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

Abstract

Satellite remote sensing permits large-scale monitoring of coastal waters through synoptic measurements of water-leaving radiance that can be scaled to relevant water quality metrics and in turn help inform local and regional responses to a variety of stressors. As both the incident and water-leaving radiance are affected by interactions with the intervening atmosphere, the efficacy of atmospheric correction algorithms is essential to derive accurate water-leaving radiometry. Modern ocean color satellite sensors such as the Ocean and Land Colour Instrument (OLCI) onboard the Copernicus Sentinel-3A and -3B satellites are providing unprecedented operational data at the higher spatial, spectral, and temporal resolution that is necessary to resolve optically complex coastal water quality. Validating these satellite-based radiance measurements with vicarious in situ radiometry, especially in optically complex coastal waters, is a critical step in not only evaluating atmospheric correction algorithm performance but ultimately providing accurate water quality metrics for stakeholders. In this study, a regional in situ dataset from the Chesapeake Bay was used to evaluate the performance of four atmospheric correction algorithms applied to OLCI Level-1 data. Images of the Chesapeake Bay are processed through a neural-net based algorithm (C2RCC), a spectral optimization-based algorithm (POLYMER), an iterative two-band bio-optical-based algorithm (L2gen), and compared to the standard Level-2 OLCI data (BAC). Performance was evaluated through a matchup analysis to in situ remote sensing reflectance data. Statistical metrics demonstrated that C2RCC had the best performance, particularly in the longer wavelengths (>560 nm) and POLYMER contained the most clear day coverage (fewest flagged data). This study provides a framework with associated uncertainties and recommendations to utilize OLCI ocean color data to monitor the water quality and biogeochemical dynamics in Chesapeake Bay.

Impacts of Hydrology and Extreme Events on Dissolved Organic Carbon Dynamics in a Heavily Urbanized Estuary and Its Major Tributaries: A View From Space

“…These results are consistent with other studies showing that Polymer has superior performance for OLCI retrievals in coastal waters (e.g., Alikas et al., 2020; Juhls et al., 2022; Pahlevan et al., 2020; Sherman, Tzortziou, Turner, Goes, & Grunert, 2023). Yet, other studies suggested that different approaches, such as C2RCC and ACOLITE, perform better in highly turbid and eutrophic environments (Giannini et al., 2021; Mograne et al., 2019; Vanhellemont & Ruddick, 2021; Windle et al., 2022), highlighting the need for more studies to assess the robustness of AC algorithms across a range of atmospheric and in‐water conditions. Based on the superior performance of Polymer across a range of conditions in the highly dynamic LIS, Polymer was selected as the preferred AC approach for subsequent processing of all OLCI L1B images.…”

Synergistic Use of Earth Observation Driven Techniques to Support the Implementation of Water Framework Directive in Europe: A Review

“…The results of the validation process showed that the performance of the algorithms varied according to each spectral band and evaluation metrics. Windle et al (2022) [129] tried to evaluate AC algorithms applied to OLCI Sentinel-3 data of Chesapeake Bay Waters. This sensor takes advantage of higher spatial (300 m), spectral (21 bands), and temporal (2-day) resolutions in coastal water bodies than in any other operational ocean color satellite sensor.…”

Modeling Coastal Water Clarity Using Landsat‐8 and Sentinel‐2

“…These results are consistent with the findings of Ilori et al ( 2019) who compared different atmospheric correction methods over optically complex coastal waters using in situ radiometric measurements from the Aerosol Robotic Network-Ocean Color (AERONET-OC). Future work could investigate other atmospheric correction algorithms such as Case 2 Regional CoastColour (C2RCC) and POLYnomial-based algorithm applied to Medium Resolution Imaging Spectrometer (POLYMER) in this water body as was done in the Chesapeake Bay (Windle et al, 2022).…”