An automated ice-mapping algorithm has been developed and evaluated using data from the GOES-13 imager. The approach includes cloud-free image compositing as well as image classification using spectral criteria. The algorithm uses an alternative snow index to the Normalized Difference Snow Index (NDSI). The GOES-13 imager does not have a 1.6 µm band, a requirement for NDSI; however, the newly proposed Mid-Infrared Sea and Lake Ice Index (MISI) incorporates the reflective component of the 3.9 µm or mid-infrared (MIR) band, which the GOES-13 imager does operate. Incorporating MISI into a sea or lake ice mapping algorithm allows for mapping of thin or broken ice with no snow cover (nilas, frazil ice) and thicker ice with snow cover to a degree of confidence that is comparable to other ice mapping products. The proposed index has been applied over the Great Lakes region and qualitatively compared to the Interactive Multi-sensor Snow and Ice Mapping System (IMS), the National Ice Center ice concentration maps and MODIS snow cover products. The application of MISI may open additional possibilities in climate research using historical GOES imagery. Furthermore, MISI may be used in addition to the current NDSI in ice identification to build more robust ice-mapping algorithms for the next generation GOES satellites.

The traditional method involved in the classification of surface types such as water, ice, and snow rely on thresholds values of spectral properties that are fixed. However, the use of daily fixed thresholds leaves a substantial number of either unclassified and/or misclassified ice and water pixels. In this study, a new dynamic threshold technique is proposed to identify and map lake ice cover in the imagery of GOES-I to P series satellites. In addition, dynamic threshold can be used as an alternative solution to Bidirectional Reflectance Distribution Function (BRDF) models. The technique has been applied using GOES-13 imager data over Lake Michigan, one of five of the Great Lakes. Nine scenes based on an hourly acquisition of a single day are used to visually sample water and ice pixels. A threshold for the visible (0.62 µm) and the reflective component of the mid-infrared (3.9 µm) is determined for each scene by the intersection of the probability distributions of the water and ice samples. The thresholds are used as decision thresholds in a binary test algorithm applied on a per-pixel basis. Both fixed threshold (single scene) and dynamic thresholds (multiple scenes) have been compared. Dynamic threshold shows a significant gain in classified pixels over fixed threshold. A preliminary quantitative assessment is introduced to evaluate the algorithm's performance using sensitivity and specificity testing. The classification results show a sensitivity of 98% when delineating thick ice and water and 87% when delineating thick/thin ice and water. Implementing a dynamic threshold, can be used in constructing ice maps in applications that benefit from high temporal resolution imagery.

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