate the potential for using InSAR for assessing the stability of landfast ice in several key regions around the Arctic, providing a new understanding of how stability may vary between regions. InSAR-derived stability may serve for strategic planning and tactical decision support for different uses of coastal ice. In a case study of the Nares Strait, we demonstrate that interferograms may reveal early-warning signals for the breakup of stationary sea ice.

scholarly journals Variability of scaling time series in the Arctic sea-ice drift dynamics

Ocean Science ◽  
2010 ◽  
Vol 6 (1) ◽  
pp. 211-217 ◽  
Author(s):  
A. Chmel ◽  
V. N. Smirnov ◽  
I. B. Sheikin
Keyword(s):  
Time Series ◽  
Wind Speed ◽  
Sea Ice ◽  
Time Windows ◽  
Distribution Functions ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Time Interval ◽  
Research Station ◽  
Ice Pack

Abstract. The motion of an individual ice floe in the Arctic Ocean was monitored at the Russian research station North Pole 35 established on the ice pack in 2008. The ice floe speed (V) was found to be correlated with wind speed (v) in main features, such as the positions of maxima and minima of V and v. However, the fine structure of the V-variation cannot be explained by the wind forcing alone. There were periods of time when the floe drift was affected by the interactions of ice floes between each other or by the periodical forcing due to either the Coriolis inertia effect or the tidal activity. These data were compared with the "waiting times" statistics that are the distributions of time intervals between subsequent, sufficiently strong changes in the kinetic energy of drifting ice floe. These distributions were measured in several time windows differing in the average wind speed and wind direction, and/or in the mechanical state of the ice pack. The distribution functions N (t>τ), where N is the number of successive events of energy change separated by the time interval t that exceeds τ, constructed in different time windows demonstrate fractal or a multifractal nature of the time series during motion in the consolidated ice pack but were truly random when the ice floe drifted in the highly fragmented sea ice. The latter result shows the existence of a relationship between the long-range mechanical interactions in the pack and long-term memory (time scaling behaviour) of the sea-ice motion.

scholarly journals Sea Ice Concentration Products over Polar Regions with Chinese FY3C/MWRI Data

2021 ◽  
Vol 13 (11) ◽  
pp. 2174
Author(s):  
Lijian Shi ◽  
Sen Liu ◽  
Yingni Shi ◽  
Xue Ao ◽  
Bin Zou ◽  
...  
Keyword(s):  
Time Series ◽  
Sea Ice ◽  
Ocean Circulation ◽  
The Arctic ◽  
Retrieval Algorithm ◽  
Atmospheric Effects ◽  
Observation Data ◽  
Long Time Series ◽  
Ice Concentration ◽  
Long Time

Polar sea ice affects atmospheric and ocean circulation and plays an important role in global climate change. Long time series sea ice concentrations (SIC) are an important parameter for climate research. This study presents an SIC retrieval algorithm based on brightness temperature (Tb) data from the FY3C Microwave Radiation Imager (MWRI) over the polar region. With the Tb data of Special Sensor Microwave Imager/Sounder (SSMIS) as a reference, monthly calibration models were established based on time–space matching and linear regression. After calibration, the correlation between the Tb of F17/SSMIS and FY3C/MWRI at different channels was improved. Then, SIC products over the Arctic and Antarctic in 2016–2019 were retrieved with the NASA team (NT) method. Atmospheric effects were reduced using two weather filters and a sea ice mask. A minimum ice concentration array used in the procedure reduced the land-to-ocean spillover effect. Compared with the SIC product of National Snow and Ice Data Center (NSIDC), the average relative difference of sea ice extent of the Arctic and Antarctic was found to be acceptable, with values of −0.27 ± 1.85 and 0.53 ± 1.50, respectively. To decrease the SIC error with fixed tie points (FTPs), the SIC was retrieved by the NT method with dynamic tie points (DTPs) based on the original Tb of FY3C/MWRI. The different SIC products were evaluated with ship observation data, synthetic aperture radar (SAR) sea ice cover products, and the Round Robin Data Package (RRDP). In comparison with the ship observation data, the SIC bias of FY3C with DTP is 4% and is much better than that of FY3C with FTP (9%). Evaluation results with SAR SIC data and closed ice data from RRDP show a similar trend between FY3C SIC with FTPs and FY3C SIC with DTPs. Using DTPs to present the Tb seasonal change of different types of sea ice improved the SIC accuracy, especially for the sea ice melting season. This study lays a foundation for the release of long time series operational SIC products with Chinese FY3 series satellites.

