Herein we present an initial approach for assessing water color, specifically chromaticity, and determining if an accurate correlation can be made within chromaticity space between the water color and a hyperspectral synthetic data set. The water color assessed in this paper consist of remote sensing reflectance (Rrs) distributions from the Suomi-National Polarorbiting Partnership (SNPP) Visible Infrared Imaging Radiometer Suite (VIIRS), and the hyperspectral synthetic data set consists of Rrs distributions of natural marine waters. Where strong correlations exist, the hyperspectral Rrs reference data can be blended into the SNPP VIIRS Rrs data, thus creating a hyperspectral SNPP VIIRS spectra. Where applicable, the newly constructed VIIRS hyperspectral signature is compared to in situ data taken during a 2018 National Oceanic and Atmospheric Administration (NOAA) Calibration/Validation cruise. Given the proliferation of small, low-cost airborne platforms equipped with color imaging cameras, there exists tremendous potential to use and hyperspectrally enhance these data streams for ocean monitoring and scientific research. However, techniques for extracting traditional ocean radiant spectra from RGB data fields are new to oceanographic disciplines.
Sean McCarthy, Jason Jolliff, Sherwin Ladner, and Mark David Lewis, "An initial approach for using chromaticity to develop hyperspectral signals for satellite multispectral ocean-color imagery," Proc. SPIE 11014, Ocean Sensing and Monitoring XI, 110140A (Presented at SPIE Defense + Commercial Sensing: April 16, 2019; Published: 10 May 2019); https://doi.org/10.1117/12.2519311.
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