Sentinel-1 Constellation Mission Operations Status

Potin, Pierre; Rosich, Betlem; Miranda, Nuno; Grimont, Patrick; Shurmer, Ian; Connell, Alistair O; Krassenburg, Mike; Gratadour, Jean-Baptiste

doi:10.1109/igarss.2018.8517743

Cited by 16 publications

(9 citation statements)

References 4 publications

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“…The presently available and upcoming C-band SAR satellite constellation missions (e.g., Sentinel-1 of the Copernicus Programme [35][36][37] and RADARSAT Constellation Mission [38,39]), along with the existing free open-source SAR processing toolboxes (e.g., SNAP [40] and POLSARPRO [41]), can provide academic centers, environmental agencies, nongovernmental organizations, and the petroleum industry itself with the required support to build libraries with the category of oil slicks in the vicinities of offshore oil and gas facilities. The scrutiny of these oil-slick category records is of great interest not only from the political-economic perspective (i.e., discovery of new exploration frontiers based on the identification of oil seeps observed in the surface of the ocean coming from active petroleum systems), but is also relevant from the social-ecological viewpoint (i.e., it can assist in reducing headline uproar inquires with reliable timely information with the detection of oil spills using more efficient environmental surveillance techniques).…”

Section: Introductionmentioning

confidence: 99%

Oil-Slick Category Discrimination (Seeps vs. Spills): A Linear Discriminant Analysis Using RADARSAT-2 Backscatter Coefficients (σ°, β°, and γ°) in Campeche Bay (Gulf of Mexico)

et al. 2019

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A novel empirical approach to categorize oil slicks’ sea surface expressions in synthetic aperture radar (SAR) measurements into oil seeps or oil spills is investigated, contributing both to academic remote sensing research and to practical applications for the petroleum industry. We use linear discriminant analysis (LDA) to try accuracy improvements from our previously published methods of discriminating seeps from spills that achieved ~70% of overall accuracy. Analyzing 244 RADARSAT-2 scenes containing 4562 slicks observed in Campeche Bay (Gulf of Mexico), our exploratory data analysis evaluates the impact of 61 combinations of SAR backscatter coefficients (σ°, β°, γ°), SAR calibrated products (received radar beam given in amplitude or decibel, with or without a despeckle filter), and data transformations (none, cube root, log10). The LDA ability to discriminate the oil-slick category is rather independent of backscatter coefficients and calibrated products, but influenced by data transformations. The combination of attributes plays a role in the discrimination; combining oil-slicks’ size and SAR information is more effective. We have simplified our analyses using fewer attributes to reach accuracies comparable to those of our earlier studies, and we suggest using other multivariate data analyses—cubist or random forest—to attempt to further improve oil-slick category discrimination.

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Section: Introductionmentioning

confidence: 99%

Oil-Slick Category Discrimination (Seeps vs. Spills): A Linear Discriminant Analysis Using RADARSAT-2 Backscatter Coefficients (σ°, β°, and γ°) in Campeche Bay (Gulf of Mexico)

et al. 2019

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“…An example of this is the Sentinel-1 mission that delivers new radar imagery for most parts of the Earth every 6 days. This imagery is made available within 24 hours of general image capture, with priority area imagery being available within three hours post image capture (Potin et al, 2019). Frameworks for automated routines could be based on predefined data processors using scripting languages, such as python (Truckenbrodt et al, 2019) or graphic processing tools such as the Sentinel Application Platform (SNAP) which allows the users to apply minor adjustments according to the area and purpose of the captured imagery (ESA, 2019).…”

Section: Data Continuity and Time-series Analysesmentioning

confidence: 99%

Spaceborne radar imagery – An under-utilized source of information for humanitarian relief

Braun

2020

JHE

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This practical paper gives an overview about the widely unused potential of radar satellite imagery to assist humanitarian action. It briefly introduces the basic differences between optical and radar images and demonstrates the practical use of the latter in different settings based on their information content and their potential for multi-temporal analyses. It gives recommendations on further reading and closes with suggestions on the practical integration of radar data into humanitarian work.

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“…Besides the large areas affected by fires, tile 29TNE was also selected since (i) Sentinel-1 A/B acquisitions from ascending and descending passes were available at the nominal temporal frequency of six days [38]; (ii) ancillary data on environmental variables at a detailed spatial resolution were available from the Copernicus Land Monitoring Service program; and (iii) cloud cover was low (annual mean cloud cover of 27.2% as computed from MODIS images) when compared to the mean cloud cover over all Europe [39], which allowed using optical sensors indices as a proxy for a range of environmental variables that informed the analysis. The SAR dataset was formed by temporal series of dual-polarized (VV and VH polarizations) C-band backscatter coefficient acquired by the Sentinel-1 A/B sensors.…”

Section: Study Area and Datasetsmentioning

confidence: 99%

Temporal Decorrelation of C-Band Backscatter Coefficient in Mediterranean Burned Areas

2019

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Burned area algorithms from radar images are often based on temporal differences between pre- and post-fire backscatter values. However, such differences may occur long past the fire event, an effect known as temporal decorrelation. Improvements in radar-based burned areas monitoring depend on a better understanding of the temporal decorrelation effects as well as its sources. This paper analyses the temporal decorrelation of the Sentinel-1 C-band backscatter coefficient over burned areas in Mediterranean ecosystems. Several environmental variables influenced the radar scattering such as fire severity, post-fire vegetation recovery, water content, soil moisture, and local slope and aspect were analyzed. The ensemble learning method random forests was employed to estimate the importance of these variables to the decorrelation process by land cover classes. Temporal decorrelation was observed for over 32% of the burned pixels located within the study area. Fire severity, vegetation water content, and soil moisture were the main drivers behind temporal decorrelation processes and are of the utmost importance for areas detected as burned immediately after fire events. When burned areas were detected long after fire (decorrelated areas), due to reduced backscatter coefficient variations between pre- to post-fire acquisitions, water content (soil and vegetation) was the main driver behind the backscatter coefficient changes. Therefore, for efficient synthetic aperture radar (SAR)-based monitoring of burned areas, detection, and mapping algorithms need to account for the interaction between fire impact and soil and vegetation water content.

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Sentinel-1 Constellation Mission Operations Status

Cited by 16 publications

References 4 publications

Oil-Slick Category Discrimination (Seeps vs. Spills): A Linear Discriminant Analysis Using RADARSAT-2 Backscatter Coefficients (σ°, β°, and γ°) in Campeche Bay (Gulf of Mexico)

Oil-Slick Category Discrimination (Seeps vs. Spills): A Linear Discriminant Analysis Using RADARSAT-2 Backscatter Coefficients (σ°, β°, and γ°) in Campeche Bay (Gulf of Mexico)

Spaceborne radar imagery – An under-utilized source of information for humanitarian relief

Temporal Decorrelation of C-Band Backscatter Coefficient in Mediterranean Burned Areas

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