This paper presents a preliminary study on mapping sea ice patterns using 100-m ERS-1 synthetic aperture radar (SAR) imagery. The authors used gray-level co-occurrence matrices (GLCM) to quantitatively evaluate textural parameters and representations, aiming to determine the best parameters for mapping sea ice texture. They conducted experiments on the quantization levels of the image and the displacement and orientation values of the GLCM, examining the effects of textural descriptors such as entropy on different sea ice textures. The study found that a complete gray-level representation is not necessary for texture mapping, and an eight-level quantization representation is undesirable. The displacement factor in texture measurements is more important than orientation. Additionally, three GLCM implementations were developed and evaluated using a supervised Bayesian classifier on sea ice textural contexts. The best implementation was one that utilized a range of displacement values, effectively capturing both microtextures and macrotextures of sea ice. These findings define the optimal quantization, displacement, and orientation values for SAR sea ice texture analysis using GLCM.This paper presents a preliminary study on mapping sea ice patterns using 100-m ERS-1 synthetic aperture radar (SAR) imagery. The authors used gray-level co-occurrence matrices (GLCM) to quantitatively evaluate textural parameters and representations, aiming to determine the best parameters for mapping sea ice texture. They conducted experiments on the quantization levels of the image and the displacement and orientation values of the GLCM, examining the effects of textural descriptors such as entropy on different sea ice textures. The study found that a complete gray-level representation is not necessary for texture mapping, and an eight-level quantization representation is undesirable. The displacement factor in texture measurements is more important than orientation. Additionally, three GLCM implementations were developed and evaluated using a supervised Bayesian classifier on sea ice textural contexts. The best implementation was one that utilized a range of displacement values, effectively capturing both microtextures and macrotextures of sea ice. These findings define the optimal quantization, displacement, and orientation values for SAR sea ice texture analysis using GLCM.