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An Assessment of Sea-Ice Thickness Along the Labrador Coast From AMSR-E and MODIS Data for Operational Data Assimilation.

Authors :
Scott, K. Andrea
Buehner, Mark
Carrieres, Tom
Source :
IEEE Transactions on Geoscience & Remote Sensing; May2014, Vol. 52 Issue 5, p2726-2737, 12p
Publication Year :
2014

Abstract

In this paper, sea-ice thickness values are calculated along the Labrador coast using data from two sensors representative of those available for operational data assimilation. Data from the first sensor, the Moderate Resolution Imaging Spectroradiometer (MODIS), are used to calculate the ice thickness using a heat balance equation. Relationships between the MODIS ice thickness and polarization ratio from the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) are used to calculate the thickness of thin ice (less than 0.2 m) from the AMSR-E data. This is done for each frequency on the AMSR-E sensor in the range of 6.9-36.5 GHz. Through comparison with data from ice charts, it is found that the errors are lowest for thickness values calculated from low-frequency AMSR-E data. The accuracies of the ice thickness from MODIS, AMSR-E, operational ice charts, and two moored upward looking sonars are further assessed using the triple collocation method. It is found that the error associated with ice thickness from AMSR-E is the lowest and the error associated with ice thickness from MODIS is the highest. While the MODIS data represent the small-scale variability of the sea-ice thickness better than the AMSR-E data, the MODIS data can produce spurious values of ice thickness due to unmasked clouds. To use ice thickness from MODIS in an automated algorithm, quality control would need to be applied to the MODIS data to remove unmasked clouds which lead to spurious values of thick ice. The errors calculated for the ice thickness from AMSR-E, which are calculated based on a relationship calibrated with MODIS ice thickness from a clear-sky day, indicate that these data would be useful for operational data assimilation. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
01962892
Volume :
52
Issue :
5
Database :
Complementary Index
Journal :
IEEE Transactions on Geoscience & Remote Sensing
Publication Type :
Academic Journal
Accession number :
101186670
Full Text :
https://doi.org/10.1109/TGRS.2013.2265091