You must have a javascript-enabled browser and javacript and stylesheets must be enabled to use some of the functions on this site.


Inferring the degree of ice field deformation in the Baltic Sea using ENVISAT ASAR images

Markku Similä(1) and Juha Karvonen(1)

(1) Finnish Institute of Marine Research, PO Box 2, 00561 Helsinki, Finland


If the the snow layer is thin and the weather is cold, the C-band backscattering in the Baltic Sea is modulated by the ice surface roughness. The presence or absence of large scale ice surface roughness indicates the occurrence of mechanical ice deformation. Hence it is reasonable to expect that in drift ice areas the magnitude of the backscattering coefficient $sigma^0$ is strongly correlated with the ice thickness. Relying on this hypothesis we at Finnish Insitute of Marine Research (FIMR) have tested an approach to estimate the degree of deformation of underlying ice cover from a SAR image.

Our field data set consists of helicopter-borne electromagnetic induction sensor (EM) based ice thickness measurements. These measurements were made and processed by Alfred Wegener Insititute. The data set analyzed here consists of EM measurement transects (totally 480 km) combined with three nearly simultaneous ENVISAT ASAR IMP images acquired in March 2005 as a part of the Finnish ENVISAT ASAR CAL/VAL project ESSI ("ENVISAT and the Ice Conditions in the Baltic Sea"). These two data sets were co-registered with respect to each other.

Due to continuous ice field movement and location inaccuracies in both data sets the registration is imprecise. This fact combined with the ambiguities between the SAR signal magnitude and the ice thickness value lead us to study a procedure where we consider ice fields associated with certain backscattering coefficient, i.e we assign a whole ice thickness distribution to each $sigma^0$ rounded to the nearest integer. E.g., in the regression approach this dependence is unambiguous, i.e., a certain predictor value ($sigma^0$) corresponds a certain response (ice thickness value).

The critical step in the proposed approach is to estimate the ice thickness distributions associated with different $sigma^0$ values in a coherent manner. To this end two geophysically motivated consistency requirements were applied. First, the range of admissable ice thickness values had its maximum range at the largest $sigma^0$ and it was required to decrease with the decreasing $sigma^0$ values. Secondly, the probability of an occurrence of an ice ridge was required to increase with the increasing $sigma^0$ value. We modelled the thickness distributions as a mixture distribution with two components. One component described the thickness distribution in the level and rafted ice areas (in our data set an appropriate limit to this was 100 cm), the other component described thickness distributions for the rest of values (in our data set the upper limit was 800 cm). The densities of both components were estimated with kernel density estimator with a Gaussian kernel. The smoothing parameter $h$ was selected to be much larger for ridged ice than for level ice. The proportions between level/rafted ice, on one hand, and ridged ice, on the other hand, were controlled by the mixture parameter $p$, which describes the relative proportion of deformed ice. We remark that several analytic expressions were fitted to the densities but no satisfying formula was found. There were two contradicting requirements: a sharp peak for level/rafted ice and a long, heavy tail for ridged ice.

First, the ice thickness densities were estimated for each discretized $sigma^0$ in the range (-21 dB, -7 dB). Then the estimated density for a given segment was obtained as a mixture of all occurred densities along the segment. Each occurrence event is indicated by the corresponding $sigma^0$. To test our approach we determined the degree of ice deformation in a given segment as a percentage of ice cover with ice thickness exceeding 2 m. The deformation degrees increases in five percent intervals, starting from zero (area of thick ice less than 5 % of the total area) to four (area of thick ice more than 20 % of the total area). The respective intervals were determined for estimated ice thickness. If the segment length was chosen to be 15 km (16 segments in our training set, 16 in the test set), the measured and estimated deformation degrees agreed for three segments, deviated just one category in ten cases and more in three cases. In the last three cases the mean magnitude of backscattering strength was over -13.5 dB but according to the EM measurements the ice cover was thin, i.e., of category zero.


Full paper


  Higher level                 Last modified: 07.10.03