Juha Karvonen

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.

Baltic Sea Ice Concentration Estimation Based on C-Band HH-Polarized SAR Data

Ice concentration measurements are important information e.g., for navigation, ice modeling, and climate change research. Here we present an algorithm for estimating ice concentration from C-band SAR data. The resolution of SAR data is significantly higher than the resolution of the current operational ice concentration products based on radiometer data. Our algorithm is based on segment-wise autocorrelation distributions. The algorithm results were compared with two reference data sets: ice concentrations from the Finnish Ice Service (FIS) ice charts, and ice concentrations from the radiometer-based operational ice concentration algorithm of University of Bremen. The new algorithm gives reasonable ice concentration estimates in a high resolution (500 m) for an arbitrary segmentation.

Baltic Sea Ice Concentration Estimation Using SENTINEL-1 SAR and AMSR2 Microwave Radiometer Data

Sea ice concentration (SIC) is an important sea ice parameter for sea ice navigation, environmental research, and weather and ice forecasting. We have developed and tested a method for estimation of the Baltic Sea SIC using SENTINEL-1 synthetic aperture radar (SAR) and Advanced Microwave Scanning Radiometer 2 passive microwave radiometer (MWR) data. Here, we present the method and results for January 2016. Ice concentration grids of Finnish Meteorological Institute daily ice charts have been used as reference data in this paper. We present a comparison of four SIC estimation methods with our reference data. In addition to the combined SAR/MWR SIC estimation method, we also compare SIC estimates produced using SAR alone and two MWR-based methods. The main target of this paper was to develop and test a high-resolution SIC estimation method suitable for operational use.

A Comparison Between High-Resolution EO-Based and Ice Analyst-Assigned Sea Ice Concentrations

In the 4th Ice Analyst Workshop 2014, ice analysts made ice concentration (IC) analysis based on two synthetic aperture radar (SAR) images over the Gulf of Bothnia located in the northern Baltic sea. The ice analysts were divided into five work groups and each group assigned ICs to predefined polygons. This exercise gave us a good opportunity to compare the analysis performed by the work groups with the results produced by our dualpol-SAR IC estimation algorithm. The ice analyzes were performed and the algorithm was run in a resolution of 100 m. For reference, we also included the polygon median values produced by the ARTIST sea ice (ASI) radiometer-based algorithm producing the ICs in a 3.125-km grid. This kind of comparisons are very important to evaluate the automated algorithms with respect to the human interpretation. Human interpretation available in the ice charts is currently considered as the best available IC information.

Sea ice thickness retrieval from SAR imagery over Bohai sea

The Bohai sea is a semi-enclosed sea located in the northeast of China. Safety of ship navigation and exploration platforms are important issues. Reliable near real time ice information is necessary and the ice thickness distribution is still a challenge. Here we present an ice thickness estimation method combining numerical sea ice model and SAR data. A high resolution thermodynamic snow and ice model (HIGHTSI) is applied to calculate the thermodynamic ice growth and used as the ice thickness background. SAR images are used to express the local ice statistics and to redistribute the modeled ice thickness. The results are evaluated by comparing with in-situ observations from oil platforms and ice forecast results from National Marine Environmental Forecasting Center (NMEFC).

Dependence between standard deviation and measurement length for C-band backscattering signatures of the Baltic Sea ice

This paper studies whether the standard deviation (std) of the Baltic Sea ice backscattering coefficient (sigmadeg) depends on the length of measurement (l). For many kinds of surfaces, especially for a fractal one, this is the case. The study was conducted using one-dimensional C-band helicopter-borne scatterometer data and ENVISAT synthetic aperture radar (SAR) images. The results with both data sets indicate mostly a strong linear dependence between ln(l) and ln(std(sigmadeg)) up to a distance of at least a few kilometers. Based on the analysis of empirical and simulated data (fractal and nonfractal profiles), it seems that sea ice sigmadeg as a function of l is not completely described either by fractional Brownian motion or by a process with a single-scale autocorrelation function. Neither can the values of sigmadeg be regarded as samples from only one probability distribution. The regression coefficients describing the dependency of ln(l) versus ln(std(sigmadeg)) do not discriminate various ice types better than just mean and std of sigmadeg. However, the use of regression coefficients instead of mean and std is preferred due to their scale-invariant comparability with the results of other studies. The dependence of std(sigmadeg) on l should also be taken generally into account in the data analysis, e.g., when constructing classifiers for sea ice SAR data

An assessment of arctic sea ice concentration retrieval based on “HY-2” scanning radiometer data using field observations during CHINARE-2012 and other satellite instruments

A retrieval algorithm of arctic sea ice concentration (SIC) based on the brightness temperature data of “HY-2” scanning microwave radiometer has been constructed. The tie points of the brightness temperature were selected based on the statistical analysis of a polarization gradient ratio and a spectral gradient ratio over open water (OW), first-year ice (FYI), and multiyear ice (MYI) in arctic. The thresholds from two weather filters were used to reduce atmospheric effects over the open ocean. SIC retrievals from the “HY-2” radiometer data for idealized OW, FYI, and MYI agreed well with theoretical values. The 2012 annual SIC was calculated and compared with two reference operational products from the National Snow and Ice Data Center (NSIDC) and the University of Bremen. The total ice-covered area yielded by the “HY-2” SIC was consistent with the results from the reference products. The assessment of SIC with the aerial photography from the fifth Chinese national arctic research expedition (CHINARE) and six synthetic aperture radar (SAR) images from the National Ice Service was carried out. The “HY-2” SIC product was 16% higher than the values derived from the aerial photography in the central arctic. The root-mean-square (RMS) values of SIC between “HY-2” and SAR were comparable with those between the reference products and SAR, varying from 8.57% to 12.34%. The “HY-2” SIC is a promising product that can be used for operational services.

Cloud masking of MODIS imagery based on multitemporal image analysis

We present multitemporal Bayesian classification of Moderate Resolution Imaging Spectroradiometer (MODIS) mosaic data over the Caspian Sea during winter. The multitemporal analysis methods used were cross-correlation and motion detection based on phase correlation. Our motion estimation algorithm estimates the motion of a target between two adjacent images over the same areas based on finding the maximum correlation with respect to location shift within a given area around each location. The motion detection algorithm also provides a quality estimate for the detection. Because sea ice, unlike clouds, is typically rigid and its motion is significantly slower than the motion and metamorphosis of clouds, drifting sea ice can be distinguished from clouds. Over land and static sea ice, detection of clouds, is easier because the cross-correlation is typically higher for land and ice than for clouds. Also, locating clouds over open water is straightforward because clouds appear significantly brighter than open water. The results show that multitemporal features can be used to distinguish between clouds and clear sky.

Bohai sea ice thickness estimation based on thermodynamic ice model and earth observation data

SAR data and its texture features are used to estimate the ice thickness over Liaodong Bay with ice model thickness. Sea ice and open water discrimination works well for dual-polarized data using a simple linear model. For ice thickness estimation the number of data points is too limited, but relatively good estimates can be extracted using the leave-one-out approach. The leave-one-out ice thickness estimation (N=31) accuracy is below: mean error is 5.8cm and RMSE is 7.1 cm.