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  • 1
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    Online Resource
    Modelling snow and ice thickness in the coastal Kara Sea, Russian Arctic
    International Glaciological Society ; 2013
    In:  Annals of Glaciology Vol. 54, No. 62 ( 2013), p. 105-113
    Details
    In: Annals of Glaciology, International Glaciological Society, Vol. 54, No. 62 ( 2013), p. 105-113
    Abstract: Snow and ice thickness in the coastal Kara Sea, Russian Arctic, were investigated by applying the thermodynamic sea-ice model HIGHTSI. The external forcing was based on two numerical weather prediction (NWP) models: the High Resolution Limited Area Model (HIRLAM) and the European Centre for Medium-Range Weather Forecasts (ECMWF) model. A number of model experiments were carried out applying different snow parameterization schemes. The modelled ice thickness was compared with in situ measurements and the modelled snow thickness was compared with the NASA Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) snow thickness. The HIRLAM and ECMWF model results agreed with each other on air temperature and wind. The NWP model precipitation forecasts caught up the synoptic-scale snowfall events, but the magnitude was liable to errors. The ice growth was modelled reasonably well applying HIGHTSI either with a simple parameterization for snow thickness or with the HIRLAM or ECMWF model precipitation as input. For the latter, however, an adjustment of snow accumulation in early winter was necessary to avoid excessive accumulation and consequent underestimation of ice thickness. Applying effective snow heat conductivity improved the modelled ice thickness. The HIGHTSI-modelled snow thickness had a seasonal evolution similar to that of the AMSR-E snow thickness. New field data are urgently needed to validate NWP and ice models and remote-sensing products for snow and sea ice in the Kara Sea.
    Type of Medium: Online Resource
    ISSN: 0260-3055 , 1727-5644
    URL: Article
    DOI: 10.3189/2013AoG62A180
    Language: English
    Publisher: International Glaciological Society
    Publication Date: 2013
    detail.hit.zdb_id: 2122400-6
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  • 2
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    Satellite Observations for Detecting and Forecasting Sea-Ice Conditions: A Summary of Advances Made in the SPICES Project by the EU’s Horizon 2020 Programme
    MDPI AG ; 2020
    In:  Remote Sensing Vol. 12, No. 7 ( 2020-04-10), p. 1214-
    Details
    In: Remote Sensing, MDPI AG, Vol. 12, No. 7 ( 2020-04-10), p. 1214-
    Abstract: 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.
    Type of Medium: Online Resource
    ISSN: 2072-4292
    URL: Article
    DOI: 10.3390/rs12071214
    Language: English
    Publisher: MDPI AG
    Publication Date: 2020
    detail.hit.zdb_id: 2513863-7
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  • 3
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    Operational Service for Mapping the Baltic Sea Landfast Ice Properties
    MDPI AG ; 2020
    In:  Remote Sensing Vol. 12, No. 24 ( 2020-12-09), p. 4032-
    Details
    In: Remote Sensing, MDPI AG, Vol. 12, No. 24 ( 2020-12-09), p. 4032-
    Abstract: The Baltic Sea is partly covered by sea ice in every winter season. Landfast ice (LFI) on the Baltic Sea is a place for recreational activities such as skiing and ice fishing. Over thick LFI ice roads can be established between mainland and islands to speed up transportation compared to the use of ferries. LFI also allows transportation of material to or from islands without piers for large ships. For all these activities, information on LFI extent and sea ice thickness, snow thickness and degree of ice deformation on LFI is very important. We generated new operational products for these LFI parameters based on synthetic aperture radar (SAR) imagery and existing products and prediction models on the Baltic Sea ice properties. The products are generated daily and have a 500 m pixel size. They are visualized in a web-portal titled “Baltic Sea landfast ice extent and thickness (BALFI)” which has free access. The BALFI service was started in February 2019. Before the BALFI service, information on the LFI properties in fine scale ( 〈 1 km) was not available from any single source or product. We studied the accuracy and quality of the BALFI products for the ice season 2019–2020 using ice charts and in-situ coastal ice station data. We suggest that the current products give usable information on the Baltic LFI properties for various end-users. We also identify some topics for the further development of the BALFI products.
    Type of Medium: Online Resource
    ISSN: 2072-4292
    URL: Article
    DOI: 10.3390/rs12244032
    Language: English
    Publisher: MDPI AG
    Publication Date: 2020
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  • 4
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    Bohai Sea Ice Parameter Estimation Based on Thermodynamic Ice Model and Earth Observation Data
    MDPI AG ; 2017
    In:  Remote Sensing Vol. 9, No. 3 ( 2017-03-03), p. 234-
    Details
    In: Remote Sensing, MDPI AG, Vol. 9, No. 3 ( 2017-03-03), p. 234-
    Abstract: 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.
