Estimating ocean productivity using our eyes in the sky: An introduction.

The world’s oceans are full of life. Phytoplankton form the base of all marine food webs, a key driver in oceanic productivity and is the reason that salmon is on your plate. Chlorophyll-a is a pigment found inside phytoplankton that reflects light and therefore can be distinguished using remote sensing techniques.

Remote what?

Remote sensing. The use of satellite or aircraft-taken images to obtain information about an object on a relatively large scale. You know, when NASA launch a rocket and send stuff into space. Ever think about what that “stuff” is? It’s usually a satellite, covered in sensors, that orbits the earth, takes daily images and sends them back to us. Think about how Google Earth was created.

But how?

Satellites have been used to monitor sea surface temperature (SST) for a really, really long time. Daily, spatially-global SST maps have been used for weather prediction, ocean forecasts, and in coastal applications such as fisheries forecasts, pollution monitoring, and tourism. Chlorophyll is a little harder to estimate, due to the dynamic nature of phytoplankton, and is relatively new on the remote sensing scene (estimations only began in the 1970’s). Chlorophyll concentrations are estimated using algorithms (aka. complex calculations) derived from satellite image data, combined with known reflectance of the pigment and in situ field data collections.

Tell me more…

Satellite images are taken every day by the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor that captures 4.6km of ocean surface per image. These images overlap and are stitched together to create a global map of the oceans. Algorithms are applies, allowing for chlorophyll estimates to be derived for the upper meters of the sea, resulting in daily or monthly image composites such as this:

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Figure 1. Average chlorophyll concentrations from 1997 – 2009.
(Dierssen 2010)

Wait, did you say reflectance?

On the light reflectance spectrum, different objects reflect lights at different wavelengths, depending on their composition. Water absorbs red and green light and reflects blue light, which results in the ocean looking “blue”. On the contrary, the chlorophyll pigment of phytoplankton shift reflectance towards the longer wavelengths, resulting in the ocean looking “green”. Higher chlorophyll equals greener colour, as shown below:

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Figure 2. The change of light reflectance (Rrs) from blue (443-510nm) to green (555nm) as chlorophyll concentration increases. (Dierssen 2010)

Now I’m interested…

Absorption properties and light-absorbing chlorophyll pigments also change with phytoplankton size. Phytoplankton are classed by size: pico plankton (<2 nm), ultraplankton (2–5 nm) and microplankton (>20 nm). Their absorption coefficients vary, influencing the amount of light that is reflected (see Figure 3 below). One study found that water dominated by microplankton had higher chlorophyll than predicted by the algorithm, and conversley, water dominated by picoplankton had less chlorophyll than predicted.

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Figure 3. The spectral shape and magnitude of chlorophyll-normalised absorption, a*ph, changes for different sizes of phytoplankton.
(Dierssen 2010)

Does it work for the whole world?

Almost. Accurate chlorophyll estimations have proven difficult in two oceanic areas – coastal regions and at the poles. Coastal locations are heavily influenced by upwelling events that bring nutrient-rich waters up from the deep ocean, causing vertical mixing. Coloured dissolved organic matter (CDOM) and total suspended matter (TSM) such as sediments congregate in coastal areas, and once mixing is induced, can interact with phytoplankton and cause ocean colour variations. These factors are particularly significant when coastal chlorophyll concentrations are being inferred.

Furthermore, accurate measurements of phytoplankton concentrations in the polar and sub-polar regions has proven difficult. Due to snow, ice and cloud cover, satellites are unable to provide accurate estimations in these areas and thus they remain a “black box” of the ocean, as you can see in the image below. These areas have been linked to high productivity and increases in CO2 as the effects of climate change continue, and thus are vital requirements of future studies.

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Figure 4. Monthly composite of April 2016 estimated chlorophyll concentrations. The black areas represent little or no data. (NASA 2016)

What can chlorophyll estimations infer?

