Using Optical Water Types to Monitor Changes in Optically Complex Inland and Coastal Waters

Abstract

The European Space Agency’s Copernicus satellites Sentinel-2 and Sentinel-3 provide observations with high spectral, spatial, and temporal resolution which can be used to monitor inland and coastal waters. Such waters are optically complex, and the water color may vary from completely clear to dark brown. The main factors influencing water color are colored dissolved organic matter, phytoplankton, and suspended sediments. Recently, there has been a growing interest in the use of the optical water type (OWT) classification in the remote sensing of ocean color. Such classification helps to clarify relationships between different properties inside a certain class and quantify variation between classes. In this study, we present a new OWT classification based on the in situ measurements of reflectance spectra for boreal region lakes and coastal areas without extreme optical conditions. This classification divides waters into five OWT (Clear, Moderate, Turbid, Very Turbid, and Brown) and shows that different OWTs have different remote sensing reflectance spectra and that each OWT is associated with a specific bio-optical condition. Developed OWTs are distinguishable by both the MultiSpectral Instrument (MSI) and the Ocean and Land Color Instrument (OLCI) sensors, and the accuracy of the OWT assignment was 95% for both the MSI and OLCI bands. To determine OWT from MSI images, we tested different atmospheric correction (AC) processors, namely ACOLITE, C2RCC, POLYMER, and Sen2Cor and for OLCI images, we tested AC processors ALTNNA, C2RCC, and L2. The C2RCC AC processor was the most accurate and reliable for use with MSI and OLCI images to estimate OWTs.

Optical Water Type Guided Approach to Estimate Optical Water Quality Parameters

“…As the R(λ) spectra are the basis for all the future calculations and developments, the error in R(λ) may multiply in the final product or lead to incorrect conclusions [78]. Moreover, it is known that atmospheric correction over inland and coastal areas is difficult [79][80][81] and the result still can contain large errors [30,45,77,82,83]. Therefore, in this study, to eliminate as many errors as possible and focus on finding the best algorithm, we used in situ measured R(λ) spectra with 1 nm step and for OLCI and MSI we recalculated in situ measured R(λ) using SRFs into OLCI and MSI bands.…”

“…However, it is still important to remember that in situ measured R(λ) is not the absolute truth and can contain large errors for example due to variable weather conditions [45,[84][85][86]. For example, R(λ) measurements are difficult to perform well in CDOM-rich waters, which often leads to strong overestimation in the blue region of the spectrum [45,65]. Moreover, the presence of sunlight increases the R(λ) values in the blue part of the spectrum.…”

“…The classification of OWTs introduced by Uudeberg et al [45] was used to determine the OWT. The classification divides the boreal region inland and coastal waters into five OWTs, each linked to the specific bio-optical condition in order to reflect the dominance of group OSC concentrations.…”

“…To assess the ability of the OWT guided approach to obtain optical water quality parameters from satellite data, the full resolution Level-1 images acquired in 2018 and 2019 by OLCI, onboard the Sentinel-3 satellite, were used. Level-1 images were processed with the Case-2 Regional CoastColour (C2RCC) atmospheric correction processor v1.15, based on the previous study's recommendation [45]. Image primary processing, such as subsetting the region of interest and atmospheric correction processing, and downloading was done using the ESTHub Processing Platform, which is the Estonian Land Board's national mirror site for Copernicus satellite data and processing [59].…”

“…To study the implementation capacity of the OWT guided approach for retrieving water quality parameters, the R(λ) spectra representing the in situ measurement stations were also convolved into MSI and OLCI bands using spectral response functions (SRFs) of satellite sensor bands and calculated according to Uudeberg et al [45]. The SRFs for the MSI and OLCI were taken from [49] and [50], respectively.…”