Thin Ice Detection in the Barents and Kara Seas Using AMSR2 High-Frequency Radiometer Data

We have developed an algorithm for thin ice detection under winter conditions using the Advanced Microwave Scanning Radiometer 2 (AMSR2) radiometer high-frequency brightness temperature (36 and 89 GHz) L1R swath data, and a method to combine thin ice swath charts to a more reliable daily thin ice chart. Moderate Resolution Imaging Spectroradiometer (MODIS) ice thickness swath charts were used as reference data for the algorithm development. The algorithm is based on the classification of 36-GHz polarization ratio (PR36) and H-polarization 89–36-GHz gradient ratio (GR3689H) signatures with linear discriminant analysis. We applied an atmospheric correction to the AMSR2 L1R data following established sea ice concentration (SIC) retrieval algorithms. The PR36 and GR8936H signatures were adjusted to a constant air temperature (<inline-formula> <tex-math notation="LaTeX">$T_{a}$ </tex-math></inline-formula>) before the thin ice detection using an empirical relationship between them and <inline-formula> <tex-math notation="LaTeX">$T_{a}$ </tex-math></inline-formula>. The maximum thickness of detected thin ice was estimated to be 20 cm. The thin ice detection with the L1R data is conducted only when SIC <inline-formula> <tex-math notation="LaTeX">$\ge70$ </tex-math></inline-formula>% and <inline-formula> <tex-math notation="LaTeX">$T_{a}\le -5\,\,^\circ \text{C}$ </tex-math></inline-formula> to limit conditions where thick ice may be erroneously detected as thin ice. The thin ice detection algorithm was developed for the Barents and Kara Seas, but it should also be applicable for other Arctic marginal ice zones (MIZs). The daily thin ice chart was validated using an independent set of MODIS daily ice thickness charts. The average probability for misclassification of thick ice as thin ice was 10% and 32% for vice versa. We demonstrate the use of the daily thin ice chart for monitoring the thin ice fraction in the Barents and Kara Seas.

[1]  Matthias Drusch,et al.  SMOS-derived thin sea ice thickness: algorithm baseline, product specifications and initial verification , 2013 .

[2]  Lars Kaleschke,et al.  Surface Emissivity of Arctic Sea Ice at AMSU Window Frequencies , 2008, IEEE Transactions on Geoscience and Remote Sensing.

[3]  Nizy Mathew,et al.  Surface Emissivity of the Arctic Sea Ice at AMSR-E Frequencies , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[4]  Takashi Maeda,et al.  GCOM-W1 AMSR2 Level 1R Product: Dataset of Brightness Temperature Modified Using the Antenna Pattern Matching Technique , 2016, IEEE Transactions on Geoscience and Remote Sensing.

[5]  Takeshi Tamura,et al.  Improved mapping of sea ice production in the Arctic Ocean using AMSR‐E thin ice thickness algorithm , 2014 .

[6]  Mikko Lensu,et al.  Estimating the Speed of Ice-Going Ships by Integrating SAR Imagery and Ship Data from an Automatic Identification System , 2018, Remote. Sens..

[7]  Sascha Willmes,et al.  Circumpolar polynya regions and ice production in the Arctic: results from MODIS thermal infrared imagery from 2002/2003 to 2014/2015 with a regional focus on the Laptev Sea , 2016 .

[8]  D. Ruppert The Elements of Statistical Learning: Data Mining, Inference, and Prediction , 2004 .

[9]  Richard A. Frey,et al.  Cloud Detection with MODIS. Part I: Improvements in the MODIS Cloud Mask for Collection 5 , 2008 .

[10]  S. Kern,et al.  Inter-comparison and evaluation of sea ice algorithms: towards further identification of challenges and optimal approach using passive microwave observations , 2015 .

[11]  Bin Cheng,et al.  On the accuracy of thin-ice thickness retrieval using MODIS thermal imagery over Arctic first-year ice , 2013, Annals of Glaciology.

[12]  Lars Kaleschke,et al.  Polynya Signature Simulation Method polynya area in comparison to AMSR-E 89GHz sea-ice concentrations in the Ross Sea and off the Adélie Coast, Antarctica, for 2002–05: first results , 2007, Annals of Glaciology.

[13]  Georg Heygster,et al.  Improved determination of the sea ice edge with SSM/I data for small-scale analyses , 1998, IEEE Trans. Geosci. Remote. Sens..

[14]  Son V. Nghiem,et al.  The role of snow on microwave emission and scattering over first-year sea ice , 1998, IEEE Trans. Geosci. Remote. Sens..

[15]  Marko Mäkynen,et al.  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 , 2017, Remote. Sens..

[16]  Lars Kaleschke,et al.  Impact of surface conditions on thin sea ice concentration estimate from passive microwave observations , 2012 .

[17]  Georg Heygster,et al.  Atmospheric Correction of Sea Ice Concentration Retrieval for 89 GHz AMSR-E Observations , 2018, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[18]  Wilford F. Weeks,et al.  On sea ice , 2010 .