    Type of Medium: Online Resource
    ISSN: 2072-4292
    URL: Article
    DOI: 10.3390/rs9030234
    Language: English
    Publisher: MDPI AG
    Publication Date: 2017
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  • 5
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    Online Resource
    Estimation of degree of sea ice ridging based on dual-polarized C-band SAR data
    Copernicus GmbH ; 2018
    In:  The Cryosphere Vol. 12, No. 1 ( 2018-01-26), p. 343-364
    Details
    In: The Cryosphere, Copernicus GmbH, Vol. 12, No. 1 ( 2018-01-26), p. 343-364
    Abstract: Abstract. For ship navigation in the Baltic Sea ice, parameters such as ice edge, ice concentration, ice thickness and degree of ridging are usually reported daily in manually prepared ice charts. These charts provide icebreakers with essential information for route optimization and fuel calculations. However, manual ice charting requires long analysis times, and detailed analysis of large areas (e.g. Arctic Ocean) is not feasible. Here, we propose a method for automatic estimation of the degree of ice ridging in the Baltic Sea region, based on RADARSAT-2 C-band dual-polarized (HH/HV channels) SAR texture features and sea ice concentration information extracted from Finnish ice charts. The SAR images were first segmented and then several texture features were extracted for each segment. Using the random forest method, we classified them into four classes of ridging intensity and compared them to the reference data extracted from the digitized ice charts. The overall agreement between the ice-chart-based degree of ice ridging and the automated results varied monthly, being 83, 63 and 81 % in January, February and March 2013, respectively. The correspondence between the degree of ice ridging reported in the ice charts and the actual ridge density was validated with data collected during a field campaign in March 2011. In principle the method can be applied to the seasonal sea ice regime in the Arctic Ocean.
    Type of Medium: Online Resource
    ISSN: 1994-0424
    URL: Article
    DOI: 10.5194/tc-12-343-2018
    Language: English
    Publisher: Copernicus GmbH
    Publication Date: 2018
    detail.hit.zdb_id: 2393169-3
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  • 6
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    Wearable Devices Combined with Artificial Intelligence—A Future Technology for Atrial Fibrillation Detection?
    MDPI AG ; 2022
    In:  Sensors Vol. 22, No. 22 ( 2022-11-08), p. 8588-
    Details
    In: Sensors, MDPI AG, Vol. 22, No. 22 ( 2022-11-08), p. 8588-
    Abstract: Atrial fibrillation (AF) is the most common cardiac arrhythmia in the world. The arrhythmia and methods developed to cure it have been studied for several decades. However, professionals worldwide are still working to improve treatment quality. One novel technology that can be useful is a wearable device. The two most used recordings from these devices are photoplethysmogram (PPG) and electrocardiogram (ECG) signals. As the price lowers, these devices will become significant technology to increase sensitivity, for monitoring and for treatment quality support. This is important as AF can be challenging to detect in advance, especially during home monitoring. Modern artificial intelligence (AI) has the potential to respond to this challenge. AI has already achieved state of the art results in many applications, including bioengineering. In this perspective, we discuss wearable devices combined with AI for AF detection, an approach that enables a new era of possibilities for the future.
    Type of Medium: Online Resource
    ISSN: 1424-8220
    URL: Article
    DOI: 10.3390/s22228588
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2052857-7
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  • 7
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    Correction: Mäkynen et al. Wearable Devices Combined with Artificial Intelligence—A Future Technology for Atrial Fibrillation Detection? Sensors 2022, 22, 8588
    MDPI AG ; 2023
    In:  Sensors Vol. 23, No. 18 ( 2023-09-19), p. 7972-
    Details
    In: Sensors, MDPI AG, Vol. 23, No. 18 ( 2023-09-19), p. 7972-
    Abstract: The authors wish to add two authors to the original paper [...]
