Version 1
: Received: 13 June 2024 / Approved: 13 June 2024 / Online: 13 June 2024 (15:37:39 CEST)
How to cite:
Williams, J.; Bhaganagar, K. Principal Component Analysis of Arctic Sea Ice Using Multi-spectral Sentinel-2 and RADAR Sentinel-1 Data. Preprints2024, 2024060961. https://doi.org/10.20944/preprints202406.0961.v1
Williams, J.; Bhaganagar, K. Principal Component Analysis of Arctic Sea Ice Using Multi-spectral Sentinel-2 and RADAR Sentinel-1 Data. Preprints 2024, 2024060961. https://doi.org/10.20944/preprints202406.0961.v1
Williams, J.; Bhaganagar, K. Principal Component Analysis of Arctic Sea Ice Using Multi-spectral Sentinel-2 and RADAR Sentinel-1 Data. Preprints2024, 2024060961. https://doi.org/10.20944/preprints202406.0961.v1
APA Style
Williams, J., & Bhaganagar, K. (2024). Principal Component Analysis of Arctic Sea Ice Using Multi-spectral Sentinel-2 and RADAR Sentinel-1 Data. Preprints. https://doi.org/10.20944/preprints202406.0961.v1
Chicago/Turabian Style
Williams, J. and Kiran Bhaganagar. 2024 "Principal Component Analysis of Arctic Sea Ice Using Multi-spectral Sentinel-2 and RADAR Sentinel-1 Data" Preprints. https://doi.org/10.20944/preprints202406.0961.v1
Abstract
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.
Keywords
Principal Component Analysis; sea ice; Sentinel-2; Sentinel-1; Beaufort Sea; Leads
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.