Validation of backscatter models for level and deformed sea-ice in ERS-l SAR images

Carlström, Anders; Ulander, Lars M. H.

doi:10.1080/01431169508954629

Cited by 52 publications

(34 citation statements)

References 17 publications

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“…There are few Baltic Sea ice studies of the σ • behavior in connection with the sea-ice geophysical parameters [11]- [14]. Only in [14] σ • time-series analysis has been conducted.…”

Section: Introductionmentioning

confidence: 99%

“…Using 1) penetration depth in snow cover estimated from the dielectric constant of snow [15], 2) results of two Baltic Sea ice studies, where measured σ • data have been compared with a theoretical backscattering model [11], [12], and 3) a firstorder radiative-transfer backscattering model [16] (surface scattering with the integral-equation method and volume scattering with Mie phase matrix) for snow-covered sea ice, we determine three coarse snow wetness classes for our C-band HH-polarization σ • data analysis: 1) dry snow (snow volumetric wetness 0%); 2) moist snow (wetness < 2%); and 3) wet snow (wetness > 2%). Under moist-snow condition, the penetration depth in snow decreases rapidly as a function of snow wetness for wetness values below 1%.…”

Section: Introductionmentioning

confidence: 99%

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Comparisons Between SAR Backscattering Coefficient and Results of a Thermodynamic Snow/Ice Model for the Baltic Sea Land-Fast Sea Ice

Mäkynen¹,

Cheng²,

Similä³

et al. 2007

IEEE Trans. Geosci. Remote Sensing

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We have compared the time series of C-band HHpolarization backscattering coefficients (σ • ) of the Baltic Sea land-fast level ice with results from a 1-D high-resolution thermodynamic snow/ice model (HIGHTSI). The σ • time series were obtained from ENVISAT synthetic aperture radar (SAR) images. The study period was from the middle of the winter to the early melt season, February 3-April 7, 2004. Due to the large incidence angle range of the SAR images, the σ • values were divided into three subseries. In general, the HIGHTSI results greatly helped to interpret the σ • behavior with changing ice and weather conditions. The modeled snow-surface temperature, cases of snow melting, and evolution of snow and ice thickness were related to the changes in σ • . Equally useful information could not be obtained solely on the basis of large-scale atmospheric models. Realistic forcing data for HIGHTSI were available in the form of coastal-weather observations and model results of the European Centre of Medium-Range Weather Forecasts (ECMWF). The latter make it possible to apply HIGHTSI in the interpretation of SAR data from all ice-covered seas. There were some cases where detailed ground truth, combined with theoretical σ • modeling, would have been needed for interpretation of the σ • trends. A very interesting observation was the large variation of level ice σ • with changing weather conditions, which complicates automatic classification of the SAR images, and thus, the algorithms must be tuned for different ice conditions. The HIGHTSI model could act as an indicator of various ice conditions for algorithm development.Index Terms-Radar scattering, sea ice, synthetic aperture radar (SAR).

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“…There are few Baltic Sea ice studies of the σ • behavior in connection with the sea-ice geophysical parameters [11]- [14]. Only in [14] σ • time-series analysis has been conducted.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Comparisons Between SAR Backscattering Coefficient and Results of a Thermodynamic Snow/Ice Model for the Baltic Sea Land-Fast Sea Ice

Mäkynen¹,

Cheng²,

Similä³

et al. 2007

IEEE Trans. Geosci. Remote Sensing

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“…On ice surfaces a thin film of water, such as observed at spring during sunny afternoons, turn ridge blocks into specular reflectors. This allows for an increased contrast between ridges and the snow covered background due to the variability in surface orientations and occurrence of multiple reflection on several faces in ridges (Hudier, 2006;Carlstrom & Ulander, 1995). On the other hand, it can also cause an enhanced forward scattering on snow surfaces, generating dark stripes or low backscattering areas where surface slopes cause the SAR incident beam to reach the snow interface at a large angle.…”

Section: Introductionmentioning

confidence: 99%

Impact of Daily Melt and Freeze Patterns on Sea Ice Large Scale Roughness Features Extraction

Hudier¹,

Gosselin²

2010

Geoscience and Remote Sensing New Achievements

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“…We selected these 3 months for the development and the test work because then the SAR images were mostly acquired under dry snow conditions. Hence, the dominant backscattering source was the sea ice surface and we could expect a statistical relationship between σ o and DIR as reported in Carlström and Ulander (1995); Dierking et al (1999); Similä et al (2001Similä et al ( , 2010; Mäkynen and Hallikainen (2004). The preprocessing of the RS-2 SCWA images consisted of calibration (calculation of σ o HH and σ o HV ), georectification, calculation of the incidence angle θ 0 and land masking.…”

Section: Radarsat-2 Sar Imagerymentioning

confidence: 99%

Estimation of degree of sea ice ridging based on dual-polarized C-band SAR data

et al. 2018

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Abstract. For ship navigation in the Baltic Sea ice, parameters such as ice edge, ice concentration, ice thickness and degree of ridging are usually reported daily in manually prepared ice charts. These charts provide icebreakers with essential information for route optimization and fuel calculations. However, manual ice charting requires long analysis times, and detailed analysis of large areas (e.g. Arctic Ocean) is not feasible. Here, we propose a method for automatic estimation of the degree of ice ridging in the Baltic Sea region, based on RADARSAT-2 C-band dual-polarized (HH/HV channels) SAR texture features and sea ice concentration information extracted from Finnish ice charts. The SAR images were first segmented and then several texture features were extracted for each segment. Using the random forest method, we classified them into four classes of ridging intensity and compared them to the reference data extracted from the digitized ice charts. The overall agreement between the ice-chartbased degree of ice ridging and the automated results varied monthly, being 83, 63 and 81 % in January, February and March 2013, respectively. The correspondence between the degree of ice ridging reported in the ice charts and the actual ridge density was validated with data collected during a field campaign in March 2011. In principle the method can be applied to the seasonal sea ice regime in the Arctic Ocean.

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Validation of backscatter models for level and deformed sea-ice in ERS-l SAR images

Cited by 52 publications

References 17 publications

Comparisons Between SAR Backscattering Coefficient and Results of a Thermodynamic Snow/Ice Model for the Baltic Sea Land-Fast Sea Ice

Comparisons Between SAR Backscattering Coefficient and Results of a Thermodynamic Snow/Ice Model for the Baltic Sea Land-Fast Sea Ice

Impact of Daily Melt and Freeze Patterns on Sea Ice Large Scale Roughness Features Extraction

Estimation of degree of sea ice ridging based on dual-polarized C-band SAR data

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