Estimating Winter Arctic Sea Ice Motion Based on Random Forest Models

• Published in: Remote Sensing
• Main Authors: Linxin Zhang, Qian Shi, Matti Leppäranta, Jiping Liu, Qinghua Yang
• Format: Article in Journal/Newspaper
• Language: English
• Published: MDPI AG 2024
• Subjects: sea ice motion; random forest; cryosphere; Science; Q; Arctic Archipelago; Arctic Ocean; Canadian Arctic Archipelago; Sea ice
• Online Access: https://doi.org/10.3390/rs16030581; https://doaj.org/article/382a70fa468541cf839e7b1d9fdbd81c

Sea ice motion (SIM) plays a crucial role in setting the distribution of the ice cover in the Arctic. Limited by images’ spatial resolution and tracking algorithms, challenges exist in obtaining coastal sea ice motion (SIM) based on passive microwave satellite sensors. In this study, we developed a method based on random forest (RF) models to obtain Arctic SIM in winter by incorporating wind field and coastal geographic location information. These random forest models were trained using Synthetic Aperture Radar (SAR) SIM data. Our results show good consistency with SIM data retrieved from satellite imagery and buoy observations. With respect to the SAR data, compared with SIM estimated with RF model training using reanalysis surface wind, the results by additional coastal information input had a lower root mean square error (RMSE) and a higher correlation coefficient by 31% and 14% relative improvement, respectively. The latter SIM result also showed a better performance for magnitude, especially within 100 km of the coastline in the north of the Canadian Arctic Archipelago. In addition, the influence of coastline on SIM is quantified through variable importance calculation, at 22% and 28% importance of all RF variables for east and north SIM components, respectively. These results indicate the great potential of RF models for estimating SIM over the whole Arctic Ocean in winter.