Incidence angle dependence of the statistical properties of C-band HH-polarization backscattering signatures of the Baltic Sea ice

Mäkynen, M.; Manninen, Terhikki; Similä, M.; Karvonen, Juha; Hallikainen, M.

doi:10.1109/tgrs.2002.806991

Cited by 107 publications

(91 citation statements)

References 16 publications

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“…Since the surface type is not known a priori we have to choose which angular correction to apply and the preference is given to the more reliable sea ice angular correction. However, the total compensation is impossible as the backscatter dependence on the incidence angle varies for dif- ferent ice types (Mäkynen et al, 2002) and water areas in the scene. The radiometric corrections during calibration process are just a first-order approximation; nevertheless, the advantages of performing the angular correction are greater than the disadvantages (Moen et al, 2015).…”

Section: Significance Of Incidence Angle Variations and Thermal Noisementioning

confidence: 99%

Operational algorithm for ice–water classification on dual-polarized RADARSAT-2 images

Zakhvatkina

Korosov²,

Muckenhuber³

et al. 2017

The Cryosphere

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Abstract. Synthetic Aperture Radar (SAR) data from RADARSAT-2 (RS2) in dual-polarization mode provide additional information for discriminating sea ice and open water compared to single-polarization data. We have developed an automatic algorithm based on dual-polarized RS2 SAR images to distinguish open water (rough and calm) and sea ice. Several technical issues inherent in RS2 data were solved in the pre-processing stage, including thermal noise reduction in HV polarization and correction of angular backscatter dependency in HH polarization. Texture features were explored and used in addition to supervised image classification based on the support vector machines (SVM) approach. The study was conducted in the ice-covered area between Greenland and Franz Josef Land. The algorithm has been trained using 24 RS2 scenes acquired in winter months in 2011 and 2012, and the results were validated against manually derived ice charts of the Norwegian Meteorological Institute. The algorithm was applied on a total of 2705 RS2 scenes obtained from 2013 to 2015, and the validation results showed that the average classification accuracy was 91 ± 4 %.

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Section: Significance Of Incidence Angle Variations and Thermal Noisementioning

confidence: 99%

Operational algorithm for ice–water classification on dual-polarized RADARSAT-2 images

Zakhvatkina

Korosov²,

Muckenhuber³

et al. 2017

The Cryosphere

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“…Advanced approaches are based on histogram or frequency matching [2], while many empirical approaches rely on fitting a first or second order polynomial to incidence angle and backscatter values [3][4][5][6][7][8]. Previous studies [7,9] have pointed out that the relationship between incidence angle and backscatter can be considered as linear for a limited incidence angle range. Mäkynen et al [9] found that the incidence angle range from 19 • to 46 • , which approximately corresponds to the incidence angle range covered by Sentinel-1, is usually well described by a linear model.…”

Section: Introductionmentioning

confidence: 99%

Simplified Normalization of C-Band Synthetic Aperture Radar Data for Terrestrial Applications in High Latitude Environments

2018

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Synthetic aperture radar (SAR) applications often require normalization to a common incidence angle. Angular signatures of radar backscatter depend on surface roughness and vegetation cover, and thus differ, from location to location. Comprehensive reference datasets are therefore required in heterogeneous landscapes. Multiple acquisitions from overlapping orbits with sufficient incidence angle range are processed in order to obtain parameters of the location specific normalization function. We propose a simpler method for C-band data, using single scenes only. It requires stable dielectric properties (no variations of liquid water content). This method is therefore applicable for frozen conditions. Winter C-band data have been shown of high value for a number of applications in high latitudes before. In this paper we explore the relationship of incidence angle and Sentinel-1 backscatter across the tundra to boreal transition zone. A linear relationship (coefficient of determination R 2 = 0.64) can be found between backscatter and incidence angle dependence (slope of normalization function) as determined by multiple acquisitions on a pixel by pixel basis for typical land cover classes in these regions. This allows a simplified normalization and thus reduced processing effort for applications over larger areas.

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“…For some other purposes, such as ice type classification, we also apply an incidence angle correction (Makynen et al, 2002), which is based on an empirical linear relationship between mean sea ice backscattering and incidence angle value. This correction is, however, not necessary for the ice drift estimation.…”

Section: Sar Preprocessingmentioning

confidence: 99%

Operational SAR-based sea ice drift monitoring over the Baltic Sea

Karvonen

2012

Ocean Sci.

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Abstract. An algorithm for computing ice drift from pairs of synthetic aperture radar (SAR) images covering a common area has been developed at FMI. The algorithm has been developed based on the C-band SAR data over the Baltic Sea. It is based on phase correlation in two scales (coarse and fine) with some additional constraints. The algorithm has been running operationally in the Baltic Sea from the beginning of 2011, using Radarsat-1 ScanSAR wide mode and Envisat ASAR wide swath mode data. The resulting ice drift fields are publicly available as part of the MyOcean EC project. The SAR-based ice drift vectors have been compared to the drift vectors from drifter buoys in the Baltic Sea during the first operational season, and also these validation results are shown in this paper. Also some navigationally useful sea ice quantities, which can be derived from ice drift vector fields, are presented.

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Incidence angle dependence of the statistical properties of C-band HH-polarization backscattering signatures of the Baltic Sea ice

Cited by 107 publications

References 16 publications

Operational algorithm for ice–water classification on dual-polarized RADARSAT-2 images

Operational algorithm for ice–water classification on dual-polarized RADARSAT-2 images

Simplified Normalization of C-Band Synthetic Aperture Radar Data for Terrestrial Applications in High Latitude Environments

Operational SAR-based sea ice drift monitoring over the Baltic Sea

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