An Automatic Method for Black Margin Elimination of Sentinel-1A Images over Antarctica

Wang, Xianwei; Holland, David M.

doi:10.3390/rs12071175

Cited by 2 publications

(2 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The water bodies identified from the data of two orbits mostly intersect. Sentinel-1 is a right-looking SAR, so the differences in the water masks correspond to the satellite look direction [78]. In Figure 18, the presence of a water pixels strip is noticeable near the western shore of Lake Lohvanjärvi for the descending orbit 153, which corresponds to the western look direction of a satellite moving from north to south.…”

Section: Ascending and Descending Orbitsmentioning

confidence: 99%

Surface Water Mapping from SAR Images Using Optimal Threshold Selection Method and Reference Water Mask

Kavats¹,

Khramov²,

Sergieieva³

2022

Water

View full text Add to dashboard Cite

Water resources are an important component of ecosystem services. During long periods of cloudiness and precipitation, when a ground-based sample is not available, the water bodies are detected from satellite SAR (synthetic-aperture radar) data using threshold methods (e.g., Otsu and Kittler–Illingworth). However, such methods do not enable to obtain the correct threshold value for the backscattering coefficient (σ0) of relatively small water areas in the image. The paper proposes and substantiates a method for the mapping of the surface of water bodies, which makes it possible to correctly identify water bodies, even in “water”/“land” class imbalance situations. The method operates on a principle of maximum compliance of the resulting SAR water mask with a given reference water mask. Therefore, the method enables the exploration of the possibilities of searching and choosing the optimal parameters (polarization and speckle filtering), which provide the maximum quality of SAR water mask. The method was applied for mapping natural and industrial water bodies in the Pohjois-Pohjanmaa region (North Ostrobothnia), Finland, using Sentinel-1A and -1B ground range detected (GRD) data (ascending and descending orbits) in 2018–2021. Reference water masks were generated based on optical spectral indices derived from Sentinel-2A and -2B data. The polarization and speckle filtering parameters were chosen since they provide the most accurate σ0 threshold (on average for all observations above 0.9 according to the Intersection over Union criterion) and are resistant to random fluctuations. If a reference water mask is available, the proposed method is more accurate than the Otsu method. Without a reference mask, the σ0 threshold is calculated as an average of thresholds obtained from previous observations. In this case, the proposed method is as good in accuracy as the Otsu method. It is shown that the proposed method enables the identification of surface water bodies under significant class imbalance conditions, such as when the water surface covers only a fraction of a percent of the area under study.

show abstract

Section: Ascending and Descending Orbitsmentioning

confidence: 99%

Surface Water Mapping from SAR Images Using Optimal Threshold Selection Method and Reference Water Mask

Kavats¹,

Khramov²,

Sergieieva³

2022

Water

View full text Add to dashboard Cite

show abstract

“…Monitoring of surface velocity in recent decades has been improved using SAR, which enables accurate large-scale detection of surface motion. In particular, SAR images from the Sentinel-1 satellite are freely available from 2014 and 2016, respectively (Nagler, Rott et al 2015, Wang andHolland 2020). This satellite has provided monthly measurements of surface velocity.…”

Section: Introductionmentioning

confidence: 99%

The Spatiotemporal Surface Velocity Variations and Analysis of Amery Ice Shelf, East Antarctica

Yuan,

Jun,

Min

et al. 2024

Preprint

View full text Add to dashboard Cite

The surface velocity of the Amery Ice Shelf (AIS) is vital to assessing its stability and mass balance. Previous studies have shown that the AIS basin has a stable multi-year average surface velocity. However, spatiotemporal variations in the surface velocity of the AIS and the underlying physical mechanism remain poorly understood. This study combined offset tracking and DInSAR methods to extract the monthly average surface velocity of the AIS and obtained the inter-annual surface velocity from the ITS_LIVE product. An uneven spatial distribution in inter-annual variation of the surface velocity was observed between 2000–2022, although the magnitude of variation was small at less than 20.5 m/yr. The increase and decrease in surface velocity on the eastern and western-central sides of the AIS, respectively, could be attributed to the change in the thickness of the AIS. There was clear seasonal variation in monthly average surface velocity at the eastern side of the AIS between 2017–2021, which could be attributed to variations in the area and thickness of fast-ice and also to variations in ocean temperature. This study suggested that changes in fast-ice and ocean temperature are the main factors driving spatiotemporal variation in the surface velocity of the AIS.

show abstract

An Automatic Method for Black Margin Elimination of Sentinel-1A Images over Antarctica

Cited by 2 publications

References 36 publications

Surface Water Mapping from SAR Images Using Optimal Threshold Selection Method and Reference Water Mask

Surface Water Mapping from SAR Images Using Optimal Threshold Selection Method and Reference Water Mask

The Spatiotemporal Surface Velocity Variations and Analysis of Amery Ice Shelf, East Antarctica

Contact Info

Product

Resources

About