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Gradient Boosting for the Spectral Super-Resolution of Ocean Color Sensor Data.

Brittney Slocum1, Jason Jolliff1, Sherwin Ladner1

  • 1U.S. Naval Research Laboratory, Stennis Space Center, Bay St. Louis, MS 39529, USA.

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|October 29, 2025
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We developed a gradient boosting method to create detailed hyperspectral images of ocean scenes from limited multispectral satellite data. This enhances ocean color monitoring and spectral data accessibility for environmental studies.

Keywords:
hyperspectralmachine learningremote sensing

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Area of Science:

  • Oceanography
  • Remote Sensing
  • Spectroscopy

Background:

  • Hyperspectral data offers detailed spectral information (100+ bands) crucial for oceanographic analysis.
  • Multispectral sensors provide limited spectral resolution (3-11 bands), undersampling ocean color.
  • Reconstructing hyperspectral signatures from multispectral data is a key challenge in ocean remote sensing.

Purpose of the Study:

  • To develop a gradient boosting framework for reconstructing hyperspectral signatures from multispectral ocean imagery.
  • To enhance the spectral resolution of satellite-based ocean color data.
  • To improve accessibility to high-fidelity hyperspectral data for scientific and environmental applications.

Main Methods:

  • Utilized a gradient boosting framework for spectral reconstruction.
  • Applied the method to remote sensing reflectance (Rrs) data from various ocean color sensors (SNPP VIIRS, OLCI, HICO, PACE OCI).
  • Validated the approach using in situ Rrs data from NOAA calibration and validation cruises.

Main Results:

  • Demonstrated the feasibility of transforming low-spectral-resolution imagery into high-fidelity hyperspectral products.
  • Successfully reconstructed hyperspectral signatures in the visible spectrum (400-700 nm).
  • Showcased the potential for hyperspectral enhancement using readily available multispectral platforms.

Conclusions:

  • The gradient boosting framework effectively reconstructs hyperspectral signatures from multispectral ocean data.
  • This advancement significantly benefits ocean color monitoring and broadens access to spectral data.
  • The technique supports scientific and environmental applications by providing high-resolution spectral information.