Sentinel-1 mission status

Potin, Pierre; Rosich, Betlem; Grimont, Patrick; Miranda, Nuno; Shurmer, Ian; O'Connell, Alistair; Torres, Ramón; Krassenburg, Mike

doi:10.1109/igarss.2017.8128255

Cited by 12 publications

(10 citation statements)

References 2 publications

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“…Another beneficial feature for snow mapping is the regular acquisition of dual polarization (VV-VH) IW mode data, being used as standard operation scenario over many land areas [37]. Co-polarized channel data are the preferred option for interferometric applications, a main application field of the S1 mission.…”

Section: Discussionmentioning

confidence: 99%

Advancements for Snowmelt Monitoring by Means of Sentinel-1 SAR

et al. 2016

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Abstract:The Sentinel satellite constellation series, developed and operated by the European Space Agency, represents the dedicated space component of the European Copernicus program, committed to long-term operational services in environment, climate and security. We developed, tested and evaluated an algorithm for generating maps of snowmelt area from C-band synthetic aperture radar (SAR) data of the Sentinel-1 mission. For snowmelt classification, a change detection method is applied, using multitemporal dual-polarized SAR data acquired in Interferometric Wide swath (IW) mode, the basic operation mode over land surfaces. Of particular benefit for wet snow retrievals are the high instrument stability, the high spatial resolution across the 250 km wide swath, and the short revisit time. In order to study the impact of polarization, we generated maps of melting snow using data of the VV-polarized channel, the VH-polarized channel and a combined VV-and VH-based channel using a weighting function that accounts for effects of the local incidence angle. Comparisons are performed with snow maps derived from Landsat images over study areas in the Alps and in Iceland. The pixel-by-pixel comparisons show good agreement between the snow products of the two sensors, with the best performance for retrievals based on the combined (VV and VH) channel and a minor decline for the VH-based product. The VV-based snowmelt extent product shows a drop-off in quality over areas with steep terrain because of the decreasing backscatter contrast of snow-covered versus snow-free surfaces on fore-slopes. The investigations demonstrate the excellent capability of the Sentinel-1 mission for operational monitoring of snowmelt areas.

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Section: Discussionmentioning

confidence: 99%

Advancements for Snowmelt Monitoring by Means of Sentinel-1 SAR

et al. 2016

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“…The Sentinel-1 IW GRDH mode has a resolution of 20 m (10-m grid spacing). Using the two satellite units, complete global coverage can be achieved within 6-8 days [1]. Over Europe and Canada, the temporal revisit time is expected to be about 1-3 days.…”

Section: A Overview: Sentinel-1 C-band Synthetic Aperture Radar (Sarmentioning

confidence: 99%

“…Due to the open access data distribution policy and improved data quality in terms of temporal and spatial resolution, the Sentinel-1 user community is growing very fast. Daily global data acquisition of Sentinel-1 A and B is more than 1.5 TB [1]. In order to handle and process such big data for regional to global scale monitoring, reliable and stable processing chains are required.…”

mentioning

confidence: 99%

Methods to Remove the Border Noise From Sentinel-1 Synthetic Aperture Radar Data: Implications and Importance For Time-Series Analysis

Ali

Cao

Naeimi

et al. 2018

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Abstract-The Sentinel-1 GRD (ground range detected) Level-1 product generated by the Instrument Processing Facil-ity of the European Space Agency has noise artifacts at the image borders, which are quite consistent at both left and right sides of the satellite's cross track and at the start and end of the data take along track. The Sentinel-1 border noise troubles the creation of clean and consistence time series of backscatter. Data quality control and management become very challenging tasks, when it comes to the large-scale data processing, both in terms of spatial coverage and data volume. In this paper, we evaluate three techniques for removing the Sentinel-1 border noise and compare the results with the existing "Sentinel-1 GRD Border Noise Removal" algorithm implemented in the Sentinel-1 toolbox of the Sentinel application platform.1 Validation and evaluation of the newly pro-posed algorithms was done using random samples containing 1500 Sentinel-1 scenes selected from a complete Sentinel-1 archive. The newly proposed approach has successfully achieved the required level of accuracy and solved the issue of time-series anomalies due to the border noise.Index Terms-C-band synthetic aperture radar (SAR), interfer-ometric wide swath (IW), Sentinel-1, Sentinel application platform (SNAP), Sentinel-1 border noise.quality insurance of large amounts of data at the scale of several hundreds of terabytes to petabyte have been becoming a challenging task.The recently launched constellation of two C-band synthetic aperture radar satellites named Sentinel-1 (A and B) is getting immense attention from both scientists and commercial users. Due to the open access data distribution policy and improved data quality in terms of temporal and spatial resolution, the Sentinel-1 user community is growing very fast. Daily global data acquisition of Sentinel-1 A and B is more than 1.5 TB [1]. In order to handle and process such big data for regional to global scale monitoring, reliable and stable processing chains are required.In this paper, we address the issue of image border noise in the Sentinel-1 GRDH (ground-range-detected high resolution) Level-1 product for large-scale data processing. The proposed methods are self-adaptive and are applicable to other acquisition modes, however, fine-tuning might be needed.

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“…Using the coherence characteristics of SAR images, the study applied the Coherence Change Detection Technique using Sentinel-1 imagery, which has a large-scale global mapping capability and operates independent of weather and light (Potin et al, 2016) to monitor disaster induced urban changes. The CCD technique is not a new remote sensing technique and has been applied in various studies to monitor urban changes (He and He, 2009;Jendryke et al, 2016;Jendryke et al, 2017;Liao et al, 2006).…”

Section: Introductionmentioning

confidence: 99%

Sar Coherence Change Detection of Urban Areas Affected by Disasters Using Sentinel-1 Imagery

Washaya

Balz

2018

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:The study focuses on two study areas: San Juan in Puerto Rico, which was affected by Hurricane Maria in September 2017, and Sarpol Zahab in Iran, which was one of the towns affected by an earthquake in November 2017. In our study, we generate coherence images, and classify them into areas of 'change' and 'no-change'. A statistical analysis is made by converting the coherence results into point data, creating street blocks for the study areas and integrating the point data into the street blocks to calculate the standard deviation over the whole stack of images. Additionally, Landsat imagery is used to create land-use classes, convert them to polygons and integrate the polygon classes to the coherence maps to determine the average coherence loss per class for each disaster. Results show 65% loss in coherence after the earthquake in Sarpol-e-Zahab and 75% loss in Puerto Rico after the Hurricane. Land-use classes show coherence losses to below 0.5 for each disaster.

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Sentinel-1 mission status

Cited by 12 publications

References 2 publications

Advancements for Snowmelt Monitoring by Means of Sentinel-1 SAR

Advancements for Snowmelt Monitoring by Means of Sentinel-1 SAR

Methods to Remove the Border Noise From Sentinel-1 Synthetic Aperture Radar Data: Implications and Importance For Time-Series Analysis

Sar Coherence Change Detection of Urban Areas Affected by Disasters Using Sentinel-1 Imagery

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