Research Article

Identification and Mapping of Ocean Biological Deserts Using Satellite Data  

Mini  Raman1 , Rahul  Rajan1 , Ajai 2
1. Space Applications Centre, Ahmedabad-380015, India
2. ES, CSIR, Space Applications Centre, Ahmedabad- 380015, India
Author    Correspondence author
International Journal of Marine Science, 2016, Vol. 6, No. 50   doi: 10.5376/ijms.2016.06.0050
Received: 18 Sep., 2016    Accepted: 06 Dec., 2016    Published: 09 Dec., 2016
© 2016 BioPublisher Publishing Platform
This is an open access article published under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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Raman M., Rajan R., and Ajai, 2016, Identification and Mapping of Ocean Biological Deserts Using Satellite Data,International Journal of Marine Science, 6(50):1-9(doi: 10.5376/ijms.2016.06.0050)


The ability of oceans to sequester large amounts of atmospheric CO2 through the biological pump has generated considerable interest in formulating strategies towards mitigating the impact of climate change. One such mitigation strategy is the artificial fertilisation of oceanic waters characterised by very low phytoplankton biomass. Regions of ocean that contain low phytoplankton biomass or chlorophyll-concentrations are called as ‘Ocean Biological Deserts’. A primary requirement for conducting fertilisation experiments is the identification of regions that are uniformly low in chlorophyll concentration without any seasonal or interannual variations. This communication reports the identification of uniformly low chlorophyll regions (ocean biological deserts) in the Arabian Sea and Bay of Bengal using satellite derived ocean colour variables that can be considered suitable for artificial enrichment.

Ocean colour; Chlorophyll-a; Ocean biological deserts; Arabian Sea; Bay of Bengal; Artificial fertilisation


Over the past 150 years, human civilisation’s weight on the essential life-supporting services of the Earth System has grown fivefold. Human activities such as fossil fuel emissions, biomass burning and land use changes have profoundly impacted the global cycles of many greenhouse gases especially the global carbon cycle. The present carbon dioxide levels are higher than the levels experienced on the planet for over last 400, 000 years, paving the way for a rapid climate warming by several degrees in the next few decades (McCarthy and James, 2009). Based on General Circulation Model outputs for different scenarios of increased atmospheric carbon dioxide, the average global temperature is projected to increase by 1.8 to 4.0 0C by the end of 21st century (IPCC 2007). Attempts to limit the future growth of atmospheric carbon dioxide concentration will involve major and potentially costly modifications in energy and technology policy. Assessment of the climatic impacts of increasing atmospheric CO2 levels and its mitigation requires an understanding of long- term storage changes in all key carbon reservoirs (atmosphere, oceans and the terrestrial biosphere).


Oceans play a significant role in influencing Earth’s weather and climate. Through a complex system of winds and currents, the oceans and atmosphere work together to distribute enormous quantities of heat and to regulate global temperature (Trenberth and Soloman, 1994).Of all the greenhouse gases, carbon dioxide is the most important because of its links with anthropogenic activities. Ocean’s carbon inventory is approximately 40 giga tons. This is approximately sixty five times larger than the CO2 inventory of the atmosphere and ~ 20 times larger than the amount of carbon tied up in terrestrial biosphere (Trenberth and Soloman, 1994). A number of physical, chemical and biological processes govern the transport of carbon in the ocean from the surface waters to the deep waters and sediments of the ocean floor as well as its cycling among various organic and inorganic forms. The transport of atmospheric carbon dioxide into the ocean’s interior is governed by two pumps, the solubility pump and the biological pump. The uptake of carbon through photosynthesis by phytoplankton and its export to ocean interior and sediments constitute the biological pump. Phytoplankton, a group of microscopic, free-floating autotrophic organisms are the main primary producers of the upper ocean forming the base of food chain and providing trophic support for oceanic life. Through the process of photosynthesis, phytoplankton converts inorganic matter into organic matter. As a constituent of phytoplankton tissue, the fixed carbon may sink, in part, out of the surface mixed layer; may be transferred in part to higher levels; may be recycled back to carbon dioxide through respiration. The amount of organic biomass produced per unit area per unit time is called the primary production. The rate of primary production is one of the major factors controlling the rate at which carbon dioxide will be driven from the atmosphere into the ocean. The current estimates of primary production by phytoplankton in the global ocean is between 30-50 gigatonnes of carbon annually which is about 40% of the total global carbon fixation (Falkowski, 1994; Sakshaug, et al., 1997).


