Ice Floe Tracker An algorithm to automatically retrieve

Ice Floe Tracker: An algorithm to automatically retrieve Lagrangian trajectories via feature matching from National Aeronautics and Space Administration Jet Propulsion Laboratory California Institute of Technology moderate-resolution visual imagery M. Shodlok Science Question: Satellite observations of sea ice along Marginal Ice Zones suggests a strong coupling between sea ice transport and the underlying ocean turbulent eddy field. Moderate Resolution Imaging Spectroradiometer (MODIS) satellite imagery spanning over almost two decades of daily observations at a resolution of 250 m provides a resource for deriving long-term ocean kinematics from sea ice dynamics. We apply a new method to ultimately analyze the feedback between drifting sea ice floes and mesoscale turbulent eddy fields in the Arctic Ocean. Data & Results: We use MODIS images for automatic sea Figure: Mean sea ice velocity field (Apr-Jun 2017) derived from tracked sea ice floes on a 20 km grid. An example trajectory of an ice floe for ~2 weeks is superimposed. Red circles denote sea ice floe centroids. ice retrieval in the East Greenland Current. Evaluation of the technique is done by comparing automatically vs manually detected ice floes, as well as to CMEMS global highresolution SAR sea ice drift product. For Spring 2017 ~1000 ice floes (8 – 65 km lengths) were identified and tracked along East Greenland Coast, and a mean velocity on a 20 km grid established. Lopez, R. , Schodlok, M. P. , and Wilhelmus, M. M. (2019) Ice Floe Tracker: An algorithm to automatically retrieve Lagrangian trajectories via feature matching from moderate-resolution visual imagery. Remote Sensing of Environment. DOI: 10. 1016/j. rse. 2019. 111406 Significance: This study shows that automatically identified and retrieved ice floe positions based on feature matching technique provide a gridded mean velocity field. These fields will be applied to analyze nonlinear interactions between sea ice and ocean turbulent eddy fields. This work was partly supported by the Jet Propulsion Laboratory, California Institute of Technology, under contract with NASA.

National Aeronautics and Space Administration Jet Propulsion Laboratory California Institute of Technology Contact: Michael Schodlok, MS 300 -323, Jet Propulsion Laboratory, Pasadena, CA 91109 schodlok@jpl. nasa. gov Citation: Lopez, R. , Schodlok, M. P. , and Wilhelmus, M. M. (2019) Ice Floe Tracker: An algorithm to automatically retrieve Lagrangian trajectories via feature matching from moderate-resolution visual imagery. Remote Sensing of Environment, in press. Data Sources: MODIS - NASA Worldview https: //worldview. earthdata. nasa. gov. Copernicus Marine Environment Monitoring Service� CMEMS http: //marine. copernicus. eu. Technical Description of Figure: Mean velocity field retrieved from 20. 04. 2017 to 05. 06. 2017 displayed on a 20 km grid. A Lagrangian trajectory of an ice floe identified for almost 2 weeks during the study is superimposed, with red circles denoting its centroid. Ocean bathymetry is reproduced from ETOPO 1, National Geophysical Data Center (NGDC) NOAA. Scientific significance, societal relevance, and relationships to future missions: This study shows that automatically identified and retrieved ice floe positions based on feature matching technique provide a gridded mean velocity field. These fields will be applied to analyze nonlinear interactions between sea ice and ocean turbulent eddy fields. The method will be employed to process longer time sequences to analyze nonlinear interactions between drifting ice floes and the upper ocean turbulent eddy fields in the ECG as well as to investigate other prominent regions of the Arctic Ocean. The ice floe tracker provides new means to study sea ice drift and its variability in the Arctic.