Operational Ice Charts Research Articles

Arctic Sea Ice and Open Water Classification From Spaceborne Fully Polarimetric Synthetic Aperture Radar

We present an approach for automatic classification of Arctic sea ice and open water from spaceborne C-band RADARSAT-2 fully polarimetric (HH, HV, VH, VV) synthetic aperture radar (SAR) imagery, based on the random forest (RF) model. The HH- and HV-polarized radar backscatter, incidence angle and optimal polarimetric features are inputs of the RF model. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">First</i> , we use a physics-based unsupervised scheme to generate well-annotated sea ice and open water samples. This scheme can alleviate labeling errors arising from visual interpretation or temporal-spatial mismatch between SAR images and operational ice charts. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Subsequently</i> , we estimate a set of characteristic polarimetric parameters and select the most representative features, using the recursive feature elimination method. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Finally</i> , the RF model is trained and validated using more than one million labelled samples. Statistical validation results show that the overall sea ice and water classification accuracy is 99.94% and the Kappa coefficient is 0.999. We find that ice–water discrimination accuracy can be improved by about 4%–10%, when optimal polarimetric features are involved in the RF model input. Moreover, the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">polarization difference</i> (PD, VV–HH) is found to be the foremost polarimetric parameter for distinguishing sea ice from open water. The proposed approach has capability of yielding satisfactory ice–water classification over the complicated marginal ice zone. High-resolution ice–water classification maps clearly show that sea ice leads and their surroundings are also well separated.

Southern Ocean ice charts at the Argentine Naval Hydrographic Service and their impact on safety of navigation

Antarctica is a largely inhospitable and inaccessible continent that plays a key role in regulating the climate through ocean currents, winds, icebergs drift, and sea-ice concentration and thickness. The study area of ​​this work corresponds to the Weddell Sea, Bellingshausen Sea and the South Atlantic Ocean. These areas are relevant because of supply operations to Antarctic stations and scientific and tourist activities. The Antarctic Peninsula is the most visited region of the continent for tourist and research vessels and requires special efforts in the development and dissemination of updated ice information for Safety of Navigation. For this purpose, it is critical to have information that discriminates the origin of the ice from land and open water, sea-ice concentration, and stage of development. The high recurrence of cloud cover over the Antarctic Peninsula during the summer hinders the operational use of visible/infra red satellite imagery, therefore access to Synthetic Aperture Radar (SAR) sensors is considered to be a high priority. Between 2018 and 2020, with the launch of the SAOCOM (Satélite Argentino de Observación Con Microondas) constellation, Argentina has evidenced an increase in the availability of SAR images for sea-ice operations. This paper presents the current state of routine production of operational ice charts at the Argentine Naval Hydrographic Service for mariners in the vicinity of the Antarctic Peninsula and South Atlantic Ocean and discuss future developments in place to prepare for expected increases in marine traffic in these areas.

Accounting for Label Errors When Training a Convolutional Neural Network to Estimate Sea Ice Concentration Using Operational Ice Charts

Convolutional neural networks (CNNs) are being increasingly investigated as a means to extract sea ice concentration from synthetic aperture radar (SAR) in an automated manner. This is often done using ice charts as training data. However, in these charts, an ice concentration label is given to a large region, which may not have a spatially uniform sea ice concentration distribution at the prediction scale of the CNN. This leads to representativity errors, which can be more pronounced at intermediate sea ice concentrations. In this study, we first investigate ways to perturb the ice chart labels to obtain improved predictions to account for the label uncertainty for intermediate ice concentrations. We then propose a method to augment the ice chart data by rescaling the information in the SAR imagery. The method is found to lead to improved accuracy in comparison to using the ice chart labels alone, with accuracy improving from 0.919 to 0.966. The sea ice concentration maps with the augmented labels also have much finer detail than the other approaches evaluated. These details are visually in agreement with expected sea ice concentration from the SAR data.

