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

Mäkynen, M.; Karvonen, Juha

doi:10.3390/rs9121324

Cited by 14 publications

(11 citation statements)

References 57 publications

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“…The sole use of thermal infrared satellite data to analyze wintertime polynya (i.e., thin-ice and open water areas) dynamics has in the past been limited to case studies and/or single winter seasons (Adams et al, 2013;Aulicino et al, 2014;Drucker et al, 2003;Mäkynen & Karvonen, 2017;Willmes et al, 2010, 2011, andothers), as the high amount of cloud cover in the polar regions and inherent problems with the use of thermal infrared (i.e., optical) data are difficult to overcome. Scott et al (2014) presented a study on assessing the TIT for operational data assimilation using both MODIS thermal infrared data as well as AMSR-E passive microwave data along the Labrador Coast, northeastern Canada.…”

Section: Introductionmentioning

confidence: 99%

Retrieval of Wintertime Sea Ice Production in Arctic Polynyas Using Thermal Infrared and Passive Microwave Remote Sensing Data

et al. 2019

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Precise knowledge of wintertime sea ice production in Arctic polynyas is not only required to enhance our understanding of atmosphere‐sea ice‐ocean interactions but also to verify frequently utilized climate and ocean models. Here, a high‐resolution (2‐km) Moderate Resolution Imaging Spectroradiometer (MODIS) thermal infrared satellite data set featuring spatial and temporal characteristics of 17 Arctic polynya regions for the winter seasons 2002/2003 to 2017/2018 is directly compared to an akin low‐resolution Advanced Microwave Scanning Radiometer‐EOS (AMSR‐E) passive microwave data set for 2002/2003 to 2010/2011. The MODIS data set is purely based on a 1‐D energy‐balance model, where thin‐ice thicknesses (≤ 20 cm) are directly derived from ice‐surface temperature swath data and European Centre for Medium‐Range Weather Forecasts Re‐Analysis‐Interim atmospheric reanalysis data on a quasi‐daily basis. Thin‐ice thicknesses in the AMSR‐E data set are derived empirically. Important polynya properties such as areal extent and potential thermodynamic ice production can be estimated from both pan‐Arctic data sets. Although independently derived, our results show that both data sets feature quite similar spatial and temporal variations of polynya area (POLA) and ice production (IP), which suggests a high reliability. The average POLA (average accumulated IP) for all Arctic polynyas combined derived from both MODIS and AMSR‐E are 1.99×105 km2 (1.34×103 km3) and 2.29×105 km2 (1.31×103 km3), respectively. Narrow polynyas in areas such as the Canadian Arctic Archipelago are notably better resolved by MODIS. Analysis of 16 winter seasons provides an evaluation of long‐term trends in POLA and IP, revealing the significant increase of ice formation in polynyas along the Siberian coast.

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Section: Introductionmentioning

confidence: 99%

Retrieval of Wintertime Sea Ice Production in Arctic Polynyas Using Thermal Infrared and Passive Microwave Remote Sensing Data

et al. 2019

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“…In our study we use the Russian Arctic-Antarctic Research Institute (AARI) ice charts, Ocean Reanalysis System 5 (ORAS5), Pan-Arctic Ice-Ocean Modeling and Assimilation System (PIOMAS), and TOPAZ4 ice model ocean-sea ice reanalyses SIT data, CS2SMOS SIT data (Ricker et al, 2017), and MODIS daily SIT charts (Makynen and Karvonen, 2017a) as reference data. They are used to evaluate the performance of our CS2-SAR SIT estimation method.…”

Section: Reference Datamentioning

confidence: 99%

“…Unfortunately, this method is restricted by cloud cover and quality of cloud masking in polar conditions (Frey at al., 2008). A daily MODIS SIT chart combined from swath SIT charts, which mitigates the cloud problem, has been developed in Makynen and Karvonen (2017a). In addition, daily cloud-cover-corrected MODIS SIT composites for polynya monitoring in the Arctic and Antarctic have been produced utilizing several days of swath data (Paul et al, 2015;Preußer et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

