Intense and colourful bloom of various phytoplankton species across the Black Sea in early July 2022.
Last Updated
04 May 2023
Published on
14 July 2022
By Ivan Smiljanic (Exostaff), Ben Loveday (Innoflair UG) and Hayley Evers-King (EUMETSAT)
True Colour RGB composite, due to the fact that it utilises spectral channels from the visible range of spectra, can depict the signatures of different constituents of sea water, including phytoplankton. Phytoplankton absorb and scatter light as a result of the pigments they contain and the structure of their cells. For example, high concentrations of phytoplankton cells usually result in high concentrations of the primary photosynthetic pigment – chlorophyll a. This results in various gradients of blue and turquoise shades in this RGB product, related to different concentrations of more than 150 different types of phytoplankton found in the Black Sea (Figure 1).
Figure 1: Phytoplankton bloom seen by Sentinel-3 OLCI enhanced True Colour RGB, 4 July 23:59 UTC
Phytoplankton blooms are influenced by the availability of nutrients and light, which are, in turn, influenced by the physical circulation of water masses in the oceans. As such, the shapes, patterns, and variations in colour reflect these complex dynamics. Polar orbiting ocean colour missions, like Sentinel-3's OLCI, are designed to provide measurements of these colours with a high degree of sensitivity; measuring subtle changes of colours in many different parts of the visible light spectrum, at the same local time every day under relatively consistent illumination conditions (day-to-day loop in Figure 2).
Figure 2: Sentinel-3 OLCI True Colour RGB, 29 June-9 July
Although not designed for quantitative ocean colour studies, geostationary sensors that make measurements of visible light can trace some of the patterns of plankton blooms at the sub daily scale, helping to avoid cloud contamination. The current European Meteosat geostationary satellites (with the SEVIRI imager onboard) have only a few spectral bands in the visible spectrum — typically used for ocean colour (400-800nm). As phytoplankton pigment primarily absorbs light in the blue region of the spectrum, the SEVIRI instrument, with its wide bands around 600nm or higher, is not suited to catching the gradients in the ocean colour. This can be seen from the Natural Colour RGB loop (Figure 3), consisting of visible/near-IR channels, where the Black Sea is seen in uniform, dark-blue hues. Note: some light-blue/cyan shades correspond to reflection from the thin smoke layer above the sea, only seen in morning hours due to a strong forward scattering.
Figure 3: Meteosat-11 Natural Colour RGB, 5 July, 15-min time step 03:00-05:00 UTC.
The next generation of Meteosat satellites (Meteosat Third Generation) will carry an FCI imaging instrument that will have two additional spectral channels with shorter wavelengths than 600nm, expanding some capability to trace 'ocean colour' signatures over Europe, and wider. However, in addition to the number of bands that an instruments measures in, one also needs to consider response functions of the same/similar channels across different instruments, that also contribute to different results when detecting the features in the upper ocean layers. Note, for instance, the difference in hues between OLCI and MODIS True Colour RGBs — OLCI has a higher number of spectral bands, with narrower widths and more constrained response functions. For sensors not designed for ocean colour applications, the situation is even more different, with typically fewer, wider bands, that are more suitable for weather, atmospheric, and land applications.
Different spectral properties of same/similar channels across different instruments might bring slight differences in colour hues (spectral response) in the same RGBs. Figure 4 covers the similar scene also with MODIS daily True Color RGB, for comparison with OLCI loop in Figure 2.
Figure 4: Terra MODIS True Color RGB, 30 June-6 July
The polar orbiting ocean colour missions, with these specific bands, are particularly advantageous when it comes to production of quantitative geophysical products for a number of reasons (e.g. atmospheric correction and cloud filtering, constant viewing angle, and ability to distinguish and quantify components that have complex spectral signatures). An example of such a quantitative product, namely the Chlorophyll-a Concentration product, is given in Figure 5.