An Application of Sea Ice Tracking Algorithm for Fast Ice and Stamukhas Detection in the Arctic

For regional environmental studies it is important to know the location of the fast ice edge which affects the coastal processes in the Arctic. The aim of this study is to develop a new automated method for fast ice delineation from SAR imagery. The method is based on a fine resolution hybrid sea ice tracking algorithm utilizing advantages of feature tracking and cross-correlation approaches. The developed method consists of three main steps: drift field retrieval at sub-kilometer scale, selection of motionless features and edge delineation. The method was tested on a time series of C-band co-polarized (HH) ENVISAT ASAR and Sentinel-1 imagery in the Laptev and East Siberian Seas. The comparison of the retrieved edges with the operational ice charts produced by the Arctic and Antarctic Research Institute (Russia) showed a good agreement between the data sets with a mean distance between the edges of &lt;15 km. Thanks to the high density of the ice drift product, the method allows for detailed fast ice edge delineation. In addition, large stamukhas with horizontal size of tens of kilometers can be detected. The proposed method can be applied for regional fast ice mapping and large stamukhas detection to aid coastal research. Additionally, the method can serve as a tool for operational sea ice mapping.

Estimation of degree of sea ice ridging in the Bay of Bothnia based on geolocated photon heights from ICESat-2

Abstract. We present a comparison of Ice, Cloud and land Elevation Satellite-2 (ICESat-2) geolocated photon heights and operational ice charts from the Finnish Ice Service in the Bay of Bothnia in spring 2019. We show that ICESat-2 (IS2) retrievals from ice areas with different ridging characteristics, more precisely the degree of ice ridging (DIR), differ significantly. DIR is a particularly useful parameter for ice navigators, as it provides information on how difficult it is to navigate through an area based on e.g. sail heights and distribution of sea ice ridges. DIR estimates are included in ice charts of the Baltic Sea and are based primarily on in situ observations from an active icebreaker fleet. We show that DIR may potentially be estimated from IS2. We also present a comparison of IS2 measurements and Sentinel-1 synthetic aperture radar frames, discussing several individual cases of IS2 photon elevation behaviour over Baltic sea ice. We suggest that IS2 data can be of benefit to international ice services, especially if a time-critical photon height product were to be made available. Furthermore, we show that the difference between highest and mean photon elevations (elevation anomalies) of IS2 correspond to expected ridge sail heights in our study area. Our study is one of the first steps in creating sea ice applications beyond the traditional goal of freeboard and thickness retrieval for IS2.

MYI Floes Identification Based on the Texture and Shape Feature from Dual-Polarized Sentinel-1 Imagery

The Northwest Passage (NWP) in the Arctic is usually covered with hazardous multi-year ice (MYI) and seasonal first-year ice (FYI) in winter, with possible thin ice and open-water areas during transition seasons. Ice classification is important for both marine navigation and climate change studies. Satellite-based Synthetic Aperture Radar (SAR) systems have shown advantages of retrieving this information. Operational ice mapping relies on visual analysis of SAR images along with ancillary data. However, these maps estimate ice types and concentrations within large-size polygons of a few tens or hundreds of kilometers, which are subjectively identified and selected by analysts. This study aims at developing an automated algorithm to identify individual MYI floes from SAR images then classify the rest of the image as FYI and other ice types. The algorithm identifies the MYI floes using extended-maximum operator, morphological image processing, and a few geometrical features. Classifying the rest of the image uses texture and neural network model. The input data is a set of Sentinel-1 A/B Extended Wide (EW) mode images, acquired between September and March 2016–2019. Although the overall accuracy (for all type classification) from the new method scored 93.26%, the accuracy from using the texture classifier only was 75.81%. The kappa coefficient from the former was higher than the latter by 0.25. Compared with the operational ice charts from the Canadian Ice Service, ice type maps from the new method show better distribution of MYI at the fine scale of individual floes. Comparison against MYI concentration from two automated algorithms that use a combination of coarse-resolution passive and active microwave data also confirms the advantage of resolving MYI floes from the fine-resolution SAR.

Discrimination of different sea ice types from CryoSat-2 satellite data using an Object-based Random Forest (ORF)

Sea ice type is one of the most sensitive variables in Arctic sea ice monitoring, and it is important for the retrieval of ice thickness. In this study, we analyzed various waveform features that characterize the echo waveform shape and Sigma0 (i.e., backscatter coefficient) of CryoSat-2 synthetic aperture radar altimeter data over different sea ice types. Arctic and Antarctic Research Institute operational ice charts were input as reference. An object-based random forest (ORF) classification method is proposed with overall classification accuracy of 90.1%. Accuracy of 92.7% was achieved for first-year ice (FYI), which is the domain ice type in the Arctic. Accuracy of 76.7% was achieved at the border of FYI and multiyear ice (MYI), which is better than current state-of-the-art methods. Accuracy of 83.8% was achieved for MYI. Results showed the overall accuracy of the ORF method was increased by ∼8% in comparison with other methods, and the classification accuracy at the border of FYI and MYI was increased by ∼10.5%. Nevertheless, ORF classification performance might be influenced by the selected waveform features, snow loading, and the ability to distinguish sea ice from leads.

