Markku Similä

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

Incidence angle dependence of three statistical parameters-the mean of the backscattering coefficient (/spl sigma//spl deg/), standard deviation, and autocorrelation coefficient of texture (/spl sigma//sub T/ and /spl rho//sub T/)-of the C-band horizontal-horizontal (HH) polarization backscattering signatures of the Baltic Sea ice are investigated using RADARSAT ScanSAR Narrow images and helicopter-borne Helsinki University of Technology Scatterometer (HUTSCAT) data. The analysis of the large amount of data shows that the relationship between the mean /spl sigma//spl deg/ in decibel scale and the incidence angle in the range from 19/spl deg/ to 46/spl deg/ is usually well described by a linear model. In general, the RADARSAT and HUTSCAT results agree with each other, and they are also supported by theoretical backscattering model calculations; the more deformed the ice, the smaller the slope between /spl sigma//spl deg/ and the incidence angle, and the higher the moisture content of snow or ice, the larger the slope. The derived /spl sigma//spl deg/ incidence angle dependencies can be used to roughly compensate the /spl sigma//spl deg/ incidence angle variation in the SAR images to help their visual and automated classification. The variability of /spl sigma//sub T/ and /spl rho//sub T/ with the increasing incidence angle is insignificant compared to the variability within each ice type. Their average changes with the incidence angle are so small that, in practice, their trends do not need to be compensated. The results of this study can be utilized when developing classification algorithms for the RADARSAT ScanSAR and ENVISAT HH-polarization Wide Swath images of the Baltic Sea ice.

Open water detection from Baltic Sea ice Radarsat-1 SAR imagery

An algorithm for open water and sea ice discrimination for Radarsat-1 ScanSAR images is presented. The algorithm is based on segmentation and local synthetic aperture radar signal intensity autocorrelation. The algorithm performance is evaluated by comparing the results to operational digitized ice charts, in which the sea ice information is based on human interpretation of multiple data sources, including remote sensing data. The algorithm locates the open water of the digitized ice charts with about 90% accuracy.

On the accuracy of thin-ice thickness retrieval using MODIS thermal imagery over Arctic first-year ice

Abstract We have studied the accuracy of ice thickness (hi) retrieval based on night-time MODIS (Moderate Resolution Imaging Spectroradiometer) ice surface temperature (Ts) images and HIRLAM (High Resolution Limited Area Model) weather forcing data from the Arctic. The study area is the Kara Sea and eastern part of the Barents Sea, and the study period spans November-April 2008–11 with 199 hi charts. For cloud masking of the MODIS data we had to use manual methods in order to improve detection of thin clouds and ice fog. The accuracy analysis of the retrieved hi was conducted with different methods, taking into account the inaccuracy of the HIRLAM weather forcing data. Maximum reliable hi under different air-temperature and wind-speed ranges was 35–50 cm under typical weather conditions (air temperature <–20cC, wind speed <5ms–1) present in the MODIS data. The accuracy is best for the 15–30 cm thickness range, ∼38%. The largest hi uncertainty comes from air temperature data. Our ice-thickness limits are more conservative than those in previous studies where numerical weather prediction model data were not used in the hi retrieval. Our study gives new detailed insight into the capability of Ts-based hi retrieval in the Arctic marginal seas during freeze-up and wintertime, and should also benefit work where MODIS hi charts are used.

Ice thickness estimation using SAR data and ice thickness history

We introduce an algorithm for sea ice thickness estimation by augmenting the sea ice thickness history derived from the daily digitized ice charts for the Baltic Sea ice. This algorithm is designed for operational use and utilizes the C-band Radarsat-1 data.

An iterative incidence angle normalization algorithm for sea ice SAR images

For automated interpretation of the sea ice SAR data, the change in backscatter level due to the incidence angle variation should be taken into account. We have studied the statistics of the Radarsat ScanSAR Narrow data for sea ice, and developed an iterative algorithm for normalizing the data for automated interpretation of it.

