INCORRECT MATCH DETECTION METHOD FOR ARCTIC SEA-ICE RECONSTRUCTION USING UAV IMAGES

Delving into CO dynamics from the East Sea to the Bering Sea, our study unveils the influence of physical transport on CO budgets. By measuring CO concentrations and parameters, we elucidate the interplay between biological and physical processes, highlighting the role of lateral transport in shaping CO distributions. Our findings underscore the importance of considering both biogeochemical and physical drivers in understanding marine carbon fluxes.

A new Sentinel-1 radar-based sea ice classification algorithm is proposed. We show that the readily available ice charts from operational ice services can reduce the amount of manual work in preparation of large amounts of training/testing data and feed highly reliable data to the trainer in an efficient way. Test results showed that the classifier is capable of retrieving three generalized cover types with overall accuracy of 87 % and 67 % in the winter and summer seasons, respectively.

In this study, we proposed a novel 1-month sea ice concentration (SIC) prediction model with eight predictors using a deep-learning approach, convolutional neural networks (CNNs). The proposed CNN model was evaluated and compared with the two baseline approaches, random-forest and simple-regression models, resulting in better performance. This study also examined SIC predictions for two extreme cases in 2007 and 2012 in detail and the influencing factors through a sensitivity analysis.

Our paper provides an intensive overview of the artificial ocean iron fertilization (aOIF) experiments conducted over the last 25 years to test Martin’s hypothesis, discusses aOIF-related important unanswered open questions, suggests considerations for the design of future aOIF experiments to maximize their effectiveness, and introduces design guidelines for a future Korean Iron Fertilization Experiment in the Southern Ocean.