On Thin Ice Identification with AMSR-E Radiometer Data
Mäkynen, Marko; Similä, Markku
Finnish Meteorological Institute, FINLAND
The ocean-atmosphere heat, momentum and gas exchanges are controlled by the sea ice thickness distribution in the polar oceans. Thin ice with a thickness of less than half a meter produces strong heat flux affecting the weather and salt flux contributing to deep water circulation in the polar oceans. For ship navigation in sea ice covered waters the identification of thin ice areas is essential. Thus, an operational remote sensing method for thin ice detection and thickness estimation is of importance for several purposes.
Estimation of thin ice thickness can be conducted using satellite thermal imagery (e.g. MODIS) based ice surface temperature together with atmospheric forcing data through ice surface heat balance equation. However, this requires cloud-free conditions, and thus, there may be long temporal gaps in the thickness chart coverage over a region of interest, e.g. a polynya. In addition, discriminating clear-sky from clouds is difficult in winter night-time conditions.
Passive microwave radiometer data from AMSR-E (36.5 and 89 GHz channels) sensor have been used to estimate thickness of thin ice up to 10-20 cm, e.g. (Tamura and Ohshima 2011). The spatial resolution of the radiometer thin ice thickness (hR) charts, e.g. 12.5 km with AMSR-E 36 GHz data, is much coarser than those from the thermal imagery, but daily Arctic and Antarctic coverage is possible. The ice thickness retrieval algorithms are linear or exponential regression equations between polarization ratio (PR) or V- to H-polarization ratio and AVHRR or MODIS thickness charts obtained by assuming snow-free sea ice. In the hR charts sea ice has been distinguished from open water using radiometer data based sea ice concentration (IC). The radiometer data provides rather indirect estimation of thin ice thickness through observed ice surface conditions; thin ice emission properties depends mainly on near surface salinity which on the average decreases with increasing ice thickness (Naoki et al. 2008). For the marginal ice zones and divergent ice areas it is not currently possible to determine whether the hR data represent really thin ice or alternatively a mixture of open water and thick ice. Part of the limitations in the thin ice thickness estimation can be attributed to the presence of snow or dense frost flower coverage (>60%) on the ice surface (Hwang et al. 2007, Nihashi et al. 2009). For thin ice detection only an algorithm called polynya signature simulation method (PSSM) has been developed (e.g. Hunewinkel et al. 1998). PSSM classifies radiometer data to open water, first-year ice, and thin ice (or low ice concentrations) with 5 km resolution at the best.
Here we will study thin ice (thickness up to 30 cm) detection over the Kara Sea and the eastern part of the Barents Sea using AMSR-E radiometer data and 199 MODIS thickness (hT) charts for three winters (Nov - Apr) in 2008-2011 (Mäkynen et al. 2013). Our dataset exhibits a large scatter between hT and radiometer data. The confidence intervals of any equation fitted to the data are large and, hence, the thickness estimates are uncertain. This opposite conclusion compared to many previous studies could be a study region related (Kara Sea here), but more likely due to much larger dataset than in any previous study including besides thin ice in polynyas also thin ice in the marginal ice zone and large thin ice areas from freeze-up period. The detection of thin ice is studied first with different AMSR-E PR and spectral gradient ratios (GR) and their combinations. Next, the best single parameter or combination of PR and GR for thin ice detection is selected. The effect of possible snow cover is studied by using hT charts retrieved assuming snow-free sea ice and a simple snow vs. ice thickness relationship developed in (Mäkynen et al. 2013). The PSSM algorithm is not applied here as we are interested to study thoroughly the capability of simple PR and GR threshold methods in the thin ice detection. Finally, an algorithm for operational thin ice detection is developed which uses only two ice thickness categories: thin ice (<20 cm) and thicker ice. When the decision of ice thickness class for a specific grid cell is made it depends on the previous ice class in the grid cell as well as the ice classes of neighboring grid cells in the previous and the current chart. Our purpose is to use resulting thin ice identification charts as an part of a multisensor (SAR, radiometer, MODIS) and a sea ice model based ice thickness algorithm for first-year ice (Similä et al. 2013).
T. Hunewinkel, T. Markus, and G. C. Heygster, "Improved determination of the sea ice edge with SSM/I data for small-scale analyses," IEEE Trans. Geosci. Remote Sens., 36(5), 1795-1808, 1998.
B. J. Hwang, J. K. Ehn, D. G. Barber, R. Galley, and T. C. Grenfell, "Investigations of newly formed sea ice in the Cape Bathurst polynya: 2. Microwave emission," J. Geophys. Res., 112, C05003, 2007.
M. Mäkynen, B. Cheng, and M. Similä, "On the accuracy of the thin ice thickness retrieval using MODIS thermal imagery over the Arctic first year ice," Ann. Glaciol., 54(62), 87-96, 2013.
K. Naoki, J. Ukita, F. Nishio, M. Nakayama, J. C. Comiso, and A. Gasiewski, "Thin sea ice thickness as inferred from passive microwave and in situ observations," J. Geophys. Res., 113, C02S16, 2008.
S. Nihashi, K. I. Ohshima, T. Tamura, Y. Fukamachi, and S. Saitoh, "Thickness and production of sea ice in the Okhotsk Sea coastal polynyas from AMSR-E", J. Geophys. Res., 114, C10025, 2009.
M. Similä,M. Mäkynen, B. Cheng, and E. Rinne, "Multisensor data and thermodynamic sea ice model based sea ice thickness chart with application to the Kara Sea", under review for Ann. Glaciol.
T. Tamura and K. I. Ohshima, "Mapping of sea ice production in the Arctic coastal polynyas," J. Geophys. Res., 116, C07030, 2011.