Reconstruction of Subsurface Velocities From Satellite Observations Using Iterative Self-Organizing Maps

• Published in: IEEE geoscience and remote sensing letters Vol. 14; no. 5; pp. 617 - 620
• Main Authors: Chapman, Christopher, Charantonis, Anastase Alexandre
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.05.2017; The Institute of Electrical and Electronics Engineers, Inc. (IEEE); IEEE - Institute of Electrical and Electronics Engineers
• Subjects: Columnar structure; Columns (structural); Depth; Floats; Geophysics; High resolution; Mapping; Ocean currents; Ocean temperature; Oceans; Open area test sites; Physics; Reconstruction; Remote sensing; Satellite observation; Satellites; Sea surface; Sea surface temperature; Self organizing maps; self-organizing feature maps; Surface reconstruction; Surface temperature; Surface topography; Surface velocity; Temperature (air-sea); Velocity; Water column; Oceans; self-organizing feature maps; remote sensing
• ISSN: 1545-598X; 1558-0571
• DOI: 10.1109/LGRS.2017.2665603

A new method based on modified self-organizing maps is presented for the reconstruction of deep ocean current velocities from surface information provided by satellites. This method takes advantage of local correlations in the data-space to improve the accuracy of the reconstructed deep velocities. No assumptions regarding the structure of the water column, nor the underlying dynamics of the flow field, are made. Using satellite observations of surface velocity, sea-surface height and sea-surface temperature, as well as observations of the deep current velocity from autonomous Argo floats to train the map, we are able to reconstruct realistic high-resolution velocity fields at a depth of 1000 m. Validation reveals promising results, with a speed root mean squared error of ~2.8 cm.s -1 , more than a factor of two smaller than competing methods, and direction errors consistently smaller than 30°. Finally, we discuss the merits and shortcomings of this methodology.