scholarly journals Sea Ice Concentration and Sea Ice Extent Mapping with L-Band Microwave Radiometry and GNSS-R Data from the FFSCat Mission Using Neural Networks

2021 ◽  
Vol 13 (6) ◽  
pp. 1139
Author(s):  
David Llaveria ◽  
Juan Francesc Munoz-Martin ◽  
Christoph Herbert ◽  
Miriam Pablos ◽  
Hyuk Park ◽  
...  
Keyword(s):  
Sea Ice ◽  
Microwave Radiometer ◽  
Cost Effective ◽  
The Arctic ◽  
Sea Ice Concentration ◽  
Sea Ice Extent ◽  
Neural Network Approach ◽  
Ice Concentration ◽  
L Band ◽  
Moderate Cost

CubeSat-based Earth Observation missions have emerged in recent times, achieving scientifically valuable data at a moderate cost. FSSCat is a two 6U CubeSats mission, winner of the ESA S3 challenge and overall winner of the 2017 Copernicus Masters Competition, that was launched in September 2020. The first satellite, 3Cat-5/A, carries the FMPL-2 instrument, an L-band microwave radiometer and a GNSS-Reflectometer. This work presents a neural network approach for retrieving sea ice concentration and sea ice extent maps on the Arctic and the Antarctic oceans using FMPL-2 data. The results from the first months of operations are presented and analyzed, and the quality of the retrieved maps is assessed by comparing them with other existing sea ice concentration maps. As compared to OSI SAF products, the overall accuracy for the sea ice extent maps is greater than 97% using MWR data, and up to 99% when using combined GNSS-R and MWR data. In the case of Sea ice concentration, the absolute errors are lower than 5%, with MWR and lower than 3% combining it with the GNSS-R. The total extent area computed using this methodology is close, with 2.5% difference, to those computed by other well consolidated algorithms, such as OSI SAF or NSIDC. The approach presented for estimating sea ice extent and concentration maps is a cost-effective alternative, and using a constellation of CubeSats, it can be further improved.

scholarly journals WDSchain: A Toolbox for Enhancing the Security Using Blockchain Technology in Water Distribution System

Water ◽  
2021 ◽  
Vol 13 (14) ◽  
pp. 1944
Author(s):  
Haitham H. Mahmoud ◽  
Wenyan Wu ◽  
Yonghao Wang
Keyword(s):  
Time Series ◽  
Distribution Systems ◽  
Water Distribution ◽  
Time Series Data ◽  
Series Data ◽  
Process Time ◽  
Time Interval ◽  
Security Level ◽  
Data Validation ◽  
Simulated Time

This work develops a toolbox called WDSchain on MATLAB that can simulate blockchain on water distribution systems (WDS). WDSchain can import data from Excel and EPANET water modelling software. It extends the EPANET to enable simulation blockchain of the hydraulic data at any intended nodes. Using WDSchain will strengthen network automation and the security in WDS. WDSchain can process time-series data with two simulation modes: (1) static blockchain, which takes a snapshot of one-time interval data of all nodes in WDS as input and output into chained blocks at a time, and (2) dynamic blockchain, which takes all simulated time-series data of all the nodes as input and establishes chained blocks at the simulated time. Five consensus mechanisms are developed in WDSchain to provide data at different security levels using PoW, PoT, PoV, PoA, and PoAuth. Five different sizes of WDS are simulated in WDSchain for performance evaluation. The results show that a trade-off is needed between the system complexity and security level for data validation. The WDSchain provides a methodology to further explore the data validation using Blockchain to WDS. The limitations of WDSchain do not consider selection of blockchain nodes and broadcasting delay compared to commercial blockchain platforms.

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

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

<p>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.</p><p>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.</p><p>Here, we assess the potential of the combined use of C-band Sentinel-1 (freely available) and L-band ALOS PALSAR-2 data  (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.</p>

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