Gray Level Co-occurrence Matrix textural analysis for temporal mapping of sea ice in Sentinel-1A SAR images

Sea ice is a critical component of the cryosphere and plays a role in the heat and moisture exchange processes between the ocean and atmosphere, thus regulating the global climate. With climate change, detailed monitoring of changes occurring in sea ice is necessary. Therefore, an analysis was conducted to evaluate the potential of using the Gray Level Co-occurrence Matrix (GLCM) texture analysis combined with the backscattering coefficient (σ°) of HH polarization in Sentinel-1A Synthetic Aperture Radar (SAR) images, interferometric imaging mode, for mapping sea ice in time series. Data processing was performed using cloud computing on the Google Earth Engine platform with routines written in JavaScript. To train the Random Forest (RF) classifier, samples of regions with open water and sea ice were obtained through visual interpretation of false-color SAR images from Sentinel-1B in the extra-wide swath imaging mode. The analysis demonstrated that training samples used in the RF classifier from a specific date can be applied to images from other dates within the freezing period, achieving accuracies ≥ 90% when using 64-bit grayscale quantization in GLCM combined with σ° data. However, when using only σ° data in the RF classifier, accuracies ≥ 93% were observed.