A Study of the Technology Used to Distinguish Sea Ice and Seawater on the Haiyang-2A/B (HY-2A/B) Altimeter Data

When the Haiyang-2B (HY-2B) was launched into space to form a star network with the Haiyang-2A (HY-2A), it provided new data sources for the sea ice research of the Earth’s polar regions. The ability of altimeter echoes to distinguish sea ice and sea water is usable in operational ice charting. In this research study, the level 1B (L1B) data of HY-2A/B altimeter from November 2018 was used to analyze the altimeter waveforms from the polar regions. The Suboptimal Maximum Likelihood Estimation (SMLE) and Offset Center of Gravity (OCOG) tracking packages could maintain the waveform characteristics of diffused and quasi-specular surfaces by comparison. Also, they could be utilized to distinguish sea ice from seawater in the polar regions. It was determined that the types of echoes obtained from the seawater were diffuse. Also, some “ocean-like” waveform data had existed for the old ice formations in the Arctic regions during the study period. The types of echoes obtained from Arctic sea ice were found to be mainly quasi-specular. In the present study, three methods (Threshold segmentation, K-nearest-neighbor (KNN), and Lib-Support Vector machine (LIBSVM)) with four waveform parameters (Automatic Gain Control (AGC) and Pulse Peaking (PP) values of the Ku and C Bands) were adopted to distinguish between the sea ice and seawater areas. The accuracy rate of the separation results for the LIBSVM except band Ku from HY-2B ALT was found to be less than 40% in Antarctic. Meanwhile, the other two methods were observed to have maintained the waveforms correctly at accuracy rates of approximately 80% in Antarctic and the Arctic. In addition, the observed distinguishing errors were located in the regions of the old ice of the Arctic region. In addition, due to the summer melting processes, the large number of ice floes and the snow cover had made it difficult to distinguish the seawater and sea ice in the Antarctic regions.

Automatic Detection of Small Icebergs in Fast Ice Using Satellite Wide-Swath SAR Images

Automatic detection of icebergs in satellite images is regarded a useful tool to provide information necessary for safety in Arctic shipping or operations over large ocean areas in near-real time. In this work, we investigated the feasibility of automatic iceberg detection in Sentinel-1 Extra Wide Swath (EWS) SAR images which follow the preferred image mode in operational ice charting. As test region, we selected the Barents Sea where the size of many icebergs is on the order of the spatial resolution of the EWS-mode. We tested a new approach for a detection scheme. It is based on a combination of a filter for enhancing the contrast between icebergs and background, subsequent blob detection, and final application of a Constant False Alarm Rate (CFAR) algorithm. The filter relies mainly on the HV-polarized intensity which often reveals a larger difference between icebergs and sea ice or open water. The blob detector identifies locations of potential icebergs and thus shortens computation time. The final detection is performed on the identified blobs using the CFAR algorithm. About 2000 icebergs captured in fast ice were visually identified in Sentinel-2 Multi Spectral Imager (MSI) data and exploited for an assessment of the detection scheme performance using confusion matrices. For our performance tests, we used four Sentinel-1 EWS images. For judging the effect of spatial resolution, we carried out an additional test with one Sentinel-1 Interferometric Wide Swath (IWS) mode image. Our results show that only 8–22 percent of the icebergs could be detected in the EWS images, and over 90 percent of all detections were false alarms. In IWS mode, the number of correctly identified icebergs increased to 38 percent. However, we obtained a larger number of false alarms in the IWS image than in the corresponding EWS image. We identified two problems for iceberg detection: 1) with the given frequency–polarization combination, not all icebergs are strong scatterers at HV-polarization, and (2) icebergs and deformation structures present on fast ice can often not be distinguished since both may reveal equally strong responses at HV-polarization.

