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Chlorophyll Estimation on Hypso-1 Using Ensemble Machine Learning

Abstract

This work compares different chlorophyll inversion techniques based on features created with surface reflectance R rs . Band-difference and ratio, as well as more complex biomass descriptors such as TBVI and TBM, can be used to correlate chlorophyll with changes in the spectra. The 6SV1 model will be used to retrieve R rs from HYPSO-1 spectral images through atmospheric correction so that relevant chlorophyll descriptors can be fine-tuned for the hyperpsectral camera used. Based on the findings of this work, an ensemble regression model with optimal descriptors can estimate biomass concentrations in the ocean better than a multivariate linear and polynomial regression. The potential to improve biomass concentration estimates from surface reflectance has been demonstrated in the study.

Category

Academic article

Language

English

Author(s)

  • Alvaro Flores Romero
  • Steven Yves Le Moan
  • Joseph Landon Garrett
  • Sivert Bakken

Affiliation

  • Norwegian University of Science and Technology
  • SINTEF Ocean / Fisheries and New Biomarine Industry

Year

2024

Published in

Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing

ISSN

2158-6276

Publisher

IEEE (Institute of Electrical and Electronics Engineers)

View this publication at Cristin