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Sensors (Basel). 2020 Jan 29;20(3). pii: E742. doi: 10.3390/s20030742.

Validation and Comparison of Water Quality Products in Baltic Lakes Using Sentinel-2 MSI and Sentinel-3 OLCI Data.

Author information

1
Institute for Environmental Solutions, Lidlauks, LV-4101 Priekuļu parish, Latvia.
2
Tartu Observatory, University of Tartu, Observatooriumi 1, 61602Tõravere, Estonia.
3
Estonian Marine Institute, University of Tartu, Mäealuse 14, 12618 Tallinn, Estonia.

Abstract

Inland waters, including lakes, are one of the key points of the carbon cycle. Using remote sensing data in lake monitoring has advantages in both temporal and spatial coverage over traditional in-situ methods that are time consuming and expensive. In this study, we compared two sensors on different Copernicus satellites: Multispectral Instrument (MSI) on Sentinel-2 and Ocean and Land Color Instrument (OLCI) on Sentinel-3 to validate several processors and methods to derive water quality products with best performing atmospheric correction processor applied. For validation we used in-situ data from 49 sampling points across four different lakes, collected during 2018. Level-2 optical water quality products, such as chlorophyll-a and the total suspended matter concentrations, water transparency, and the absorption coefficient of the colored dissolved organic matter were compared against in-situ data. Along with the water quality products, the optical water types were obtained, because in lakes one-method-to-all approach is not working well due to the optical complexity of the inland waters. The dynamics of the optical water types of the two sensors were generally in agreement. In most cases, the band ratio algorithms for both sensors with optical water type guidance gave the best results. The best algorithms to obtain the Level-2 water quality products were different for MSI and OLCI. MSI always outperformed OLCI, with R2 0.84-0.97 for different water quality products. Deriving the water quality parameters with optical water type classification should be the first step in estimating the ecological status of the lakes with remote sensing.

KEYWORDS:

MSI; OLCI; Sentinel-2; Sentinel-3; lakes; optical properties; optical water types; optically complex waters; remote sensing; water quality

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