[19]  D. Rothrock,et al.  Thin ice thickness from satellite thermal imagery , 1996 .

[20]  Sohey Nihashi,et al.  Estimation of Thin-Ice Thickness and Discrimination of Ice Type From AMSR-E Passive Microwave Data , 2019, IEEE Transactions on Geoscience and Remote Sensing.

[21]  A. Fung Microwave Scattering and Emission Models and their Applications , 1994 .

[22]  Marko Mäkynen,et al.  Thin Ice Detection in the Barents and Kara Seas With AMSR-E and SSMIS Radiometer Data , 2015, IEEE Trans. Geosci. Remote. Sens..

[23]  Sascha Willmes,et al.  Spatial Feature Reconstruction of Cloud-Covered Areas in Daily MODIS Composites , 2015, Remote. Sens..

[24]  David G. Barber,et al.  Investigations of newly formed sea ice in the Cape Bathurst polynya: 2. Microwave emission , 2007 .

[25]  T. Tamura,et al.  Mapping of sea ice production in the Arctic coastal polynyas , 2011 .

[26]  J. Thepaut,et al.  The ERA‐Interim reanalysis: configuration and performance of the data assimilation system , 2011 .

[27]  Mohammed Shokr,et al.  Microwave Emission Observations from Artificial Thin Sea Ice: The Ice-Tank Experiment , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[28]  T. Markus,et al.  A method to estimate subpixel‐scale coastal polynyas with satellite passive microwave data , 1995 .

[29]  Fumihiko Nishio,et al.  Thin sea ice thickness as inferred from passive microwave and in situ observations , 2008 .

[30]  Øystein Skagseth,et al.  Quantifying the Influence of Atlantic Heat on Barents Sea Ice Variability and Retreat , 2012 .

[31]  Naohiko Hirasawa,et al.  Estimation of Thin Ice Thickness and Detection of Fast Ice from SSM/I Data in the Antarctic Ocean , 2007 .

[32]  Sascha Willmes,et al.  Thin-ice dynamics and ice production in the Storfjorden polynya for winter seasons 2002/2003-2013/2014 using MODIS thermal infrared imagery , 2014 .

[33]  Rasmus T. Tonboe,et al.  Improved retrieval of sea ice total concentration from spaceborne passive microwave observations using numerical weather prediction model fields: An intercomparison of nine algorithms , 2006 .

[34]  Sascha Willmes,et al.  Improvement and Sensitivity Analysis of Thermal Thin-Ice Thickness Retrievals , 2013, IEEE Transactions on Geoscience and Remote Sensing.

[35]  Leif Toudal Pedersen,et al.  Experiences With an Optimal Estimation Algorithm for Surface and Atmospheric Parameter Retrieval From Passive Microwave Data in the Arctic , 2017, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[36]  Juha Karvonen,et al.  Modeled Sea Ice Thickness Enhanced by Remote Sensing Data , 2016 .

[37]  Sohey Nihashi,et al.  Global view of sea-ice production in polynyas and its linkage to dense/bottom water formation , 2016, Geoscience Letters.

[38]  F. Wentz A well‐calibrated ocean algorithm for special sensor microwave / imager , 1997 .

[39]  Sohey Nihashi,et al.  Sea-Ice Production in Antarctic Coastal Polynyas Estimated From AMSR2 Data and Its Validation Using AMSR-E and SSM/I-SSMIS Data , 2017, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[40]  Ron Kwok,et al.  Estimation of the thin ice thickness and heat flux for the Chukchi Sea Alaskan coast polynya from Special Sensor Microwave/Imager data, 1990–2001 , 2004 .

[41]  Niels Skou,et al.  SMOS sea ice product: operational application and validation in the Barents Sea marginal ice zone , 2016 .

[42]  P. Gloersen,et al.  Passive Microwave Signatures of Sea Ice , 2013 .

[43]  Sascha Willmes,et al.  Long-term coastal-polynya dynamics in the southern Weddell Sea from MODIS thermal-infrared imagery , 2015 .

[44]  Thomas Meissner,et al.  AMSR Ocean Algorithm , 2000 .

[45]  S. Kern,et al.  The EUMETSAT sea ice concentration climate data record , 2016 .

[46]  Sohey Nihashi,et al.  Circumpolar Mapping of Antarctic Coastal Polynyas and Landfast Sea Ice: Relationship and Variability , 2015 .

[47]  Jeffrey R. Key,et al.  Less winter cloud aids summer 2013 Arctic sea ice return from 2012 minimum , 2014 .

[48]  Dorothy K. Hall,et al.  MODIS Sea Ice Products User Guide to Collection 5 , 2007 .

[49]  Natalia Ivanova,et al.  Response of passive microwave sea ice concentration algorithms to thin ice , 2014, 2014 IEEE Geoscience and Remote Sensing Symposium.

[50]  Ron Kwok,et al.  Ross Sea polynyas: Response of ice concentration retrievals to large areas of thin ice , 2007 .