Principal Component Analysis of Arctic Sea Ice Using Multi-spectral Sentinel-2 and RADAR Sentinel-1 Data

Jullian Williams; Kiran Bhaganagar

doi:10.20944/preprints202406.0961.v1

Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Version 1 : Received: 13 June 2024 / Approved: 13 June 2024 / Online: 13 June 2024 (15:37:39 CEST)

The increasing concern regarding climate change continues to motivate research in the Arctic. Within the delicate cycle of the cryosphere, leads are an important kinematic feature that regulate heat balances in the Arctic. Therefore, it is necessary to quantify the genesis of leads over time to identify when and where changes are occurring. The use of a principal component analysis (PCA) is one such tool that is used to identify the characteristics of seasonal ice variability. The PCA not only identifies “normal” or “dominant” conditions or features, but also extract anomalous spatial features from an archive of long-term sequence of images.

Principal Component Analysis; sea ice; Sentinel-2; Sentinel-1; Beaufort Sea; Leads

Computer Science and Mathematics, Artificial Intelligence and Machine Learning

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Principal Component Analysis of Arctic Sea Ice Using Multi-spectral Sentinel-2 and RADAR Sentinel-1 Data

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