The detection, monitoring, and forecasting of sea-ice conditions, including their extremes, is very important for ship navigation and offshore activities, and for monitoring of sea-ice processes and trends. We summarize here recent advances in the monitoring of sea-ice conditions and their extremes from satellite data as well as the development of sea-ice seasonal forecasting capabilities. Our results are the outcome of the three-year (2015–2018) SPICES (Space-borne Observations for Detecting and Forecasting Sea-Ice Cover Extremes) project funded by the EU’s Horizon 2020 programme. New SPICES sea-ice products include pancake ice thickness and degree of ice ridging based on synthetic aperture radar imagery, Arctic sea-ice volume and export derived from multisensor satellite data, and melt pond fraction and sea-ice concentration using Soil Moisture and Ocean Salinity (SMOS) radiometer data. Forecasts of July sea-ice conditions from initial conditions in May showed substantial improvement in some Arctic regions after adding sea-ice thickness (SIT) data to the model initialization. The SIT initialization also improved seasonal forecasts for years with extremely low summer sea-ice extent. New SPICES sea-ice products have a demonstrable level of maturity, and with a reasonable amount of further work they can be integrated into various operational sea-ice services.

We estimate two essential sea ice parameters—namely, sea ice concentration (SIC) and sea ice thickness (SIT)—for the Bohai Sea using a combination of a thermodynamic sea ice model and Earth observation (EO) data from synthetic aperture radar (SAR) and microwave radiometer. We compare the SIC and SIT estimation results with in-situ measurements conducted in the study area and estimates based on independent EO data from near-infrared/optical instruments. These comparisons suggest that the SAR-based discrimination between sea ice and open-water works well, and areas of thinner and thicker ice can be distinguished. A larger comprehensive training dataset is needed to set up an operational algorithm for the estimation of SIC and SIT.

The automatic identification system (AIS) was developed to support the safety of marine traffic. In ice-covered seas, the ship speeds extracted from AIS data vary with ice conditions that are simultaneously reflected by features in synthetic aperture radar (SAR) images. In this study, the speed variation was related to the SAR features and the results were applied to generate a chart of expected speeds from the SAR image. The study was done in the Gulf of Bothnia in March 2013 for ships with ice class IA Super that are able to navigate without icebreaker assistance. The speeds were normalized to dimensionless units ranging from 0 to 10 for each ship. As the matching between AIS and SAR was complicated by ice drift during the time gap (from hours to two days), we calculated a set of local statistical SAR features over several scales. Random forest tree regression was used to estimate the speed. The accuracy was quantified by mean squared error and by the fraction of estimates close to the actual speeds. These depended strongly on the route and the day. The error varied from 0.4 to 2.7 ${\mathrm{units}}^2$ for daily routes. Sixty-five percent of the estimates deviated by less than one speed unit and 82% by less than 1.5 speed units from the AIS speeds. The estimated daily mean speeds were close to the observations. The largest speed decreases were provided by the estimator in a dampened form or not at all. This improved when the ice chart thickness was included as a predictor.

We introduce a method to construct an ice thickness chart using two satellite datasets and a one-dimensional high-resolution thermodynamic snow and sea-ice model (HIGHTSI). Thin-ice thickness up to 40 cm is retrieved using MODIS ice surface temperature data. For thicker ice, thickness is retrieved from a combination of SAR image and background level ice thickness chart H_i provided by HIGHTSI. Because H_i is inherently static, we utilize passive microwave data based sea-ice concentration charts to introduce mesoscale ice dynamics into H_i. In order to create the thickness chart, the value of modified H_i is scaled by a factor which depends on the magnitude of backscattering coefficient σ⁰. The proposed method is effective in cold winter conditions. We calculated a series of ice thickness charts covering the Kara Sea, Arctic Russia, during winter 2008/09. The ice thickness charts were validated against Russian ice charts. Our method gave realistic results in the thickness range 30–120 cm and was uncertain for the detection of thinner-ice areas. Due to the limitations in our reference dataset, more validation work is needed to establish the accuracy in more detail. Our thickness charts can be used for operational purposes and in climate studies.

We have developed a thin ice detection algorithm for the AMSR2 radiometer data. The algorithm, denoted as AMSR2 thin ice detection algorithm—version 2 (ATIDA2), is targeted for the Arctic Ocean. The detection of thin ice with a maximum thickness of 20 cm is based on the classification of the 36-GHz polarization ratio ( $PR36$ ) and H-polarization 89–36-GHz gradient ratio ( $GR8936H$ ) signatures with a linear discriminant analysis (LDA) and thick ice restoration with $GR3610H$ . The thick ice restoration removes erroneous thin ice detections due to thin and thick ice $PR36$ and $GR8936H$ signature mixing. ATIDA2 is applied only when sea ice concentration (SIC) is ≥70% and the air temperature is ≤−5 °C to decrease misclassification of thick ice as thin ice. For the AMSR2 L1R brightness temperature data, an atmospheric correction is applied following an EUMETSAT OSI SAF correction scheme in SIC retrieval algorithms. ATIDA2 is applied to L1R swath datasets, and then, the results are combined to a daily thin ice chart. ATIDA2 was developed and validated using MODIS ice thickness charts over the Barents and Kara Seas. The average probability for misclassification of thick ice as thin ice in the daily chart is 8.7%, and 37.0% for vice versa. The comparison of the ATIDA2 chart and the SMOS ice thickness chart over the Arctic showed rough correspondence in the thin versus thick ice classification. The ATIDA2 chart is targeted to be used together with SAR imagery for various sea ice classifications.