2. Significance of Ocean Biological Deserts

The ability of oceans to sequester large amounts of atmospheric CO2 through the biological pump has generated considerable interest in formulating mitigation strategies for reducing the rising concentrations of atmospheric CO2. One such mitigation strategy that has spawned several investigations in recent years is the artificial fertilisation of oceanic waters characterised by very low phytoplankton biomass. Regions of ocean that contain low phytoplankton biomass indexed as chlorophyll-concentrations are called “ocean biological deserts”.


The concept of ocean fertilisation is based on the effectiveness of the biological pump to sequester large amount of atmospheric carbon into deep layers of the oceans. It is widely accepted that the glacial-interglacial cyclic changes in the CO2 content were caused by changes in the efficiency of the biological pump (Martin, 1990). As stated earlier, large phytoplankton populations are supported in sunlit zones enriched by nutrient availability (Dugdale, 1976). This enrichment or fertilisation occurs naturally when upwelling or vertical mixing entrains nutrient-rich water to the surface. Fertilization also occurs when weather carries wind-blown dust containing small amount of iron long distances over the ocean, or iron-rich minerals are carried into the ocean by glaciers, rivers and icebergs. Due to low solubility under oxidising conditions and not readily bio-available, iron limitations in certain areas of oceans were known to limit photosynthesis and growth of phytoplankton biomass (Martin et al., 1991). Further studies indicated that scarcity of iron as a micro- nutrient indeed limited the phytoplankton growth and overall productivity in these desolate zones, later termed as HNLC (High Nutrient Low Chlorophyll) regions. About 20% of world’s surface ocean belongs to HNLC regions where macronutrients remain largely unutilised and low biomass and chlorophyll concentrations prevail. These regions comprise large part of Southern Ocean, Equatorial Pacific and part of North Pacific.


Ocean fertilisation experiments in macronutrient rich but biologically impoverished (HNLC) regions for sequestration of carbon dioxide were started in early nineties (Martin et al.,1991). The first “proof of concept” trial experiment on ocean fertilisation Iron EX1, was carried out near the Galapagos Islands in 1993 demonstrating the generation of phytoplankton bloom by iron addition. A patch of ocean (64 km2) near the Galapagos Islands fertilized with iron sulphate into the surface ocean waters for 18 days produced an intense bloom causing a large drawdown of carbon dioxide. Since then, several international research teams have completed eleven ocean trials confirming the iron fertilisation effects (Jin et al., 2008). The most recent open ocean trial of ocean iron fertilization, dubbed LOHAFEX, was conducted from January to March 2009 in the South Atlantic. 

The surface waters of sub-tropical and tropical oceans have very low concentrations of macro nutrients resulting in low phytoplankton biomass and are characterised by low rates of primary production and export of particulate organic carbon (POC) to the deep ocean. These regions are termed as low nutrients low chlorophyll (LNLC) areas because the magnitude of fluxes in the carbon cycle of these habitats is determined by the supply of inorganic macronutrients. In the carbon sequestration potential, LNLC areas correspond to the global ocean minima. However, LNLC regions are important in the global marine carbon export budget since they occupy approximately 50% of the ocean (Cullen and Boyd, 2008). These oceanic deserts are deficient in one or more micro or macro nutrients and are classified as HNLC (High nutrient low chlorophyll) and LNLC (low nutrient low chlorophyll) region.


A primary requirement for conducting fertilisation experiments is the identification of oceanic deserts, the regions with uniformly low chlorophyll concentration and without any seasonal or interannual variations.  


Given the vast expanse of oceanic waters, ship-board observations are not only laborious, time consuming and expensive but are also limited to small space and time scales and many regions of the ocean remain grossly under sampled. Satellite remote sensing provides a viable alternative for data collection over a vast oceanic area. Global ocean colour data has been available from missions like SeaWiFS and Oceansat1 & 2.    


Remote sensing of ocean colour or visible spectral radiometry refers to reflectance properties of ocean in the visible and near infrared regions caused by the interactions of incident light with constituents present in water.  Water molecules, phytoplankton, detritus (algal debris, inorganic components, bacteria) and colored dissolved   organic matter modify the light emanating from the sea surface through selective absorption and backscattering (Morel and Prieur, 1977).


