Estimation of Thin-Ice Thickness and Discrimination of Ice Type From AMSR-E Passive Microwave Data

Detection of thin-ice thickness with microwave radiometers, such as the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E), is very effective for the estimation of sea-ice production, which causes dense water driving ocean thermohaline circulation. In previous thin-ice thickness...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:IEEE transactions on geoscience and remote sensing 2019-01, Vol.57 (1), p.263-276
Hauptverfasser: Nakata, Kazuki, Ohshima, Kay I., Nihashi, Sohey
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext bestellen
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Detection of thin-ice thickness with microwave radiometers, such as the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E), is very effective for the estimation of sea-ice production, which causes dense water driving ocean thermohaline circulation. In previous thin-ice thickness algorithms, ice thickness is estimated by utilizing a negative correlation between ice thickness and polarization ratio (PR) of AMSR-E. However, in these thin-ice algorithms, the relationship has large dispersion. We consider that the problem is caused by not taking account of ice type. We classified thin-ice regions around Antarctica into two ice types: 1) active frazil, comprising frazil and open water and 2) thin solid ice, areas of the relatively uniform thin ice, using Moderate Resolution Imaging Spectroradiometer and Advanced Synthetic Aperture Radar data. For each ice type, we examined the relationship between the AMSR-E PR of 36 GHz and ice thickness, showing that the active frazil type has a much smaller thickness than the thin solid ice type for the same PR. The two ice types can be discriminated by a simple linear discriminant method in the plane of the PR and gradient ratio of AMSR-E, with the misclassification of 3%. From these results, we propose a new thin-ice algorithm. The two ice types are classified by the linear discriminant method, and then empirical equations are used to obtain the ice thickness for each ice type. This algorithm significantly improves the accuracy of the thin-ice thickness.
ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2018.2853590