Automated detection and tracking of medium-large icebergs from Sentinel-1 imagery using Google Earth Engine

Abstract

Monitoring Antarctic icebergs helps us understand the interaction between ocean, atmosphere, and sea ice in the Southern Ocean. Although gigantic icebergs have been the objects of many remote sensing studies, medium icebergs in the Southern Ocean have been rarely monitored or traced. In this study, we develop an iceberg detection and tracking tool particularly for medium and large icebergs (0.4–10 km2), by using Python programming language and Sentinel-1 (S1) imagery, based on Google Earth Engine (GEE). To detect icebergs, we employ the simple non-iterative clustering (SNIC) and region adjacency graph (RAG) merging for object-based image segmentation and train/test the support vector machine (SVM) model with 6432 labeled segments of iceberg or non-icebergs from 40 images of S1 (2019–2021). Radar backscatter features and morphological features of those segments are used as the inputs of the SVM model. After icebergs are detected in two image scenes of different dates, we track the displacements of detected icebergs by comparing their 1-D shape signals. Our model shows ∼99% of accuracy in detecting icebergs and ∼ 93–98% of accuracy in tracking icebergs depending on the day difference between image scenes. When using our tool for the monitoring of icebergs in the Amundsen Sea, we find that the iceberg fraction varies from 2% to 8% in 2021 and most of icebergs move westward with a speed of <0.2 km/day.