A cloud screening method initially generated to mask cloud contaminated pixels over the ocean in visible/infrared imagery, has been revised and adapted to detect clouds over Arctic regions with encouraging results. Although the method is quite successful in eliminating very cold clouds, it underestimates low level clouds. However, this does not appear to interfere with monitoring of ice related features such as leads or the ice edge in Advanced Very High Resolution Radiometer (AVHRR) scenes. The method uses: a multiple-band approach to produce signatures not readily available in single channel data, an edge detection/dilation technique to locate features in the clouds and to join isolated edges, and a polygon identification technique to remove noise in the form of isolated pixels and separate clear regions from cloud contaminated areas. The method has been tested over a limited set of data with consistent results. Initial evaluation of the usefulness of this cloud-detection algorithm in data-fusion experiments indicate a potential in locating areas in AVHRR data which are cloud contaminated and which could yield a far superior representation of the ice features if replaced with data from a different sensor such as the Special Sensor Microwave/lmager (SSM/I).

The temperature and albedo distributions of Arctic sea ice are calculated from images obtained from the AVHRR satellite sensor. The temperature estimate uses a split window correction incorporating regression coefficients appropriate for the arctic atmosphere. The albedo estimate is found assuming a clear and dry atmosphere. Both estimates are made with published correction techniques. Inherent errors due to the uncertainty of the atmospheric interference produced by humidity, aerosols, and diamond dust are judged to be 2–5°C in surface temperature and 0.10–0.20 in surface albedo. Cloudy regions are masked out manually using data from all five channels. The relationship between temperature and albedo is shown for a sample scene. A simple model of a surface composed of only cold, bright ice and warm, dark water is inadequate. Model calculations based on the surface energy balance allow us to relate albedo and temperature to ice thickness and snow-cover thickness and to further assess the accuracy of the surface estimates.

Under the Office of Naval Research-sponsored Arctic Leads Accelerated Research Initiative (ARI), a data set of Advanced Very High Resolution Radiometer (AVHRR) imagery covering the years 1988 through 1992 is being constructed. Relatively cloud-free imagery is selected from image hardcopies. Each image examined is subjectively ranked on the percentage of each sea or seas it covers, and the cloudiness of the image within each sea. The images are then logged in a spreadsheet. From the spreadsheet, about 20 images per month (for the year 1989) are ordered from the National Oceanic and Atmospheric Administration for processing. The image data are calibrated and mapped to one of two grids, which together cover most of the Arctic at 1 km per pixel. Care has been taken to match the grid and the projection to that of Special Sensor Microwave Imager (SSM/I) data distributed by the National Snow and Ice Data Center (NSIDC). The 1989 data set is complete at this time. Presently, data are distributed to the Remote Sensing Working Group of the ARI. NSIDC will distribute the data set to a wider audience at a later date.

An Ice Tracking System (ITS) is under development which will automatically extract ice kinematic information from time sequential imagery over Canadian waterways. NOAA AVHRR data are routinely collected at the Ice Centre Environment Canada (ICEC) and are a valuable data source for operational ice analysis and forecasting. For this study AVHRR data were collected between January and February 1992 over the Grand Banks of Newfoundland. The requirement for cloud-free data limited its use. Multiple images over short time intervals were available for a one to two day period, however these were followed by gaps of three to four days. On days when imagery was available synoptic conditions were always similar favouring a southeasterly ice drift. The east coast of Canada is a very difficult ice environment in which to extract accurate ice motion. The ice regime consists mainly of young ice types exhibiting very dynamic behaviour. To the NOAA sensor the ice cover can appear viscous and featureless. The inaccuracies in the extracted ice kinematic information was attributed to positional errors with the input data and the inability of the tracking algorithm to identify correctly the same features on time sequential images. Geocoding inaccuracies, a result of the systematic geocoding process, had an average error of 2.7 km. Algorithm inaccuracies, a result of incorrect matching, had an average error of 2.45 km.

Accurate co-location and geo-registration of AVHRR (Advanced Very High Resolution Radiometer) is necessary for most applications involving these data. To obtain the necessary accuracy, most of the current systems available for AVHRR image rectification require corrections to errors arising from several different sources. This paper presents a software package which is able to correct registration errors from all sources using effective roll, pitch, and yaw values determined from ground control point offsets. These effective values are used to establish a baseline registration against which other models are compared. Spacecraft and sampling geometry are revisited in order to systemize and better define the procedure of AVHRR geo-registration. This systematic approach has resulted in a modular software package which has a registration accuracy of 1 satellite pixel, and is easily modified and improved.