Chl-a (Chlorophyll-a), the green pigment primarily involved in the process of photosynthesis, selectively absorbs blue (440 nm) and red light (670nm) of the electromagnetic spectrum leaving the green region (520-620nm) of the spectrum relatively less absorbed. The colour of the ocean progressively shifts from blue to green as the chlorophyll bearing phytoplankton concentration increases. This property of colour change is quantitatively related to the ratios of backscattered light at various wavelengths taken from satellite-borne instrument to produce synoptic maps of phytoplankton biomass characterized in terms of surface Chl-a concentration (O’Reilly et al., 1998). Thus ocean colour images represent the amount of phytoplankton indexed as Chl-a concentration (mg.m-3) in space and time. The new generation ocean colour sensors have been specifically designed to provide quantitative estimation of Chl-a concentration over the range of 0.05-50 mg.m-3 (Rast, 1996).  


In this study, satellite derived ocean colour data has been used to identify low nutrient low chlorophyll (LNLC) regions (ocean biological deserts), suitable for artificial enrichment, in the northern Indian ocean (Arabian Sea and Bay of Bengal). 


3. Materials and Methods

The study area comprises of Arabian Sea and Bay of Bengal, northern Indian Ocean. The Indian sub-continent divides the northern Indian Ocean region into North West Indian Ocean (NWIO) and North East Indian Ocean (NEIO). NWIO comprises Arabian Sea and Lakshadweep Seas and the NEIO consists of Bay of Bengal and Andaman Seas. Due to the asymmetric shape imposed by the existence of Asian sub-continent an unique atmospheric circulation known as monsoon circulation develops, which affect the ocean surface circulation extending beyond the equator up to 10o S. Consequently, Indian Ocean north of 10o experiences semi-annual reversal of the surface circulation in response to the changing wind system (Wyrtki, 1973).The seasonally-reversing monsoon winds are divided into south-west (June-September) and north-east (December-March) monsoon phases with two transition periods, spring inter monsoon (April-May) and fall inter monsoon (October-November). The large-scale open-ocean, seasonally-reversing currents are known as monsoon currents. Contrasting oceanographic regimes in NWIO and NEIO are produced due to asymmetrical distribution of fresh water and energy arising from the continental topography and monsoon dynamics. The NWIO region is affected by intense upwelling during the south–west monsoon and convective mixing during the north-east monsoon that enriches the surface waters with essential nutrients, resulting in high rates of primary production (Prasanna Kumar and Prasad, 1996). In contrast, an excess of precipitation over evaporation characterizes the NEIO. Most of the sub-continent’s rivers drain into this region, which results in a strong thermohaline stratification of the upper water column. As a result, despite large terrestrial inputs of nutrients NEIO is characterised by relatively low primary production.


Monthly ocean colour datasets derived from SeaWiFS Level 3 monthly composites of Arabian Sea and Bay of Bengal have been used in this study. Data sets were obtained from Goddard Distributed Active Archive Center ( for the years January 1998 through December 2007 at 9 km pixel resolution. To complement the time series of phytoplankton biomass fields, monthly products of Sea Surface Temperature (C°) from Advanced Very High Resolution Radiometer (AVHRR) were also obtained and analyzed. Accuracy of retrieved chlorophyll concentrations using a universal algorithm for global oceans are within ± 35% of in situ concentrations in accordance with the goal set by ocean colour missions. Since the data analysis is based on basin-wide spatial and temporal variations in pigment patterns, it is assumed that spatial and temporal changes seen in satellite data on monthly time scales are the result of changes in population structure of phytoplankton. Annual mean Chl-a and SST for each year and a decadal (1998-2008) were computed from the time–series datasets of ocean colour and SST. Bathymetry image was generated for the same region to know the depth of ocean basin. Spatial anomaly index was computed as the difference between mean climatic value of each pixel and the basin scale climatic mean of north Indian Ocean normalized to basin scale standard deviation. This procedure not only removes the spatial variability caused due the inert-annual variations but also make the data dimensionless. Primary production in gC m-2 year-1 of the north Indian Ocean was also estimated using an analytical model (Platt and Sathyendranath, 1988) incorporating the decadal mean Chl-a data as an input coupled with parameters of photosynthesis-light relationship, sea surface irradiance, day length, photic depth and attenuation of light within the water column.


4. Results and Discussions

Annual mean Chl-a images from 1998-2007 are shown in figure1. Mean Chl-a values ranged from 0.1 to ~7.5 mg.m-3in the Indian Ocean basin. 



Figure 1 Time series of annual mean chlorophyll a maps


Decadal mean of phytoplankton biomass concentration averaged over a period of ten years from 1998-2007 in the Arabian Sea and Bay of Bengal is shown in (Figure 2 a). Both annual and climatic mean images show high concentration of phytoplankton biomass in the coastal, shelf and open ocean region of northern, eastern and western basins of northern Indian Ocean. High spatial and temporal variability in the chlorophyll concentration is observed for most parts of the northern Indian Ocean basin in the annual mean images as well as in the decadal mean image. This is clearly depicted in the standard deviation image of the climatic mean chlorophyll (Figure 2 b). 