Combined observations of Arctic sea ice with near‐coincident colocated X‐band, C‐band, and L‐band SAR satellite remote sensing and helicopter‐borne measurements

AbstractIn this study, we compare colocated near‐coincident X‐, C‐, and L‐band fully polarimetry SAR satellite images with helicopter‐borne ice thickness measurements acquired during the Norwegian Young sea ICE 2015 (N‐ICE2015) expedition in the region of the Arctic Ocean north of Svalbard in April 2015. The air‐borne surveys provide near‐coincident snow plus ice thickness, surface roughness data, and photographs. This unique data set allows us to investigate how the different frequencies can complement one another for sea ice studies, but also to raise awareness of limitations. X‐band and L‐band satellite scenes were shown to be a useful complement to the standard SAR frequency for sea ice monitoring (C‐band) for lead ice and newly formed sea ice identification. This may be in part be due to the frequency but also the high spatial resolution of these sensors. We found a relatively low correlation between snow plus ice thickness and surface roughness. Therefore, in our dataset ice thickness cannot directly be observed by SAR which has important implications for operational ice charting based on automatic segmentation.

Utilisation of CryoSat-2 SAR altimeter in operational ice charting

Abstract. We present methods to utilise CryoSat-2 (CS-2) synthetic aperture radar (SAR) mode data in operational ice charting. We compare CS-2 data qualitatively to SAR mosaics over the Barents and Kara seas. Furthermore, we compare the CS-2 to archived operational ice charts. We present distributions of four CS-2 waveform parameters for different ice types as presented in the ice charts. We go on to present an automatic classification method for CS-2 data which, after training with operational ice charts, is capable of determining open ocean from ice with a hit rate of &gt; 90 %. The training data are dynamically updated every 5 days using the most recent 15 days of CS-2 data and operative ice charts. This helps the adaption of the classifier to the evolving ice/snow conditions throughout winter. The classifier is also capable of detecting three different ice classes (thin and thick first-year ice as well as old ice) with success rates good enough for the output to be usable to support operational ice charting. Finally, we present a near-real-time CS-2 product just plotting the waveform characteristics and conclude that even such a simple product is usable for some of the needs of ice charting.

Sea Ice Surface Temperature Estimation Using MODIS and AMSR-E Data Within a Guided Variational Model Along the Labrador Coast

In this study, a new method, entitled as the multi-modality guided variational (MGV) method, is proposed, in which the data from a passive microwave sensor is used jointly with the data from the Moderate Resolution Imaging Spectroradiometer (MODIS) to estimate the sea ice surface temperature (IST). The method augments existing sea IST values from the MODIS IST map, while filling in areas in the MODIS image that may be sparsely sampled due to the cloud cover, or due to increased spacing between the pixels at the swath edges. The former issue is particularly problematic in the marginal ice zone, where the atmospheric conditions often lead to persistent cloud cover. The sea IST is of interest because it can be used to estimate the sea ice thickness, an important parameter for shipping, climate change, and weather forecasting applications. The impact of the MGV method is checked through a comparison between the sea ice thickness calculated using the swath surface temperature and that calculated using the surface temperature from MGV. Using the operational ice charts as a guideline, it is found that the sea ice thickness values calculated using the MGV surface temperature are realistic, and there is a 16% increase in the number of sea ice thickness data points available when the MGV method is used as compared to when the swath data are used.

An Assessment of Sea-Ice Thickness Along the Labrador Coast From AMSR-E and MODIS Data for Operational Data Assimilation

In this paper, sea-ice thickness values are calculated along the Labrador coast using data from two sensors representative of those available for operational data assimilation. Data from the first sensor, the Moderate Resolution Imaging Spectroradiometer (MODIS), are used to calculate the ice thickness using a heat balance equation. Relationships between the MODIS ice thickness and polarization ratio from the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) are used to calculate the thickness of thin ice (less than 0.2 m) from the AMSR-E data. This is done for each frequency on the AMSR-E sensor in the range of 6.9-36.5 GHz. Through comparison with data from ice charts, it is found that the errors are lowest for thickness values calculated from low-frequency AMSR-E data. The accuracies of the ice thickness from MODIS, AMSR-E, operational ice charts, and two moored upward looking sonars are further assessed using the triple collocation method. It is found that the error associated with ice thickness from AMSR-E is the lowest and the error associated with ice thickness from MODIS is the highest. While the MODIS data represent the small-scale variability of the sea-ice thickness better than the AMSR-E data, the MODIS data can produce spurious values of ice thickness due to unmasked clouds. To use ice thickness from MODIS in an automated algorithm, quality control would need to be applied to the MODIS data to remove unmasked clouds which lead to spurious values of thick ice. The errors calculated for the ice thickness from AMSR-E, which are calculated based on a relationship calibrated with MODIS ice thickness from a clear-sky day, indicate that these data would be useful for operational data assimilation.

