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Optimizing retrieval spaces of bio-optical models for remote sensing of ocean color

Neranga Hannadige, Peng‐Wang Zhai, P. Jeremy Werdell, Meng Gao, Bryan A. Franz, Kirk Knobelspiesse, Amir Ibrahim

2023Applied Optics10 citationsDOIOpen Access PDF

Abstract

We investigated the optimal number of independent parameters required to accurately represent spectral remote sensing reflectances ( R rs ) by performing principal component analysis on quality controlled in situ and synthetic R rs data. We found that retrieval algorithms should be able to retrieve no more than four free parameters from R rs spectra for most ocean waters. In addition, we evaluated the performance of five different bio-optical models with different numbers of free parameters for the direct inversion of in-water inherent optical properties (IOPs) from in situ and synthetic R rs data. The multi-parameter models showed similar performances regardless of the number of parameters. Considering the computational cost associated with larger parameter spaces, we recommend bio-optical models with three free parameters for the use of IOP or joint retrieval algorithms.

Topics & Concepts

Remote sensingComputer sciencePrincipal component analysisInversion (geology)Ocean colorOpticsArtificial intelligenceGeologyPhysicsStructural basinSatelliteAstronomyPaleontologyMarine and coastal ecosystemsWater Quality Monitoring TechnologiesEnvironmental Monitoring and Data Management
Optimizing retrieval spaces of bio-optical models for remote sensing of ocean color | Litcius