Michael Schaale and Thomas Schroeder

Michael and Thomas have previously worked together on a processor for ocean data from the MERIS sensor, which was onboard the Envisat satellite from 2002–2012. MERIS was one of the heritage ocean colour instruments that influenced the design of the Ocean and Land Colour Instrument (OLCI) sensors on the Sentinel-3 satellites. Through support from the Copernicus Collaborative Exchange they have now developed a publicly-available version of the FUB-CSIRO coastal water processor for OLCI data.

The colour of the ocean is influenced by a diverse range of factors, including the water itself and its depth (and in shallow cases, the type of substrate); the organisms that live within it, and the presence of sediments, from rivers or re-suspended from the ocean floor. These factors also represent key oceanographic processes vital for managing ocean resources, human impacts on the ocean, and the impact of the ocean on us. Ocean colour sensors, like OLCI aboard the Sentinel-3 satellites, can be used to monitor the ocean colour at a global scale, and, as such, the factors that influence it.

However, there are significant challenges in quantifying and understanding the variability of the factors that influence ocean colour. These influences arise from diverse oceanographic conditions that occur in both space and time around the world. This challenge can be particularly acute in coastal areas where oceanographic conditions tend to be complex and dynamic.

In addition, other challenges, such as atmospheric complexity must be corrected for in the satellite data. As a result, many regional approaches have been developed to support the use of data in different areas. The latter expands the scientific understanding of ocean optics, providing valuable feedback to satellite operators like EUMETSAT, and improves the applicability of data from ocean colour instruments.

Satellite-based instruments have also developed through time. This presents opportunities for the development of new methods, but also challenges that must be addressed to apply existing approaches.

As part of their recent work, Michael and Thomas used in situ data collected under the Australian Integrated Marine Observing System (IMOS), allowing them to validate the results from the FUB-CSIRO Coastal Water Processor, allowing them to validate the results from the FUB-CSIRO coastal water processor for OLCI data of the processor (which is based on several artificial neural networks), across a diverse range of ocean colour conditions.

Figure 1 shows the IMOS instrumentation and field locations that were used to collect the data coincident with the satellites flying overhead. Comparing the in situ measurements with the results obtained from the new processor showed good performance of the atmospheric correction method across the complex range of coast oceanographic conditions.

Figure 2 shows a True Colour image created from OLCI data in the region, showing how variable coastal oceanographic conditions can be, with areas of shallow coral reef, turbid and sediment rich coastal waters, and clear oceanic blue water. The image covers parts of the southern Great Barrier Reef. Region (A) shows an area of high turbidity caused by tidal re-suspension, while region (B) is associated with clear oceanic blue waters.

Figure 3 is an example output from the processor — the computed reflectance at 560 nm — which is very sensitive to turbidity. It shows estimated remote sensing reflectance (after atmopsheric correction) with the FUB-CSIRO coastal water processor over the parts of Great Barrier Reef, Australia and corresponding true colour composite in Figure 2. High reflectance areas are shown in red and low reflectance areas associated with less turbid waters off shore in blue.

As well as conducting the atmospheric correction process necessary to derive the regional ocean colour, the processor also estimates the aerosol optical depth, chlorophyll-a and total suspended matter concentrations, absorption of yellow substance at 443 nm, the water-leaving reflectances at selected wavelengths, and the per-pixel uncertainties of all derived products.

The Sentinel-3 FUB-CSIRO Coastal Water Processor is available as a SNAP plug in on github. Details can be found in the associated publication.