Validation of an AMSR2 Thin-Ice Thickness Algorithm for Global Sea-Ice-Covered Oceans Using Satellite and In Situ Observations
The detection of thin-ice thickness using satellite microwave radiometers is a strong tool for estimating sea-ice production in coastal polynyas, which leads to dense water formation driving ocean thermohaline circulation. Thin-ice areas are classified into two ice types: active frazil, comprising f...
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MDPI AG
2025-01-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/1/171 |
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| author | Kazuki Nakata Misako Kachi Rigen Shimada Eri Yoshizawa Masato Ito Kay I. Ohshima |
| author_facet | Kazuki Nakata Misako Kachi Rigen Shimada Eri Yoshizawa Masato Ito Kay I. Ohshima |
| author_sort | Kazuki Nakata |
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| description | The detection of thin-ice thickness using satellite microwave radiometers is a strong tool for estimating sea-ice production in coastal polynyas, which leads to dense water formation driving ocean thermohaline circulation. Thin-ice areas are classified into two ice types: active frazil, comprising frazil ice and open water, and thin solid ice, areas of relatively uniform thin ice. A thin-ice algorithm for AMSR-E has been developed to classify these two ice types and estimate ice thickness of <20 cm. In this study, we validate the applicability of the algorithm to the successor, AMSR2, using validation data of ice types identified from Sentinel-1 and ice thickness derived from MODIS. The validation results show an ice-type misclassification rate of ~3% and mean absolute errors in ice thickness of 2.0 cm and 5.0 cm for active frazil and thin solid ice, respectively. These values are similar to those for AMSR-E, indicating that the thin-ice algorithm can be applied to AMSR2. Further validations with the moored ADCP backscattering data capturing underwater frazil ice signals demonstrate that the algorithm can accurately distinguish between two ice types and effectively detect deep-penetrating frazil ice. The AMSR2 thin-ice thickness data has been released as a JAXA research product. |
| format | Article |
| id | doaj-art-9eb9f55979a544a0b70bddab2b0e85aa |
| institution | Kabale University |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-9eb9f55979a544a0b70bddab2b0e85aa2025-01-10T13:20:28ZengMDPI AGRemote Sensing2072-42922025-01-0117117110.3390/rs17010171Validation of an AMSR2 Thin-Ice Thickness Algorithm for Global Sea-Ice-Covered Oceans Using Satellite and In Situ ObservationsKazuki Nakata0Misako Kachi1Rigen Shimada2Eri Yoshizawa3Masato Ito4Kay I. Ohshima5Earth Observation Research Center, Japan Aerospace Exploration Agency, Tsukuba 305-8505, JapanEarth Observation Research Center, Japan Aerospace Exploration Agency, Tsukuba 305-8505, JapanEarth Observation Research Center, Japan Aerospace Exploration Agency, Tsukuba 305-8505, JapanEarth Observation Research Center, Japan Aerospace Exploration Agency, Tsukuba 305-8505, JapanNational Institute of Polar Research, Tachikawa 190-8518, JapanInstitute of Low Temperature Science, Hokkaido University, Sapporo 060-0819, JapanThe detection of thin-ice thickness using satellite microwave radiometers is a strong tool for estimating sea-ice production in coastal polynyas, which leads to dense water formation driving ocean thermohaline circulation. Thin-ice areas are classified into two ice types: active frazil, comprising frazil ice and open water, and thin solid ice, areas of relatively uniform thin ice. A thin-ice algorithm for AMSR-E has been developed to classify these two ice types and estimate ice thickness of <20 cm. In this study, we validate the applicability of the algorithm to the successor, AMSR2, using validation data of ice types identified from Sentinel-1 and ice thickness derived from MODIS. The validation results show an ice-type misclassification rate of ~3% and mean absolute errors in ice thickness of 2.0 cm and 5.0 cm for active frazil and thin solid ice, respectively. These values are similar to those for AMSR-E, indicating that the thin-ice algorithm can be applied to AMSR2. Further validations with the moored ADCP backscattering data capturing underwater frazil ice signals demonstrate that the algorithm can accurately distinguish between two ice types and effectively detect deep-penetrating frazil ice. The AMSR2 thin-ice thickness data has been released as a JAXA research product.https://www.mdpi.com/2072-4292/17/1/171thin-ice thicknessAMSR2polynyaice typefrazil ice |
| spellingShingle | Kazuki Nakata Misako Kachi Rigen Shimada Eri Yoshizawa Masato Ito Kay I. Ohshima Validation of an AMSR2 Thin-Ice Thickness Algorithm for Global Sea-Ice-Covered Oceans Using Satellite and In Situ Observations Remote Sensing thin-ice thickness AMSR2 polynya ice type frazil ice |
| title | Validation of an AMSR2 Thin-Ice Thickness Algorithm for Global Sea-Ice-Covered Oceans Using Satellite and In Situ Observations |
| title_full | Validation of an AMSR2 Thin-Ice Thickness Algorithm for Global Sea-Ice-Covered Oceans Using Satellite and In Situ Observations |
| title_fullStr | Validation of an AMSR2 Thin-Ice Thickness Algorithm for Global Sea-Ice-Covered Oceans Using Satellite and In Situ Observations |
| title_full_unstemmed | Validation of an AMSR2 Thin-Ice Thickness Algorithm for Global Sea-Ice-Covered Oceans Using Satellite and In Situ Observations |
| title_short | Validation of an AMSR2 Thin-Ice Thickness Algorithm for Global Sea-Ice-Covered Oceans Using Satellite and In Situ Observations |
| title_sort | validation of an amsr2 thin ice thickness algorithm for global sea ice covered oceans using satellite and in situ observations |
| topic | thin-ice thickness AMSR2 polynya ice type frazil ice |
| url | https://www.mdpi.com/2072-4292/17/1/171 |
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