Assessment of Polymer Atmospheric Correction Algorithm for Hyperspectral Remote Sensing Imagery over Coastal Waters.

Darryl Keith,Mariana A Soppa,Astrid Bracher,François Steinmetz,Niklas Bohn,Daniel Scheffler,Brenner Silva

doi:10.3390/s21124125

Abstract

Spaceborne imaging spectroscopy, also called hyperspectral remote sensing, has shown huge potential to improve current water colour retrievals and, thereby, the monitoring of inland and coastal water ecosystems. However, the quality of water colour retrievals strongly depends on successful removal of the atmospheric/surface contributions to the radiance measured by satellite sensors. Atmospheric correction (AC) algorithms are specially designed to handle these effects, but are challenged by the hundreds of narrow spectral bands obtained by hyperspectral sensors. In this paper, we investigate the performance of Polymer AC for hyperspectral remote sensing over coastal waters. Polymer is, in nature, a hyperspectral algorithm that has been mostly applied to multispectral satellite data to date. Polymer was applied to data from the Hyperspectral Imager for the Coastal Ocean (HICO), validated against in situ multispectral (AERONET-OC) and hyperspectral radiometric measurements, and its performance was compared against that of the hyperspectral version of NASA’s standard AC algorithm, L2gen. The match-up analysis demonstrated very good performance of Polymer in the green spectral region. The mean absolute percentage difference across all the visible bands varied between 16% (green spectral region) and 66% (red spectral region). Compared with L2gen, Polymer remote sensing reflectances presented lower uncertainties, greater data coverage, and higher spectral similarity to in situ measurements. These results demonstrate the potential of Polymer to perform AC on hyperspectral satellite data over coastal waters, thus supporting its application in current and future hyperspectral satellite missions.

Highlights

The vast information provided by hyperspectral sensors allows us to comprehensively explore the upwelling spectral radiance distribution from water and to improve the retrieval of geo-biophysical parameters
In support of the Environmental Mapping and Analysis Program (EnMAP) scientific preparation program, we investigate the performance of POLYnomial-based algorithm applied to MERIS (Polymer) on hyperspectral remote sensing data over coastal waters
At the MVCO, Lisco, Gloria, and Pensacola sites, the mean spectra presented a gradual increase in the Rrs until the peak around 550–570 nm, where they started to decrease towards the longer wavelengths

Summary

Introduction

The vast information provided by hyperspectral sensors allows us to comprehensively explore the upwelling spectral radiance distribution from water and to improve the retrieval of geo-biophysical parameters. The wide range of applications includes the detection of phytoplankton types and harmful algal blooms, benthic habitat mapping, and phytoplankton fluorescence retrievals. Current, and future hyperspectral sensors with potential application in water colour studies include Hyperion (2000–2017), HICO (2009–2014), SCIAMACHY (2002–2012), CHRIS-PROBA (2001–), OMI (2004–), GOME-2 LW carries the information of the water optical properties and constitutes only a small fraction of the radiance measured by the sensor. AC is key to obtaining accurate retrievals over water using spaceborne remote sensing. There exist several AC algorithms; most of them have been designed and applied to multispectral sensors, as hyperspectral spaceborne data appropriate for water colour research and application are limited.

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Results

Conclusion