Figure 2 a) Climatic mean chlorophylla map (1998-2007). b) Standard deviation chlorophyll a map (1998-2007)


However, two regions of low chlorophyll concentration (Figure 2a, b shown in dark purple) and minimum standard deviation (<1 mg .m-3) can be identified in the southern oceanic regions of Arabian Sea, Bay of Bengal (0-10° N) and central Bay of Bengal (8-15° N). These comprise the low chlorophyll (< 0.2 mg.m-3) oligotrophic waters of the northern Indian Ocean basin. The overall concentration patterns are repetitive for all the years indicating the presence of low nutrient low chlorophyll region (LNLC) with no pronounced inter annual variability.


Two small areas (marked as red) in the Figure 1 were identified for further analysis. The bathymetry image corresponding to LNLC regions indicate that both areas are situated above a deep ocean away from the continental margins. The depths of the ocean floor below these regions are approximately between 3000-4000 m (Figure 3). LNLC area identified in Arabian Sea is a large patch of approximately 62010 km2 and LNLC area in Bay of Bengal corresponds roughly to a circular patch of ~ 16641 km2 (Figure 3 a). Figure 3 b shows the interannual variability of chlorophyll concentrations in the LNLC area of Arabian Sea and Bay of Bengal. The LNLC area in Arabian Sea has a low climatic mean chlorophyll value of 0.15 mg.m-3(SD ± 0.013). A CV (coefficient of variation) of 8.5% indicates a homogenous area of very low phytoplankton abundance. Similar results are obtained for the LNLC area in Bay of Bengal with a climatic mean chlorophyll value of 0.15 mg.m-3 (SD ± 0.005) and CV of only 3.2 %.



Figure 3  a) Bathymetry map of Northern Indian Ocean Basin with LNLC regions. Edge of continental shelf at 200m is marked in purple. 3000 m depth contour is marked in white. LNLC regions are marked in Arabian Sea (green patch) and Bay of Bengal (red patch) b) Minimum inter annual variability in the LNLC areas


Spatial anomaly index computed by the method described earlier indicates the chlorophyll values in the LNLC regions to be below (-0.5) the climatic basin scale means (Figure 4).



Figure 4 Spatial anomaly index of climatic mean chlorophyllmap. (Dotted line represents contour of –0.5)


The existence of LNLC areas in Arabian Sea and Bay of Bengal is further supported by climatic SST image and a spatial anomaly index of SST (Figure 5a, 5b). High SST values >28 °C persists throughout the year in this region with very little inter annual variability. 



Figure 5 a) Climatic SST map derived from NOAA-AVHR (1998-2007) b) Spatial anomaly index map


In the Arabian Sea LNLC area, SST values range from 30°-32°C whereas in Bay of Bengal, SST values are slightly lowered ranging from 27°-29°C. This may be attributed to the increased duration of cloud cover during an annual cycle. Spatial anomaly index of SST shows that LNLC area of Arabian Sea is warmer (0.5-0.70) compared to the climatic basin –wide mean indicating a permanently stratified ocean with very little vertical mixing of upper mixed layer and deeper waters. The LNLC region in Bay of Bengal is cooler (-0.1-0.40) than the mean climatic basin scale SST. However, this region is more oligotrophic (as seen from the chlorophyll images) compared to Arabian Sea LNLC. Influence of less saline waters flowing to the Bay during the post monsoon period may stratify the upper mixed layer leading to poorer mixing and oligotrophic conditions.


Primary production (Figure 6) in gC m-2y-1 computed using climatic mean chlorophyll a and other variables in an analytical model depicts very low productivity values ranging from 0.2 – 0.3 gC m-2y-1 with a mean value of 0.22 gC m-2y-1 (SD ± 0.054) in the Arabian Sea LNLC area. Bay of Bengal LNCL area also showed primary production, values ranging from 0.2-0.24 gC m-2y-1 with a mean value of 0.22 gC m-2y-1 (SD ± 3.9).


