ASL 720 Project Ocean Color Remote Sensing Submitted by:­ Akanksha Jain 2010cs50273 Ankur Garg 2010cs10208 Mukesh Kumar 2010cs50288 Saurabh Agarwal 2010cs50293 Saurabh Anand 2010cs50294 Vivek Mittal 2010cs50301 Prince Chandan 2010me20788 1 Contents 1. Introduction 3 2. Applications of Ocean Color Remote Sensing 3 3. Season Variance 4 4. Upwelling / Downwelling 7 5. Chlorophyll concentration comparison between seaWIFS and 13 MODIS­aqua 6. Chlorophyll and Sea Surface Temperature Correlation 17 7. Work Division 21 8. References 22 2 Introduction The "color" of the ocean is determined by the interactions of incident light with substances or particles present in the water Ocean Remote Sensing is concerning the study of the interaction between the visible electromagnetic radiation coming from the sun and aquatic environments Information on the abundance of phytoplankton and dissolved particulate matter can be traced by the values obtained at different locations via the images Applications of Ocean Color Remote Sensing The following are the applications of ocean color remote sensing : ● Surface Temperature ● Mapping of chlorophyll concentrations ● Measurement of inherent optical properties such as absorption and backscatter ● Determination of phytoplankton physiology, phenology, and functional groups ● Studies of ocean carbon fixation and cycling ● Monitoring of ecosystem changes resulting from climate change ● Fisheries management ● Mapping of coral reefs, sea­grass beds, and kelp forests ● Mapping of shallow­water bathymetry and bottom type for military operations ● Detection of harmful algal blooms and pollution events 3 Seasonal Variance We wanted to study how the chlorophyll concentration changes with respect to the seasons. We took the chlorophyll a concentration data from the SeaWiFS satellite at 9km for different months of 2010. Here are the images : 4 As we can see, during the summers the concentration of chlorophyll decreases significantly and is very high during the colder seasons. This can be attributed to the significant increase in the phytoplankton population during the different seasons. Phytoplankton are microscopic plants that are the principal photosynthetic organisms in the ocean and form the base of ocean food webs. Chlorophyll a is the most important pigment involved in phytoplankton photosynthesis. Seasonal phytoplankton variability is related to stratification, destratification and incident solar irradiance which all essentially affect the nutrient availability and hence the population of the phytoplanktons change during the seasons. 5 Seasonal warming leads to a stratified water column which helps retain phytoplankton in well­lit and nutrient­rich surface waters, causing seasonal biomass peaks (blooms). However, strong stratification during summer at low latitudes (like in the Arabian Sea) and midlatitudes cuts off the supply of new nutrients to upper layers and often leads to low rates of photosynthesis and biomass. The absorption rate of carbon dioxide in water also changes due to the temperature changes in the different seasons. All the above reasons contribute to the variance. 6 Upwelling and Downwelling 1. Introduction Upwelling and downwelling describe mass movements of the ocean, which affect both surface and deep currents. These movements are essential in stirring the ocean, delivering oxygen to depth, distributing heat, and bringing nutrients to the surface. Upwelling is the movement of cold, deep, often nutrient­rich water to the surface mixed layer; and downwelling is the movement of surface water to deeper depths. Downwelling occurs when surface waters converge (come together), pushing the surface water downwards. Regions of downwelling have low productivity because of the nutrients get used up and are not continuously resupplied by the cold, nutrient­rich water from below the surface. Upwelling occurs when surface waters diverge (move apart), enabling upward movement of water. Upwelling brings water to the surface that is enriched with nutrients important for primary productivity (algal growth) that in turn supports richly productive marine ecosystems. Upwelling regions are often measured by their productivity due to the influx of nutrients to the surface mixed layer and euphotic zone (sunlit layer) by upwelling currents. This drives photosynthesis of phytoplankton (tiny alga), which form the base of the ocean food web. 2. Areas with Upwelling Whether any region will have upwelling or not, depends on many factors including wind direction, place of the region on earth etc. Based on such factors, various regions may or may not show upwelling. We are mainly interested in upwelling along the coastal areas. The following map shows the regions of upwelling along the coasts of different continents. 