Mapping Water Quality Parameters with Sentinel-3 Ocean and Land Colour Instrument imagery in the Baltic Sea

Kaire Toming,Tiit Kutser,Birgot Paavel,Rivo Uiboupin,Age Arikas,Kaimo Vahter

doi:10.3390/rs9101070

Abstract

The launch of Ocean and Land Colour Instrument (OLCI) on board Sentinel-3A in 2016 is the beginning of a new era in long time, continuous, high frequency water quality monitoring of coastal waters. Therefore, there is a strong need to validate the OLCI products to be sure that the technical capabilities provided will be used in the best possible way in water quality monitoring and research. The Baltic Sea is an optically complex waterbody where many ocean colour products, performing well in other waterbodies, fail. We tested the performance of standard Case-2 Regional/Coast Colour (C2RCC) processing chain in retrieving water reflectance, inherent optical properties (IOPs), and water quality parameters such as chlorophyll a, total suspended matter (TSM) and coloured dissolved organic matter (CDOM) in the Baltic Sea. The reflectance spectra produced by the C2RCC are realistic in both shape and magnitude. However, the IOPs, and consequently the water quality parameters estimated by the C2RCC, did not have correlation with in situ data. On the other hand, some tested empirical remote sensing algorithms performed well in retrieving chlorophyll a, TSM, CDOM and Secchi depth from the reflectance produced by the C2RCC. This suggests that the atmospheric correction part of the processor performs relatively well while IOP retrieval part of the neural network needs extensive training with actual IOP data before it can produce reasonable estimates for the Baltic Sea.

Highlights

The launch of Ocean and Land Colour Instrument (OLCI) on board ESA Sentinel-3A satellite in February 2016 and the planned near future launch of identical sensor on Sentinel-3B opened a new era in coastal water remote sensing
Combining the Bottom of Atmosphere (BOA) reflectance spectra obtained by means of Case-2 Regional/Coast Colour (C2RCC) processor with the in situ reflectance spectra shows that the atmospheric correction part of the neural network performs quite well in non-bloom conditions
This suggests that the performance of atmospheric correction part of the C2RCC is robust for this part of spectrum

Summary

Introduction

The launch of Ocean and Land Colour Instrument (OLCI) on board ESA Sentinel-3A satellite in February 2016 and the planned near future launch of identical sensor on Sentinel-3B opened a new era in coastal water remote sensing. High amount of coloured dissolved organic matter (CDOM) received from boreal forest in the catchment area makes the water dark. This means that the water leaving signal is very low requiring highly sensitive remote sensing devices and very accurate atmospheric correction (typically more than 95% of the signal measured by satellites originates from atmosphere not water). Diatoms dominate the spring bloom that occurs after ice melt and cyanobacteria dominate during summer and early autumn. Optical properties of these two assemblages are so different that seasonal remote sensing algorithms may be needed [10,11]. There are indications that the seasonality is changing [12,13]

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Conclusion