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Kara and Barents sea ice thickness estimation based on CryoSat-2 radar altimeter and Sentinel-1 dual-polarized synthetic aperture radar

et al. 2022

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Abstract. We present a method to combine CryoSat-2 (CS2) radar altimeter and Sentinel-1 synthetic aperture radar (SAR) data to obtain sea ice thickness (SIT) estimates for the Barents and Kara seas. From the viewpoint of tactical navigation, along-track altimeter SIT estimates are sparse, and the goal of our study is to develop a method to interpolate altimeter SIT measurements between CS2 ground tracks. The SIT estimation method developed here is based on the interpolation of CS2 SIT utilizing SAR segmentation and segmentwise SAR texture features. The SIT results are compared to SIT data derived from the AARI ice charts; to ORAS5, PIOMAS and TOPAZ4 ocean–sea ice data assimilation system reanalyses; to combined CS2 and Soil Moisture and Ocean Salinity (SMOS) radiometer weekly SIT (CS2SMOS SIT) charts; and to the daily MODIS (Moderate Resolution Imaging Spectroradiometer) SIT chart. We studied two approaches: CS2 directly interpolated to SAR segments and CS2 SIT interpolated to SAR segments with mapping of the CS2 SIT distributions to correspond to SIT distribution of the PIOMAS ice model. Our approaches yield larger spatial coverage and better accuracy compared to SIT estimates based on either CS2 or SAR data alone. The agreement with modelled SIT is better than with the CS2SMOS SIT. The average differences when compared to ice models and the AARI ice chart SIT were typically tens of centimetres, and there was a significant positive bias when compared to the AARI SIT (on average 27 cm) and a similar bias (24 cm) when compared to the CS2SMOS SIT. Our results are directly applicable to the future CRISTAL mission and Copernicus programme SAR missions.

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“…From time series of ice drift estimates it is possible to derive the static ice areas which can then be interpreted as LFI, assuming the time series of ice drift at a certain location is long enough. Also SAR interferometry can be used for LFI detection (Meyer et al, 2011;Marbouti et al, 2017), as the phase difference is random for drift ice and coherent for the static ice fields. However, the availability of Single Look Complex (SLC) SAR data required for SAR interferometry is currently restricted, and thus methods based on SAR interferometry are not yet suitable for LFI monitoring in a large spatial scale.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Arctic land-fast ice cover based on dual-polarized Sentinel-1 SAR imagery

Karvonen¹

2018

The Cryosphere

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Here a method for estimating the land-fast ice (LFI) extent from dual-polarized Sentinel-1 SAR mosaics of an Arctic study area over the Kara and Barents seas is presented. The method is based on temporal cross-correlation between adjacent daily SAR mosaics. The results are compared to the LFI of the Russian Arctic and Antarctic Research Institute (AARI) ice charts. Two versions of the method were studied: in the first version (FMI-A) the overall performance was optimized, and in the second version (FMI-B) the target was a low LFI misdetection rate. FMI-A detected over 73 % of the AARI ice chart LFI, and FMI-B a little over 50 % of the AARI ice chart LFI. During the winter months the detection rates were higher than during the melt-down season for both the studied algorithm versions. An LFI time series covering the time period from October 2015 to the end of August 2017 computed using the proposed methodology is provided on the FMI ftp server. The time series will be extended twice annually.

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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

Cited by 14 publications

References 57 publications

Retrieval of Wintertime Sea Ice Production in Arctic Polynyas Using Thermal Infrared and Passive Microwave Remote Sensing Data

Retrieval of Wintertime Sea Ice Production in Arctic Polynyas Using Thermal Infrared and Passive Microwave Remote Sensing Data

Kara and Barents sea ice thickness estimation based on CryoSat-2 radar altimeter and Sentinel-1 dual-polarized synthetic aperture radar

Estimation of Arctic land-fast ice cover based on dual-polarized Sentinel-1 SAR imagery

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