Estimating Errors in Satellite Retrievals of Bio-Optical Properties due to Incorrect Aerosol Model Selection

Abstract

We examine the impact of incorrect atmospheric correction, specifically incorrect aerosol model selection, on retrieval of bio-optical properties from satellite ocean color imagery Uncertainties in retrievals of bio-optical properties (such as chlorophyll, absorption and backscattering coefficients) from satellite ocean color imagery are related to a variety of factors, including errors associated with sensor calibration, atmospheric correction, and the bio-optical inversion algorithms In many cases, selection of an inappropriate or erroneous aerosol model during atmospheric correction can dominate the errors in the satellite estimation of the normalized water-leaving radiances ((n)L(w)), especially over turbid, coastal waters. These errors affect the downstream bio-optical properties Here, we focus on only the impact of incorrect aerosol model selection on the (n)L(w) radiance estimates, through comparisons between Moderate- Resolution Imaging Spectroradiometer (MODIS) satellite data and in situ measurements from AERONET-OC (Aerosol Robotic NETwork - Ocean Color) sampling platforms.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2011
Accession Number
ADA556083

Entities

People

  • Adam Lawson
  • Courtney Kearney
  • James G. Richman
  • Richard W. Gould Jr.
  • Sean C. Mccarthy

Organizations

  • United States Naval Research Laboratory

Tags

Communities of Interest

  • Space

DTIC Thesaurus Topics

  • Algorithms
  • Artificial Satellites
  • Atmospheres
  • Automatic
  • Chlorophylls
  • Data Sets
  • Electromagnetic Scattering
  • Humidity
  • Measurement
  • Military Research
  • Optical Properties
  • Particle Size
  • Radiance
  • Radiative Transfer
  • Remote Sensing
  • Satellite Imaging
  • Scattering

Fields of Study

  • Environmental science

Readers

  • Atmospheric Remote Sensing.
  • Computational Modeling and Simulation

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • Autonomy
  • Space