    Type of Medium: Online Resource
    ISSN: 1424-8220
    URL: Article
    DOI: 10.3390/s23187972
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
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  • 8
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    Online Resource
    On the accuracy of thin-ice thickness retrieval using MODIS thermal imagery over Arctic first-year ice
    International Glaciological Society ; 2013
    In:  Annals of Glaciology Vol. 54, No. 62 ( 2013), p. 87-96
    Details
    In: Annals of Glaciology, International Glaciological Society, Vol. 54, No. 62 ( 2013), p. 87-96
    Abstract: We have studied the accuracy of ice thickness ( h i) retrieval based on night-time MODIS (Moderate Resolution Imaging Spectroradiometer) ice surface temperature ( T s) images and HIRLAM (High Resolution Limited Area Model) weather forcing data from the Arctic. The study area is the Kara Sea and eastern part of the Barents Sea, and the study period spans November-April 2008–11 with 199 hi charts. For cloud masking of the MODIS data we had to use manual methods in order to improve detection of thin clouds and ice fog. The accuracy analysis of the retrieved h i was conducted with different methods, taking into account the inaccuracy of the HIRLAM weather forcing data. Maximum reliable hi under different air-temperature and wind-speed ranges was 35–50 cm under typical weather conditions (air temperature 〈 –20cC, wind speed 〈 5ms–1) present in the MODIS data. The accuracy is best for the 15–30 cm thickness range, ∼38%. The largest hi uncertainty comes from air temperature data. Our ice-thickness limits are more conservative than those in previous studies where numerical weather prediction model data were not used in the hi retrieval. Our study gives new detailed insight into the capability of T s-based h i retrieval in the Arctic marginal seas during freeze-up and wintertime, and should also benefit work where MODIS h i charts are used.
    Type of Medium: Online Resource
    ISSN: 0260-3055 , 1727-5644
    URL: Article
    DOI: 10.3189/2013AoG62A166
    Language: English
    Publisher: International Glaciological Society
    Publication Date: 2013
    detail.hit.zdb_id: 2122400-6
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  • 9
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    Editorial for the Special Issue “Combining Different Data Sources for Environmental and Operational Satellite Monitoring of Sea Ice Conditions”
    MDPI AG ; 2020
    In:  Remote Sensing Vol. 12, No. 4 ( 2020-02-12), p. 606-
    Details
    In: Remote Sensing, MDPI AG, Vol. 12, No. 4 ( 2020-02-12), p. 606-
    Abstract: Satellite remote sensing is an important tool for continuous monitoring of sea ice covered ocean regions and spatial and temporal variations of their geophysical characteristics [...]
    Type of Medium: Online Resource
    ISSN: 2072-4292
    URL: Article
    DOI: 10.3390/rs12040606
    Language: English
    Publisher: MDPI AG
    Publication Date: 2020
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  • 10
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    MODIS Sea Ice Thickness and Open Water–Sea Ice Charts over the Barents and Kara Seas for Development and Validation of Sea Ice Products from Microwave Sensor Data
    MDPI AG ; 2017
    In:  Remote Sensing Vol. 9, No. 12 ( 2017-12-16), p. 1324-
    Details
    In: Remote Sensing, MDPI AG, Vol. 9, No. 12 ( 2017-12-16), p. 1324-
    Abstract: We have developed algorithms and procedures for calculating daily sea ice thickness (SIT) and open water–sea ice (OWSI) charts, based on the Moderate Resolution Imaging Spectroradiometer (MODIS), ice surface temperature (IST) (night-time only), and reflectance ( R ) swath data, respectively. The resolution of the SIT chart is 1 km and that of the OWSI chart is 250 m. The charts are targeted to be used in development and validation of sea ice products from microwave sensor data. We improve the original MODIS cloud masks for the IST and R data, with a focus on identifying larger cloud-free areas in the data. The SIT estimation from the MODIS IST swath data follows previous studies. The daily SIT chart is composed from available swath charts by assigning daily median SIT to a pixel. The OWSI classification is simply conducted by a fixed threshold for the MODIS band 1 R . This was based on manually selected R data for various ice types in late winter, early melt, and advanced melt conditions. The composition procedures for the daily SIT and OWSI charts somewhat compensates for errors due to the undetected clouds. The SIT and OWSI charts were compared against manual ice charts by Arctic and Antarctic Research Institute in Russia and by Norwegian Meteorological Institute, respectively, and on average, a good relationship between the charts was found. Pixel-wise comparison of the SIT and OWSI charts showed very good agreement in open water vs. sea ice classification, which gives further confidence on the reliability of our algorithms. We also demonstrate usage of the MODIS OWSI and SIT charts for validation of sea ice concentration charts based on the SENTINEL-1 SAR and AMSR2 radiometer data and two different algorithms.
    Type of Medium: Online Resource
    ISSN: 2072-4292
    URL: Article
    DOI: 10.3390/rs9121324
    Language: English
    Publisher: MDPI AG
    Publication Date: 2017
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