Beyond the First Optical Depth: Fusing Optical Data From Ocean Color Imagery and Gliders

Abstract

Optical properties derived from ocean color imagery represent vertically-integrated values from roughly the first attenuation length in the water column, thereby providing no information on the vertical structure. Robotic, in situ gliders, on the other hand, are not as synoptic, but provide the vertical structure. By linking measurements from these two platforms we can obtain a more complete environmental picture. We merged optical measurements derived from gliders with ocean color satellite imagery to reconstruct vertical structure of particle size spectra (PSD) in Antarctic shelf waters during January 2007. Satellite-derived PSD was estimated from reflectance ratios using the spectral slope of paniculate backscattering. Average surface values (0-20 m depth) of were spatially coherent (I to 50 km resolution) between space and in-water remote sensing estimates. This agreement was confirmed with shipboard vertical profiles of spectral backscattering (llydroScat-6). It is suggested the complimentary use of glider-satellite optical relationships, ancillary data (e.g., wind speed) and ecological interpretation of spatial changes on particle dynamics (e.g., phytoplankton growth) to model underwater light fields based on cloud-free ocean color imagery.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2009
Accession Number
ADA513120

Entities

People

  • David English
  • Hugh Ducklow
  • J. Kerfoot
  • K. Carder
  • M. A. Montes-hugo
  • Oscar Schofield
  • Richard W Gould
  • Robert A. Arnone

Organizations

  • United States Naval Research Laboratory

Tags

Communities of Interest

  • Space

DTIC Thesaurus Topics

  • Artificial Satellites
  • Backscattering
  • Birds
  • Detection
  • Detectors
  • Grids
  • Measurement
  • Oceanography
  • Oceans
  • Optical Properties
  • Optics
  • Particle Size
  • Particles
  • Phytoplankton
  • Remote Sensing
  • Satellite Imaging
  • Three Dimensional

Fields of Study

  • Environmental science

Readers

  • Atmospheric Science/Meteorology
  • Computer Vision.
  • Image Processing and Computer Vision.

Technology Areas

  • AI & ML
  • Autonomy
  • Space