7 3. Regions of Chlorophyll­a concentration We make use of the upwelling/downwelling concepts to explain the differences between the Chlorophyll­a concentrations between the different coastal areas around the globe. African Coast: The following two maps show chlorophyll­a concentrations along the coast of southern Africa from two different sensors. This map is from the sensor SeaWIFS 8 This map is from the sensor MODIS­Aqua Using the maps above from both sensors, we see that the western coast of African southern continent has more Chlorophyll­a concentration as compared to the Eastern coast. Bay of Bengal Coast We also observed data from the Indian coasts – Bay of Bengal Coast and Arabian Sea coast. The following two maps are from SeaWIFS and Modis Aqua sensors for Chlorophyll­a concentration in East coast of India 9 Arabian Sea Coast The following two maps are from SeaWIFS and Modis Aqua sensors for Chlorophyll­a concentration in West coast of India. 10 Data from both east and west coast show us that Chlorophyll­a concentration is more in the coastal areas of Arabian Sea as compared to the Chlorophyll­a concentration in the coastal areas of Bay of Bengal. 4. Relation between Chlorophyll concentration and Upwelling We analyzed maps of chlorophyll concentration and the map of regions with upwelling. On the following map of regions with upwelling, we see that the in southern African continent, the west coast show quite high upwelling as compared to the east coast. Also from the chlorophyll concentration plots, we find that chlorophyll concentration is high along the west coast of Africa. 11 Similarly, along the Indian coast, The upwelling is high along the coast of Arabian Sea as compared to the coast of Bay of Bengal. Also, we have seen from the plots of chlorophyll concentration, that it is higher in the coastal areas of Arabia Sea as compared to the coastal areas of Bay of Bengal. From the above two cases, we find a direct correlation between the chlorophyll­a concentration and upwelling. Higher the upwelling in an area, it is more probable to have higher concentrations of chlorophyll­a. Due to it being nutrient rich, the phytoplankton growth is supported and vegetation around that coastal area increases. This way we find that there is more chlorophyll­a concentration in areas with high upwelling as compared to those which don’t. We also see this correlation when we compare Sea temperature and Chlorophyll concentration. The regions with lower temperature have higher chlorophyll concentration. A small reason for cooler sea surface temperatures is upwelling, because it brings cold water from deep sea to the surface. 12 Comparison between MODIS­aqua & seaWIFS To comment on the usability of two satellite,we chose MODIS­aqua at 9km and seaWIFS at 9km. We took the data of chlorophyll concentration from these satellites and compare it. ● We get the data from the a website of NASA which receives the most recently and preprocessed data. Link of website: Ocean Color Radiometry Online Visualization and Analysis http://gdata1.sci.gsfc.nasa.gov/daac­bin/G3/gui.cgi?instance_id=ocean_month 13 ● We downloaded the data and find out the correlation for the two sensors. Data was in .hdf format and can’t open in normal editors, so we used HDF viewer to read data. Then we used matlab to find the correlation between two data. Given below is the matlab code which we have used to find out correlation. fileinfo1=hdfinfo('MAMO_Chlo_9km.hdf'); sdsinfo1=fileinfo1.SDS(1); data1=hdfread(sdsinfo1); fileinfo2=hdfinfo('SWFMO_Chlo.hdf'); sdsinfo2=fileinfo2.SDS(1); data2=hdfread(sdsinfo2); correlation=corrcoef(data1,data2); 14 The correlation variable in above code gives the correlation between the data of two sensors. After the execution of the above code in matlab, we find out value of correlation. Correlation matrix 1.0 0.7997 0.7997 1.0 Diagonal of this matrix corresponds to correlation of with itself hence they are all equal to 1. Off­Diagonal of this matrix corresponds to sample correlation coefficients between the two random variables. In our case these variables are the two satellites whose data we are analysing. Correlation coefficient ranges between [­1 1]. 1 denotes a perfect correlation and 0 denotes no correlation. The positive and negative signs denotes positive and negative correlation between the random variables. abs(correlation)>0.7 indicates strong correlation. ● Result:We are getting the value of the correlation between two satellites = 0.7997, which indicate that the data of two sensors are strongly positively correlated. ● Conclusion: Since the data coming from two satellites are strongly correlated, we can use any sensor for remote sensing because the results of both sensors are almost similar. ● Further Observations: ○ The precision of Modis is higher at high chlorophyll values, while of SeaWIFS is higher at low chlorophyll values as seen from the maps, but there is not much theoretical support to this analysis from the resources searched. ■ These sensors can be used accordingly if there is more practical evidence to the analysis ○ It has been proven in many works that the net error by the two sensors is almost equivalent, which is almost true here too (if judging the differences by averaging) 15 ● Reasons of similarity ○ The orbital characteristics and spatial resolution of the MODIS instruments are similar to those of SeaWiFS ○ Cross­track swath width of both sensors are about 2300 km.