Modelling snow and ice thickness in the coastal Kara Sea, Russian Arctic

Abstract Snow and ice thickness in the coastal Kara Sea, Russian Arctic, were investigated by applying the thermodynamic sea-ice model HIGHTSI. The external forcing was based on two numerical weather prediction (NWP) models: the High Resolution Limited Area Model (HIRLAM) and the European Centre for Medium-Range Weather Forecasts (ECMWF) model. A number of model experiments were carried out applying different snow parameterization schemes. The modelled ice thickness was compared with in situ measurements and the modelled snow thickness was compared with the NASA Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) snow thickness. The HIRLAM and ECMWF model results agreed with each other on air temperature and wind. The NWP model precipitation forecasts caught up the synoptic-scale snowfall events, but the magnitude was liable to errors. The ice growth was modelled reasonably well applying HIGHTSI either with a simple parameterization for snow thickness or with the HIRLAM or ECMWF model precipitation as input. For the latter, however, an adjustment of snow accumulation in early winter was necessary to avoid excessive accumulation and consequent underestimation of ice thickness. Applying effective snow heat conductivity improved the modelled ice thickness. The HIGHTSI-modelled snow thickness had a seasonal evolution similar to that of the AMSR-E snow thickness. New field data are urgently needed to validate NWP and ice models and remote-sensing products for snow and sea ice in the Kara Sea.

Thin Ice Detection in the Barents and Kara Seas With AMSR-E and SSMIS Radiometer Data

We have studied thin ice detection using Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) and Special Sensor Microwave Imager/Sounder radiometer data acquired over the Barents and Kara Seas during three winters (November-April) in 2008-2011. Moderate Resolution Imaging Spectroradiometer-based ice thickness charts were used as reference data. Thin ice detection was studied using polarization and spectral gradient ratios (PR and GR) calculated from the 36/37 and 89/91 GHz radiometer data. Thresholds for thin ice detection and maximum thicknesses for the detected thin ice (hT) were determined, as were error rates for misdetections. The results for different 1-D PR and GR parameters led to the conclusion that the AMSR-E PR36 and H-polarized GR8936 would be the best parameters for a 2-D classifier. We adopted the linear discrimination analysis (LDA) as a statistical tool. Thin ice areas with hT of 30 cm could be separated from thicker ice fields with approximately 20% error level. In our large data set, the estimation of thin ice thickness was not possible with reasonable accuracy due to the large scatter between ice thickness and the PR and GR signatures. This is likely due to a large data set, besides thin ice in polynyas also thin ice in the marginal ice zone and thin ice from freeze-up period. The optimal LDA parameters in the classifier and hT depended on the daily mean air temperature ((T am )). We could not yet parameterize the classifier optimally according to (T am ), but the constructed classifier worked rather robustly as indicated by the relative small error rate variation between the three analyzed winters.

SAR-based estimation of the baltic sea ice motion

The ice season in the northern part of the Baltic Sea varies from a few weeks up to 4-5 months depending on the location and the winter. As a long-term average the Baltic Sea is ice covered for about 45% of its surface area at the annual maximum. Due to the relatively narrow basins, the deformation rate remains rather high throughout the winter. The ice thickness measurements show that in the Bay of Bothnia over one third of the ice cover is thicker than 1 m in the later stages of the ice season during a typical winter. Modeling the state and development of the Baltic ice cover has been an active research area which have resulted in operative ice models. The current operative ice model run at the Finnish Ice Service (FIS) models the following parameters: ice motion and concentration, mean ice thickness, ridged ice thickness, ridged ice concentration, compressive region, and deformed ice fraction. Some case studies have been made to validate the results. However, a more systematic forecast evaluation is needed, the final goal being the data assimilation to integrate the EO data into the numerical ice model. In this paper we present an algorithm which estimates the ice motion field from two successive RADARSAT-1 SAR images. A large number of RADARSAT-1 ScanSAR Wide mode SAR images, usually over 100, is received at FIS every winter. The same sea areas are visible in the images with a time interval of 1-3 days.

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

We have compared the time series of C-band HH-polarization backscattering coefficients (sigmadeg) of the Baltic Sea land-fast level ice with results from a 1-D high-resolution thermodynamic snow/ice model (HIGHTSI). The sigmadeg 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 sigmadeg values were divided into three subseries. In general, the HIGHTSI results greatly helped to interpret the sigmadeg 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 sigmadeg. 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 sigmadeg modeling, would have been needed for interpretation of the sigmadeg trends. A very interesting observation was the large variation of level ice sigmadeg 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

Comparison of SAR data and operational sea ice products to EM ice thickness measurements in the Baltic Sea

In February 2003, sea ice thickness measurements using an electromagnetic induction (EM) instrument were made in the Gulf of Bothnia and Gulf of Finland. We have made comparisons between the EM measurements and Radarsat-1 ScanSAR Wide mode SAR data, and also between our operational sea ice products (digitized ice thickness charts, and ice thickness charts refined by the latest Radarsat-1 image). The SAR images are in 100 m resolution, and the other products are in 500 m resolution