Impact of surface conditions on thin sea ice concentration estimate from passive microwave observations Mohammed Shokr a, ⁎, Lars Kaleschke b a Science and Technology Branch, Environment Canada, 4905 Dufferin St., Toronto, Ont. Canada, M3H 5T4 b Institute of Oceanography, University of Hamburg, Bundesstrasse 53, D-20146 Hamburg, Germany abstract article info Article history: Received 6 July 2011 Received in revised form 5 January 2012 Accepted 8 January 2012 Available online 17 February 2012 Keywords: Sea ice concentration Passive microwave Ice algorithms Thin ice Snow on sea ice Ice concentration retrieved from spaceborne passive microwave observations is a prime input to operational sea ice monitoring programs, numerical weather prediction and global climate models. However, it is usually underestimated by existing algorithms due to surface conditions, especially in case of young ice types. Eval- uation of those algorithms identifies errors in concentration estimates but does not necessarily link them to the adverse surface conditions. The present study is an attempt to establish those links for young ice (b 25 cm) thick. It uses measurements of microwave emission from artificially grown sea ice in an outdoor tank and calculates ice concentration using five established algorithms: NT, Bootstrap (BSA), NT2, ASI and ECICE. Since the actual concentration is known (100%), then any deviation from this value is considered an error and can be linked to the observed surface conditions, which are usually caused by weather events. Those conditions were acquired on hourly or daily basis. Results identify key conditions that lead to under- estimation of ice concentration. They include surface refreezing, slush, snow settling following fresh snowfall, and falling precipitation in different forms. The study shows also that NT and NT2 are most affected by surface processes while BSA performs better. ASI is much less affected because it uses the high frequency channel (e.g. SSM/I 85 GHz), which is sensitive only to processes within the top snow layer. ECICE, with its probabi- listic and ensemble approach shows also good results under most surface conditions. Dry or wet snow does not lead to significant difference in ice concentration estimate. The study also aims at validation of ECICE. © 2012 Elsevier Inc. All rights reserved. 1. Introduction One of the most important sea ice parameters from marine navi- gational, weather prediction and climatic viewpoints is ice concentra- tion. Passive microwave sensors are most suitable for retrieving this parameter because of their ability to penetrate both cloud cover and polar darkness. Commonly used algorithms for sea ice concentra- tion retrieval include NASA Team (NT) (Cavalieri et al., 1984), The Bootstrap algorithm (BSA) (Comiso & Sullivan, 1986), Enhanced NASA Team (NT2) (Markus & Cavalieri, 2000), and ARTIST Sea Ice (ASI) (Kaleschke et al., 2001). A more recent algorithm, called Environment Canada's Ice Concentration Extractor (ECICE), has been developed to determine total and ice type concentration (Shokr et al., 2008). All algorithms employ brightness temperature (Tb) for a given frequency f and polarization p (can be horizontal “h” or vertical “v”) and/or the derived parameters of polarization ratio (PR), and the gradient ratio (GR) which are defined as follows: PR f ¼ Tb fv -Tb fh Tb fv þ Tb fh and GR f 1 pf 2 p ¼ Tb f 1 p -Tb f 2 p Tb f 1 p þ Tb f 2 p Retrieval of concentration of thin ice, which is defined in this study as ice less than 25 cm thick, from microwave data is particularly difficult for two reasons. First, large fluctuations of Tb and hence PR usually characterize the transition from open water to ice as well as the unsettled thin ice surface conditions especially during snowfall or freezing rain (Shokr et al., 2009). Since lower frequency channels (e.g. 19 GHz) are associated with larger penetration depth and hence larger fluctuations, then their use (which is the case in most algorithms) will cause wrong estimate of ice concentration. Secondly, Microwave emission from snow-covered sea ice is mainly affected by the subsurface composition and properties rather than the bulk ice properties. Consequently, the retrieved ice concen- tration may not be correct if surface conditions produce uncharacter- istic values of Tb. This is more likely to happen in case of thin ice because it exhibits a wider range of surface properties and processes that varies significantly at small spatial and temporal scales. The rea- son for this variation is twofold: (1) the steep temperature gradient Remote Sensing of Environment 121 (2012) 36–50 ⁎ Corresponding author. Tel.: + 1 416 739 4906; fax: + 1 416 739 4221. E-mail address: mohammed.shokr@ec.gc.ca (M. Shokr). 0034-4257/$ – see front matter © 2012 Elsevier Inc. All rights reserved. doi:10.1016/j.rse.2012.01.005 Contents lists available at SciVerse ScienceDirect Remote Sensing of Environment journal homepage: www.elsevier.com/locate/rse