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Assessment of Polymer Atmospheric Correction Algorithm for Hyperspectral Remote Sensing Imagery over Coastal Waters
Citation:
Soppa, M., B. Silva, F. Steinmetz, D. Keith, D. Scheffler, N. Bohn, AND A. Bracher. Assessment of Polymer Atmospheric Correction Algorithm for Hyperspectral Remote Sensing Imagery over Coastal Waters. Sensors. MDPI, Basel, Switzerland, 21(12):4125, (2021). https://doi.org/10.3390/s21124125
Impact/Purpose:
This study proves that hyperspectral data acquired from earth orbiting satellites can be transformed in water quality information in support of nutrient and harmful algal bloom monitoring in coastal and inland systems. The application of this technology to environmental monitoring is dependent on successful retrieval of color from the underwater light field. In this study, the Polymer atmospheric correction procedure demonstrated its potential as a tool to process new and future hyperspectral satellite mission data into information that can assist state and federal clients in sustainable management practices and inform the general public about water quality conditions.
Description:
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.
URLs/Downloads:
DOI: Assessment of Polymer Atmospheric Correction Algorithm for Hyperspectral Remote Sensing Imagery over Coastal Waters
https://www.mdpi.com/1424-8220/21/12/4125