The low standard deviation, indicating almost uniform low production for all the years resulted in a CV of only 1.8 % compared to 5.4 % (CV) in the Arabian Sea LNCL. Euphotic depth estimated from the model is around 78 m for both the areas. This shows that LNCL area is embedded in a typical oligotrophic region of the ocean basin. (Figure 7)



Figure 6 Decadal primary production in northern Indian Ocean (1998-2007)



Figure 7 Average Euphotic depth (m) during1998-2007. LNCL regions are marked in white


5. Conclusions

Chlorophyll a, derived from ‘ocean color’ and sea surface temperature (SST) data have been used to identify and map “ocean biological deserts” (the low nutrient low chlorophyll regions) in the north Indian Ocean. Such “ocean biological deserts” can be the potential sites for artificial enrichment and subsequent growth of phytoplankton biomass to enhance carbon sequestration and mitigate the impact of climate change. However, detailed in situ investigations on the vertical temperature, salinity and density structure up to 1000m depth, nutrient analysis, alkalinity, dissolved inorganic carbon, particulate organic carbon and total carbon dioxide, optical properties of absorption and backscattering, biological properties (pigment concentrations, primacy production, new production, phytoplankton assemblages), needs to be carried out exhaustively for one complete annual cycle before conducting fertilization experiments of these regions.


Authors Contributions: All the three authors have contributed in the concept development, data analysis and interpretation. MR and Ajai have contributed in writing the manuscript.



We thank the Goddard Earth Sciences Data and information Services Center/ Distributed Active Archive Center, NASA, for ocean colour data from SeaWiFS and National Oceanographic Data Center and GHRSST, U.S.A for SST data from NOAA-AVHRR. One of the authors, Ajai, Emeritus Scientist is thankful to the Council of Scientific and Industrial Research (CSIR), India for support through ES Scheme. We are thankful to Mr.TapanMisra, Director, Space Applications Centre, ISRO, and Ahmedabad, India for encouragement and support.



Cullen J. J., and Boyd P. W., 2008. Predicting and verifying the intended and unintended consequences of large scale ocean iron fertilization, Mar EcolProgSer, 364: 295–301.


Dugdale D. C., 1976, Nutrient cycles. In D. Cushing and J. Walsh (eds.), The Ecology of the Sea, Blackwell, Oxford, pp. 141-172.


Falkowski P.G.,1994,The role of phytoplankton photosynthesis in global biogeochemical cycles, Photosynthesis Research, 39: 235-258. PMid:24311124


IPCC.(2007), Fourth Assessment Report.Intergovernmental Panel on Climate Change Secretariat. Geneva, Switzerland


Jin X., Gruber N., Frenzel H., Doney S.C., and McWilliams J.C., 2008, The impact on atmospheric CO2 of iron fertilization induced changes in the ocean biological pump, Biogeosciences, 5: 385-406


Martin J.H., 1990, Glacial-Interglacial CO2 Change: The Iron Hypothesis, Paleoceanography, 5(1): 1–13.


Martin J.H., Gordon R.M., and Fitzwater S.E., 1991, The case for iron. LimnolOceanogr, 36 :1793-1802


McCarthy J. James, 2009, Reflections on: Our planet and its life, origins and futures. Science, 326: 1646-165 PMid:20050228


Morel A., and Prieur L., 1977, Analysis of variations in Ocean Colour, LimnolOceanogr, 22: 709-722.


O'Reilly J.E, Maritonena, Mitchell B.G., Siegal D.A., Carder K.L., Graver S.A., Kahru M and McClain C.R., 1998, Ocean colour chlorophyll algorithms for SeaWiFs, J. of Geophys. 103: 24937-24963


Platt T., and Sathyendranath S., 1988, Oceanic primary production: estimation by remote sensing at local and regional scales, Science, 241:1613-1620 PMid:17820892


Prasanna Kumar S. and Prasad T.G, 1996, Winter cooling in the northern Arabian Sea, Current Science, 71: 834-841


Rast M. A., (ed.), 1996. MERIS: The Medium Resolution Imaging Spectrometer, ESA, Noordwijk


Sakshaug E., Bricaud A., Dandonneau Y., Falkowski P.G., Kiefer D.A., Legendre L., Morel A., Parslow J., and Takahashi M., 1997, Parameters of photosynthesis : definitions, theory and interpretation of results, J. Plank. Res., 19(11): 1637-1670.


Trenberth K. E., and Solomon A., 1994, The Global Heat Balance: Heat transports in the atmosphere and ocean. Climate Dyn., 10: 107--134.


Wyrtki K., 1973, Physical oceanography of the Indian Ocean in The Biology of the Indian Ocean, (ed) B. Zeitzschel. pp. 18-36, (Berlin: Springer Verlag).

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