Validation and potential applications of Environment Canada Ice Concentration Extractor (ECICE) algorithm to Arctic ice by combining AMSR-E and QuikSCAT observations

The Environment Canada's Ice Concentration Extractor (ECICE) combines observations from several different satellite sensors to resolve heterogeneous components of a given footprint. To validate the algorithm and demonstrate its applicability, results are presented from combining the enhanced AMSR-E 36.5GHz passive microwave data with dual-polarization QuikSCAT active microwave scatterometer observations of Arctic ice during September to May; 2007/08. Validation is performed using comparison with results from other algorithms in addition to operational ice charts. Three ice types are resolved: young, first-year and multiyear. Total ice concentration from ECICE under cold Arctic winter conditions is in agreement with estimates from previous algorithms such as the enhanced NASA Team. Distribution of multiyear ice concentration from ECICE is presented along with evolution of daily concentration of each ice type. Events of melt-refreeze, which are common during seasonal transition periods, cause misidentification of multiyear ice as first-year ice in the fall. The reverse is observed in the spring. This is a limitation on ice type identification. ECICE is an optimal approach that minimizes the error between observations and predicted concentrations. It provides a confidence measure associated with each ice concentration estimate. It is a generic algorithm, i.e. its applications are not limited to AMSR-E and QuikSCAT.

Direct Assimilation of AMSR-E Brightness Temperatures for Estimating Sea Ice Concentration

Abstract In this paper a method to directly assimilate brightness temperatures from the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) to produce ice concentration analyses within a three-dimensional variational data assimilation system is investigated. To assimilate the brightness temperatures a simple radiative transfer model is used as the forward model that maps the state vector to the observation space. This allows brightness temperatures to be modeled for all channels as a function of the total ice concentration, surface wind speed, sea surface temperature, ice temperature, vertically integrated water vapor, and vertically integrated cloud liquid water. The brightness temperatures estimated by the radiative transfer model are sensitive to the specified values for the sea ice emissivity. In this paper, two methods of specifying the sea ice emissivity are compared. The first uses a constant value for each polarization and frequency, while the second uses a simple emissivity parameterization. The emissivity parameterization is found to significantly improve the fit to the observations, reducing both the bias and the standard deviation. Results from the assimilation of brightness temperatures are compared with those from assimilating a retrieved ice concentration in the context of initializing a coupled ice–ocean model for an area along the east coast of Canada. It is found that with the emissivity parameterization the assimilation of brightness temperatures produces ice concentration analyses that are in slightly better agreement with operational ice charts than when assimilating an ice concentration retrieval, with the most significant improvements during the melt season.

Comparison of NASA Team2 and AES-york ice concentration algorithms against operational ice charts from the Canadian ice service

Ice concentration retrieved from spaceborne passive-microwave observations is a prime input to operational sea-ice-monitoring programs, numerical weather prediction models, and global climate models. Atmospheric Environment Service (AES)-York and the Enhanced National Aeronautics and Space Administration Team (NT2) are two algorithms that calculate ice concentration from SpecialSensor Microwave/Imager observations. This paper furnishes a comparison between ice concentrations (total, thin, and thick types) output from NT2 and AES-York algorithms against the corresponding estimates from the operational analysis of Radarsat images in the Canadian Ice Service (CIS). A new data fusion technique, which incorporates the actual sensor's footprint, was developed to facilitate this study. Results have shown that the NT2 and AES-York algorithms underestimate total ice concentration by 18.35% and 9.66% concentration counts on average, with 16.8% and 15.35% standard deviation, respectively. However, the retrieved concentrations of thin and thick ice are in much more discrepancy with the operational CIS estimates when either one of these two types dominates the viewing area. This is more likely to occur when the total ice concentration approaches 100%. If thin and thick ice types coexist in comparable concentrations, the algorithms' estimates agree with CIS's estimates. In terms of ice concentration retrieval, thin ice is more problematic than thick ice. The concept of using a single tie point to represent a thin ice surface is not realistic and provides the largest error source for retrieval accuracy. While AES-York provides total ice concentration in slightly more agreement with CIS's estimates, NT2 provides better agreement in retrieving thin and thick ice concentrations

