Water quality

Fiery oceans. The red flame-like shapes burning in this image are actually indications of very high water (ocean and lake) chlorophyll concentrations. Chlorophyll is a pigment found in plants and algae. You can see the effect of the easterly trade winds which cause massive upwelling of nutrient-rich water off the West coast of Africa. This causes algal blooms which can be detected with the MODIS satellite. This image encompasses all the MODIS imagery for 2017 combined into a median mosaic.

For other examples check out this Instagram page.

Agricultural change

Bloemhof dam and surrounding agricultural fields along the Vaal river in South Africa. Can you make out the funky seahorse blowing bubbles?

This image was obtained from the vertical co-polarization from the synthetic aperture radar sensor on Sentinel-1. Scenes in January, June and December of 2016 where assigned to the red, blue and green bands, respectively. We cahn use such images visualize change in colout. Landcover that has changed over the year displays with more vivid colours.

For other examples check out this Instagram page.

Climate analysis

Global cloudiness. I have always wondered where on earth am I the most likely to get depressed from lack of sunshine.

I made this image of the average number of cloudy days per year picked up by the Advanced Very High Resolution Radiometer (AVHRR). Dark blue areas have nearly 365 cloudy days, while light tan areas have hardly any.

It is amazing how you can pick up continental boundaries and even major rivers purely from cloud frequency alone. Looks like Gabon and Singapore are some of the gloomiest places to live.

For other examples check out this Instagram page.

Fractional cover

South Africa: rainbow ground cover. A Landsat satellite pixel covers a 30 x 30m area on the ground. The reflectance from this square encompasses a range of substances – e.g. bare ground, water, green grass, concrete etc. Spectral unmixing allows one to decompose a measured spectrum of light into sub-specta which represent different ground surfaces. Here I have differentiated the fractional contribution of bare ground (red), green vegetation (green) and brown vegetation (blue) to individual Landsat pixels over South Africa. The resulting image gives an intuitive indication of ground cover.

For other examples check out this Instagram page.