Abstract. Sea ice concentration has been retrieved in polar regions with satellite microwave radiometers for over 30 years. However, the question remains as to what is an optimal sea ice concentration retrieval method for climate monitoring. This paper presents some of the key results of an extensive algorithm inter-comparison and evaluation experiment. The skills of 30 sea ice algorithms were evaluated systematically over low and high sea ice concentrations. Evaluation criteria included standard deviation relative to independent validation data, performance in the presence of thin ice and melt ponds, and sensitivity to error sources with seasonal to inter-annual variations and potential climatic trends, such as atmospheric water vapour and water-surface roughening by wind. A selection of 13 algorithms is shown in the article to demonstrate the results. Based on the findings, a hybrid approach is suggested to retrieve sea ice concentration globally for climate monitoring purposes. This approach consists of a combination of two algorithms plus dynamic tie points implementation and atmospheric correction of input brightness temperatures. The method minimizes inter-sensor calibration discrepancies and sensitivity to the mentioned error sources.

With the advent of airborne and spaceborne synthetic aperture radar (SAR) systems, sea ice classification from SAR images has become an important research subject. Since gray tone alone has proven to be of limited capability in differentiating ice types, texture has naturally become an attractive avenue to explore. Accordingly, performance of texture quantification parameters as related to their ability to discriminate ice types has to be examined. SAR image appearance depends on radar parameters involved in the image construction procedures from the doppler history record. Therefore the feasibility of using universal texture/ice type relationships that hold for all combinations of radar parameters also has to be investigated. To that end, imagery data from three different SAR systems were used in this study. Five conventional texture parameters, derived from the gray level co‐occurrence matrix (GLCM), were examined. Two of them were modified to ensure their invariant character under linear gray tone transformations. Results indicated that all parameters were highly correlated. The parameters did not, in general, vary with the computational variables used in generating co‐occurrence matrices. Ice types can be identified uniquely by the mean value of any texture parameter. The relatively high variability of texture parameters, however, confuses ice discrimination, particularly of smoother ice types. Ice classification was conducted using a per‐pixel maximum likelihood supervised scheme. When texture was combined with gray tone, the overall average classification accuracy was improved. Texture was successful in improving the classification accuracy of multiyear ice but was less promising in discriminating first‐season ice types. The best two GLCM texture parameters, according to the computed overall average classification accuracies, were the inverse difference moment and the entropy. A brief description of GLCM texture parameters as related to ice's physical characteristics is presented.

No abstract

No abstract

Multiyear ice (MYI) characteristics can be retrieved from passive or active microwave remote sensing observations. One of the algorithms that combine both observations to identify partial concentrations of ice types (including MYI) is the Environment Canada Ice Concentration Extractor (ECICE). However, cycles of warm-cold air temperature trigger wet-dry cycles of the snow cover on MYI surface. Under wet snow conditions, anomalous brightness temperature and backscatter, similar to those of first-year ice (FYI), are observed. This leads to misidentification of MYI as being FYI, hence decreasing the estimated MYI concentration suddenly. The purpose of this paper is to introduce a correction scheme to restore the MYI concentration under this condition. The correction is based on air temperature records. It utilizes the fact that the warm spell in autumn lasts for a short period of time (a few days). The correction is applied to MYI concentration retrievals from ECICE using an input of combined QuikSCAT and AMSR-E data, acquired over the Arctic region in a series of autumn seasons from 2003 to 2008. The correction works well by replacing anomalous MYI concentrations with interpolated ones. For September of the six years, it introduces over0.1 × 10 6 km 2 MYI area, except for 2005. Due to the regional effect of warm air spells, the correction could be important in the operational applications where ice concentrations are crucial on small scale and mesoscale. Index Terms-Arctic sea ice, Environment Canada Ice Concentration Extractor (ECICE), ice concentration, microwave remote sensing, multiyear ice (MYI), surface air temperature.