First and foremost, the presence of chlorophyll infers biological productivity. The higher the concentration, the more productive the area. As I said above, phytoplankton are the base of food webs, and therefore monitoring their spatial patterns, seasonal variability and response to climate change can aid in a greater understanding of the marine environment. Short-term chlorophyll concentrations are also of great importance. Such environmental impacts as harmful algal blooms (HABs) and devastating oil spills, can have adverse effects on oceanic health. Monitoring their impacts on chlorophyll concentration at a smaller scale can allow for early detection, monitoring and effective management.

How about fisheries management?

Correct. The application of chlorophyll remote sensing can be used to manage fisheries worldwide. Satellite-dervied data, combined with acoustic, optical and radar ship sensor data, has be realigned with oceanic outputs (such as water temperature, chlorophyll concentration and oceanic fronts), to identify fish distributions, movement patterns and other fisheries forecasts.  With increased interest in efficient and sustainable fishery management, this practice of remote sensing will only become more apparent with time.

Cool. So now I am an expert on chlorophyll…

Not quite. Be sure to administer caution when stating worldwide oceanic chlorophyll concentrations and drawing climate-relevant conclusions – the estimations are assumptions, and we’re still learning and refining the processing algorithms every day. In situ field observations should always be interspersed with satellite data, and depth-related instruments should be included to validate further. The oceans are incredibly complex, highly-dynamic environments that cannot be estimated from their surface alone. You know the old saying… Don’t judge a book by it’s cover!

 

 

REFERENCES:

Anderson, R.F., Ali, S., Bradtmiller, L.I., Nielsen, S.H., Fleisher, M.Q., Anderson, B.E. & Burckle, L.H. 2009, ‘Wind-driven upwelling in the Southern Ocean and the deglacial rise in atmospheric CO2’, Science, vol. 323, pp. 1443–1448.

Blondeau-Patissier, D., Gower, J.F.R., Dekker, A.G., Phinn, S.R. & Brando, V.E. 2013, ‘A review of ocean color remote sensing methods and statistical techniques for the detection, mapping and analysis of phytoplankton blooms in coastal and open oceans’, Progress in Oceanography, vol. 123, pp. 123–144.

Ciotti, A.M., Cullen, J.J. & Lewis, M.R. 2002, ‘Assessment of the relationships between dominant cell size in natural phytoplankton communities and the spectral shape of the absorption coefficient’, Limnology and Oceanography, vol. 47, pp. 404– 417.

Dierssen, H.M. 2010, ‘Perspectives on empirical approaches for ocean color remote sensing of chlorophyll in a changing climate’, Proceedings of the National Academy of Sciences of the United States of America, vol. 107, pp. 17073–17078.

Feldman, G.C. (NASA) 2016, United States of America, accessed 25 May 2016 <http://neo.sci.gsfc.nasa.gov/view.php?datasetId=MY1DMM_CHLORA&gt;

Gitelson, A.A., Gurlin, D., Moses, W. & Barrow, T. 2009, ‘A bio-optical algorithm for the remote estimation of the chlorophyll-a concentration in case 2 waters’, Environmental Research Letters, vol. 4, pp. 1748–9326.

Leifer, I., Lehr, W.J., Simecek-Beatty, D., Bradley, E., Clark, R., Dennison, P., Hu, Y., Matheson, S., Jones, C.E., Holt, B., Reif, M., Roberts, D.A., Svejkovsky, J., Swayze, G. & Wozencraft, J. 2013, ‘State of the art satellite and airborne marine oil spill remote sensing: Application to the BP Deepwater Horizon oil spill’, Remote Sensing of Environment, vol. 124, pp. 185–209.

Morel, A., Gentili, B., Chami, M. & Ras, J. 2006, ‘Bio-optical properties of high chlorophyll case 1 waters and of yellow-substance-dominated case 2 waters’, Deep Sea Research Part 1: Oceanographic Research Papers, vol. 53, pp. 1439–1459.

Seegers, B.N., Birch, J.M., Ill, R.M., Scholin, C.A., Caron, D.A., Seubert, E.L., Howard, M.D.A, Robertson, G.L. & Jones, B.H. 2015, ‘Subsurface seeding of surface harmful algal blooms observed through the integration of autonomous gliders, moored environmental sample processors, and satellite remote sensing in southern California’, Limnology and Oceanography, vol. 60, pp. 754–764.

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