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

Chen, Zhaohua; Montpetit, Benoît; Banks, Sarah N.; White, Lori; Behnamian, Amir; Duffe, Jason; Pasher, Jon

doi:10.3390/rs13224570

Cited by 6 publications

(3 citation statements)

References 46 publications

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“…They extended the dimensionality of InSAR technology to landfast ice deformation monitoring. For long-term monitoring, Chen et al utilized the multi-dimensional SBAS method to process time-series observations for retrieving 2D deformations that comprised the vertical and horizontal directions in Cambridge Bay [58]. At present, only Chen et al has applied multi-temporal InSAR to the monitoring of landfast ice deformation in Cambridge Bay.…”

Section: Validationmentioning

confidence: 99%

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Interannual Variation of Landfast Ice Using Ascending and Descending Sentinel-1 Images from 2019 to 2021: A Case Study of Cambridge Bay

et al. 2023

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Landfast ice has undergone a dramatic decline in recent decades, imposing potential effects on ice travel for coastal populations, habitats for marine biota, and ice use for industries. The mapping of landfast ice deformation and the investigation of corresponding causes of changes are urgent tasks that can provide substantial data to support the maintenance of the stability of the Arctic ecosystem and the development of human activities on ice. This work aims to investigate the time-series deformation characteristics of landfast ice at multi-year scales and the corresponding influence factors. For the landfast ice deformation monitoring technique, we first combined the small baseline subset approach with ascending and descending Sentinel-1 images to obtain the line-of-sight deformations for two flight directions, and then we derived the 2D deformation fields comprising the vertical and horizontal directions for the corresponding periods by introducing a transform model. The vertical deformation results were mostly within the interval [−65, 23] cm, while the horizontal displacement was largely within the range of [−26, 78] cm. Moreover, the magnitude of deformation observed in 2019 was evidently greater than those in 2020 and 2021. In accordance with the available data, we speculate that the westerly wind and eastward-flowing ocean currents are the dominant reasons for the variation in the horizontal direction in Cambridge Bay, while the factors causing spatial differences in the vertical direction are the sea-level tilt and ice growth. For the interannual variation, the leading cause is the difference in sea-level tilt. These results can assist in predicting the future deformation of landfast ice and provide a reference for on-ice activities.

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Section: Validationmentioning

confidence: 99%

“…The factors that affect horizontal deformations include ocean currents, winds, and thermal expansion [25]. Meanwhile, the factors that affect vertical deformation include temperature, ice growth, and sea-level tilt [58], which are further described below.…”

Section: Suggested Reasons For Deformationmentioning

confidence: 99%

Interannual Variation of Landfast Ice Using Ascending and Descending Sentinel-1 Images from 2019 to 2021: A Case Study of Cambridge Bay

et al. 2023

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“…Interferogram images contain information associated with differences in the phase of the SAR signal returned to the satellite. Interferometric coherence observations produced from InSAR analysis have been of great interest recently, with studies demonstrating the efficacy of time-series InSAR products over a variety of environments, such as urban [26][27][28], wetland [29][30][31][32][33], permafrost [34][35][36], forested [37][38][39], and ice-covered sea [40][41][42] areas.…”

Section: Introductionmentioning

confidence: 99%

Applying Machine Learning and Time-Series Analysis on Sentinel-1A SAR/InSAR for Characterizing Arctic Tundra Hydro-Ecological Conditions

Merchant

Obadia

Brisco³

et al. 2022

Remote Sensing

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Synthetic aperture radar (SAR) is a widely used tool for Earth observation activities. It is particularly effective during times of persistent cloud cover, low light conditions, or where in situ measurements are challenging. The intensity measured by a polarimetric SAR has proven effective for characterizing Arctic tundra landscapes due to the unique backscattering signatures associated with different cover types. However, recently, there has been increased interest in exploiting novel interferometric SAR (InSAR) techniques that rely on both the amplitude and absolute phase of a pair of acquisitions to produce coherence measurements, although the simultaneous use of both intensity and interferometric coherence in Arctic tundra image classification has not been widely tested. In this study, a time series of dual-polarimetric (VV, VH) Sentinel-1 SAR/InSAR data collected over one growing season, in addition to a digital elevation model (DEM), was used to characterize an Arctic tundra study site spanning a hydrologically dynamic coastal delta, open tundra, and high topographic relief from mountainous terrain. SAR intensity and coherence patterns based on repeat-pass interferometry were analyzed in terms of ecological structure (i.e., graminoid, or woody) and hydrology (i.e., wet, or dry) using machine learning methods. Six hydro-ecological cover types were delineated using time-series statistical descriptors (i.e., mean, standard deviation, etc.) as model inputs. Model evaluations indicated SAR intensity to have better predictive power than coherence, especially for wet landcover classes due to temporal decorrelation. However, accuracies improved when both intensity and coherence were used, highlighting the complementarity of these two measures. Combining time-series SAR/InSAR data with terrain derivatives resulted in the highest per-class F1 score values, ranging from 0.682 to 0.955. The developed methodology is independent of atmospheric conditions (i.e., cloud cover or sunlight) as it does not rely on optical information, and thus can be regularly updated over forthcoming seasons or annually to support ecosystem monitoring.

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A bibliometric analysis on the visibility of the Sentinel-1 mission in the scientific literature

Pham-Duc

Nguyen

2022

Arab J Geosci

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Seven years after the launch of the first Sentinel-1 satellite, its data have been widely used in the scientific community. This study provides the first quantitative analysis of the visibility of the Sentinel-1 mission to the scientific literature through a bibliometric analysis of 1628 articles published in scientific journals during the 2014–2020 period. The main findings show that the number of Sentinel-1 mission-related papers increased significantly over the years, with an annual growth rate of 83%. Remote sensing is the most popular journal where 31.75% of the publication collection has been published. China and the USA are the two most productive countries with a share of 22.30% and 16.22% in the collection. Research based on the Sentinel-1 data covered a wide range of topics in geoscience disciplines. The use of SAR interferometry, focusing on the studies of landslide, earthquake, ground deformation, and subsidence, is the most important research direction using Sentinel-1 data. Image fusion of Sentinel-1 and Sentinel-2 observations for mapping and monitoring applications is the second most important research direction. Other popular research areas are glaciology, soil moisture, agriculture, rice monitoring, and ship detection. This study uses bibliographic data derived only from the Scopus database; therefore, it might not cover all Sentinel-1 related documents. However, this paper is a good reference for researchers who want to use Sentinel-1 data in their studies. The two Sentinel-1 satellites will provide scientific data for years to come, meaning that this type of analysis should be done on a regular basis.

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

Cited by 6 publications

References 46 publications

Interannual Variation of Landfast Ice Using Ascending and Descending Sentinel-1 Images from 2019 to 2021: A Case Study of Cambridge Bay

Interannual Variation of Landfast Ice Using Ascending and Descending Sentinel-1 Images from 2019 to 2021: A Case Study of Cambridge Bay

Applying Machine Learning and Time-Series Analysis on Sentinel-1A SAR/InSAR for Characterizing Arctic Tundra Hydro-Ecological Conditions

A bibliometric analysis on the visibility of the Sentinel-1 mission in the scientific literature

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