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Emissivities at frequencies from 5 to 94 GHz and backscatter at frequencies from 1 to 17 GHz were measured from sea ice in Fram Strait during the Marginal Ice Zone Experiment in June and July of 1983 and 1984. The ice observed was primarily multiyear; the remainder, first-year ice, was often deformed. Results from this active and passive microwave study include the description of the evolution of the sea ice during early summer and midsummer; the absorption properties of summer snow; the interrelationship between ice thickness and the state and thickness of snow; and the modulation of the microwave signature, especially at the highest frequencies, by the freezing of the upper few centimeters of the ice. INTRODUCTION Active and passive microwave remote sensing of sea ice offer the potential of obtaining synoptic data of large expanses of remote, ice-covered oceans under all weather conditions irrespective of the amount of solar illumination. This is of particular importance for Arctic applications where much of the polar ice canopy is under clouds or in darkness. Numerous late winter and spring experiments have concentrated on the ability to classify ice types, to detect scientifically interesting features, and to describe ice field kinematics and dynamics. Efforts also focused on a determination of optimum frequencies, polarizations, and incidence angles and on the development of algorithms for extracting geophysical parameters b:om sea ice imagery. Campbell et al. [19753, Ramseier and Lapp [1980], and Livingstone et al. [1981] conclude their studies by stating that many features, including ice types, ridges and roughness features, lead and polynya formations, and icebergs, have distinct signatures which are observed using active and passive microwave sensors. They also present the hypothesis that a combination of multifrequency, active and passive (microwave and millimeter wave) sensors is especially valuable for extracting information about the state of the ice. They present the hypothesis that emissivity and backscatter are influenced by different aspects of the sea ice structure and that the relationship between microwave frequency and penetration depth may be exploited robustly. The more limited experimentation by Gray et al. [1982], Onstott et al. [1982], Onstott and Gogineni [1985], Grenfell and Lohanick [1985], and Lohanick and Grenfell [1986] during the summer melt period illustrate the extreme difficulty in detecting and classifying sea ice features when surface conditions change rapidly. They concluded that use of microwave sensors to classify sea ice type and features unambiguously requires

An approach for identification of sea ice types in spaceborne synthetic aperture radar (SAR) image data is presented. The unsupervised classification approach involves cluster analysis for segmentation of the image data followed by cluster labeling based on previously defined look-up tables containing the expected backscatter signatures of different ice types measured by land-based scatterometer. The particular look-up table used for labeling a segmented image is selected based on the seasonal and meteorological conditions at the time of data acquisition. The extensive scatterometer observations and experience accumulated in field campaigns during the last 10 years were used to construct these look-up tables. These tables are expected to evolve as sea ice observations from the European ERS-1 SAR become available. This paper presents the classification approach, its expected performance, the dependence of this performance on radar system performance, and expected ice scattering characteristics. Results using both aircraft and simulated ERS-1 SAR data are presented. The results are compared to limited field ice property measurements and coincident passive microwave imagery. An algorithm based on this experimental approach has been implemented in the geophysical processor system at the Alaska SAR Facility for classification of sea ice data in ERS-1 C band SAR data. The importance of an integrated postlaunch program for validation and improvement of this approach is discussed. INTRODUCTIONThe derivation of valuable information on sea ice properties from radar imagery has increased steadily over the last decade [Carsey, 1989] These SAR studies of sea ice have examined imagery obtained from aircraft and spaceborne SAR systems, usually in combination with other sensors, in situ measurements of ice and snow conditions, and near-surface scatterometer and radiometer measurements. The radars have operated over a wide range of frequencies, incidence angles, and polarizations. During this decade, single-channel spaceborne S ARs are to be launched on the European ERS-1 in 1991, the Japanese ERS-1 in 1992, and the Canadian RADARSAT in 1994. These satellites will provide the first opportunities for the extensive spaceborne monitoring of the polar regions with a SAR since Seasat (which operated for 3• months in 1978). At the end of this decade, NASA has proposed to launch the EOS (Earth Observing System) SAR (a multifrequency, multipolarization SAR) as an integral component of the Mission to Planet Earth, a comprehensive suite of satellite instruments designed to examine global climate change, of which sea ice is a key and supposedly dramatic indicator.In response to these opportunities for examining the polar regions with high-resolution, all-weather spaceborne SARs, the Alaska SAR Facility (ASF) has been implemented at the University of Alaska, Fairbanks, to receive, process, and archive SAR data from these satellites and to generate sea ice geophysical products from the data [ This paper focuses on the ice classification algo...

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Active and passive microwave data sets acquired during the 1984 Marginal Ice Zone (MIZ) Experiment aircraft flights in the Fram Strait region are used to examine the effects of ice surface melt on microwave signatures and their resulting error in the calculation of sea ice concentration. Conditions examined with the active-passive data set include ice floes with moist and dry snow cover and both heavily ponded and ridged surfaces. Passive sensors on the NASA CV-990 aircraft included the aircraft electrically scanning microwave radiometer (ESMR) operating at 19.4 GHz and aircraft multichannel microwave radiometer (AMMR) operating at 10.7, 18.0, 21.0, and 37.0 GHz. Active microwave sensors flown on the Canadian Centre for Remote Sensing CV-580 aircraft included the Environmental Research Institute of Michigan synthetic aperture radar (SAR) operating at 1.2 and 9.4 GHz. Coincident AMMR and SAR measurements of individual floes identified in aerial photography are used to describe the effects of surface melt on the calculation of sea ice concentration, and in particular, the response of the passive microwave polarization and spectral gradient characteristics to different stages of surface melt. Although the onset and progression of summer melt are not uniform throughout the Arctic, the stages of summer melt observed in the MIZ are also observed on a large-scale in the central Arctic. This is demonstrated using Nimbus 7 SMMR data and Arctic Ocean buoy temperature data over one annual cycle. Finally, the potential of optimally combining both active and passive microwave data in an effort to ameliorate these surface melt effects during the summer months is also explored.