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Marine Data Literacy 2.0

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Home > 2. Marine GIS > 2.38 Giovanni Chlorophyll

    2.38 Obtaining Satellite Chlorophyll-a Imagery from Giovanni 

This exericise is completely deprecated in favor of  2.21 Importing NetCDF Satellite T, S, and Chlorophyll Climatological or Operational Grids into Marine GIS: OceanColor Web that covers the most current recommendations from MDL  The text is retained below for your reference, but please use only the newer item.

  • Title:  Obtaining Satellite Chlorophyll-a Imagery from Giovanni

  • Abstract:  This exercise shows how to access, visualize and download monthly images of climatological satellite chlorophyll-a concentration at the surface from the Web-based application developed by the Goddard Earth Sciences Data and Information Services Center from NASA. After downloading, the exercise demonstrates how to import the rasters to SAGA, change color settings and visualize it in comparison with current vectors obtained in previous exercise.

  • Preliminary Reading

    • N/A

  • Required Software:

  • Other Resources: 

  • Author:  Lilian Krug and Murray Brown

  • Version:  8-6-2015

1.Access Ocean Color Radiometry Online Visualization and Analysis - Global Monthly Products which contain a list of settings to be defined in order to generate your product.  The first setting is the SPATIAL one. Provide coordinates of AOI Liberia.

2. Scrolling down, next setting is the Parameters. Check ‘Climatology’ in the Analysis Options and the Display settings changes automatically to ‘Climatology info’. 

Here we can see the list of sensors which data are available, their spatial resolution and period of activity. Go to the MODIS-Aqua 4 km box, scroll down to the very last item and check chlorophyll a concentration 4 km.  (Unfortunately, they don’t have Sea Surface Temperature climatologies available).

3. Moving on, next setting is time. For now, let’s get the climatology for January only, so adjust Begin and End Date to JAN

Visualization has many options. Here you can choose between Hovmollers, Scatterplots, Time series averages, Animations and others. For this exercise, we will stick with the default Lat-Lon map, Time-averaged

Now click on Generate Visualization and wait while the product is being processed.

4.  This is the climatological January chlorophyll-a map for Liberia. You may notice that even though this is a monthly average, it still contains a lot of missing data due to cloud presence. A big constraint for ocean color science.  

To download the data, go to the Download Data tab, on the upper left corner of the page.

5.  Select and download the NCD format of Input file category, it is a netcdf format. If you wish, you may download the KMZ file, readable in Google Earth.

Save in DATA/OCEAN/Giovanni with the suggested filename

6. Go back to the Visualization Results tab, select Refine Constraints, change the temporal setting Begin and End Dates to July and July and click on Submit Refinements. Download the July climatology, renaming the file to

7. Run SAGA and select MODULES > IMPORT/EXPORT > GDAL/OGR > GDAL:IMPORT RASTER. Locate the file and click OK. Make sure you check Transformation. This will guarantee your data comes associated with the metadata (namely, geographical coordinates). Interpolation choice will slightly affect the Chl-a value (±0.003 mg.m-3 difference in between interpolation methods). We will use same as in previous exercises with raster files and use the Nearest Neighbor method.

8. Import both images (January and July) and change settings regarding color scheme. Use histograms to define the value range and set the mode to Logarithic (up) – Logarithmic stretch factor =10.

9. Now we can compare the effect of wind on surface primary productivity by loading the climatological wind vectors from lesson 6.2 Plotting Vector Arrows from U and V Component Grids with Saga. Load Shape PRODUCTS > SAGA > VECTORS saved as wind_vecs_jan_liberia_coads_nvods_saga.shp and wind_vecs_jul_liberia_coads_nvods_saga.shp