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

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.

Evaluating Atmospheric Correction Algorithms Applied to OLCI Sentinel-3 Data of Chesapeake Bay Waters

“…The POLYMER algorithm developed by HYGEOS (Lille, France) was also initially developed for MERIS and consists of a full-spectrum coupled spectral matching algorithm [33]. The algorithm relies on a bio-optical reflectance model of Park and Ruddick (2005) and can be modified with known chlorophyll a concentrations and particle backscattering coefficients to represent various oceanic and coastal waters [56]. A polynomial expression is used to represent the coupled atmosphere and water-leaving reflectance and an iterative process is used to optimize parameters to obtain the best spectral fit of water leaving reflectance [33,56,57].…”

“…The algorithm relies on a bio-optical reflectance model of Park and Ruddick (2005) and can be modified with known chlorophyll a concentrations and particle backscattering coefficients to represent various oceanic and coastal waters [56]. A polynomial expression is used to represent the coupled atmosphere and water-leaving reflectance and an iterative process is used to optimize parameters to obtain the best spectral fit of water leaving reflectance [33,56,57]. This algorithm determines aerosol contributions from a linear combination of reflectance terms, rather than from a specific aerosol model, and can characterize complex atmospheric and surface effects such as residual sun glint.…”

A Novel Brain Mapping Method without Predetermined Targets Using Hyperspectral Microscopy

“…Hyperspectral imaging is used in fields such as geography, industry, food, and medicine, but most applications are based on conventional ideas of spectral analysis [68][69][70] . These approaches focus on specific spectral bands or spectral peaks and require the target to be predetermined arbitrarily.…”

A New Method for Calculating Water Quality Parameters by Integrating Space–Ground Hyperspectral Data and Spectral-In Situ Assay Data

“…The flight direction was along the river, and the reflectivity uncertainty caused by water flow can be ignored due to the slow velocity of the river. The calibration cloth can radiometrically calibrate the UAV image and convert the DN value into water reflectance [53], which can be expressed as:…”