The use of operational ice charts for evaluating passive microwave ice concentration data

More than 1380 regional Canadian weekly sea‐ice charts for four Canadian regions and 839 hemispheric U.S. weekly sea‐ice charts from 1979 to 1996 are compared with passive microwave sea‐ice concentration estimates using the National Aeronautics and Space Administration (NASA) Team algorithm. Compared with the Canadian regional ice charts, the NASA Team algorithm underestimates the total ice‐covered area by 20.4% to 33.5% during ice melt in the summer and by 7.6% to 43.5% during ice growth in the late fall. The wide range in performance occurs because some Canadian regions such as the western Canadian Arctic are only partly in the marginal sea‐ice zone while the Hudson Bay and the East Coast regions are entirely in the marginal sea‐ice zone, being completely ice free in summer. Compared with the U.S. National Ice Center hemispheric chart series, the average underestimation is 18.6% in summer. During other times of the year the differences are less than 7.8%. The magnitudes of the underestimation during ice melt and ice growth periods with respect to the Canadian regional charts are higher than found in other studies in the literature. The difference in performance of the NASA Team algorithm when compared with the Canadian regional ice charts and hemispheric charts is attributed to: 1) passive microwave data are used in preparing the hemispheric charts so comparison with the hemispheric charts is not independent; 2) the Canadian regional charts are in the marginal ice zone where sea‐ice melt or growth conditions occur over a large percentage of the region and last for several weeks to a month; and 3) the tie points used to calibrate the NASA Team algorithm are tuned to perform best over the hemisphere rather than over individual regions. If the Canadian regional ice charts can be accepted as correct, then these results suggest exercising caution in using passive microwave ice concentration data in the marginal ice zone where ice melt and ice growth conditions are a major component of the sea‐ice regime.

Late twentieth century Northern Hemisphere sea‐ice record from U.S. National Ice Center ice charts

Arctic sea ice plays a key role in the climate system, by acting as the interface between a warm ocean and a cold atmosphere. Establishing the true pattern of recent behavior of the sea ice in this region is critical to simulating the role of sea ice in future climate. Recently released operational ice charts from the U.S. National Ice Center provide insight into the late twentieth century behavior of Northern Hemisphere sea ice, providing more reliable ice concentrations during summer and freeze‐up than those available from the passive microwave record. The major winter and summer modes of ice concentration variability observed from empirical orthogonal function analysis covering the period 1972–1994 are shown to indicate, respectively, the 1‐year lagged response of the sea ice to the North Atlantic Oscillation and the winter preconditioning of summer sea ice coverage in the eastern Arctic by the North Atlantic Oscillation. Feedback to the atmosphere is suggested in each case by zero‐lag cyclone frequency relationships to these two sea ice modes of variability.

Ice charting based on multispectral satellite data in the Baltic Sea

Results are presented from an experiment concerning operational space-borne ice charting based on the Russian Ocean and Resource satellite systems. The surface truth consisted of routine operational data, helicopter-borne reconnaissance, and some ground measurements. Examples of the satellite imagery are given and identification of ice types is described. Cluster-analysis has been used for automatic image segmentation. The potential of these satellites in operational ice charting is discussed. A 160 m resolution optical scanner and a 2 km resolution radar are found to be very useful complements to the present routine system.

Ice charting based on multispectral satellite data in the Baltic Sea

Results are presented from an experiment concerning operational space-borne ice charting based on the Russian Ocean and Resource satellite systems. The surface truth consisted of routine operational data, helicopter-borne reconnaissance, and some ground measurements. Examples of the satellite imagery are given and identification of ice types is described. Cluster-analysis has been used for automatic image segmentation. The potential of these satellites in operational ice charting is discussed. A 160 m resolution optical scanner and a 2 km resolution radar are found to be very useful complements to the present routine system.