Tropical storm Idai made landfall in March 2019. With extremely heavy rains and strong winds it caused flash flooding resulting in many hundreds of deaths and massive destruction of property and crops. At least 1,303

people were reported killed, and 3 million others effected by the damage. This is the deadliest recorded tropical cyclone in the South-West Indian Ocean. 

The Damage was estimated at ≥ $2.2 billion (2019 USD), meaning it is also the costliest tropical cyclone in the South-West Indian Ocean on record.

In this Earth Blox example, we look at a time series of Sentinel 1 radar. Radar picks up flooding very well because it is a side-looking active instrument. At radar wavelengths, open water is much smoother than the ground surface, so that radar pulses are reflected away from the direction of the radar antenna, and no echo is detected. This means that the radar image becomes much darker in areas where there is surface water, as is the case with flooding. You will notice that lakes, rivers and the oceans also appear much darker in a radar image.

To see this for the Mozambique flood, follow the steps below. 

• Step 1: Select the relevant area on the map
• Step 2: Set up the radar data block
• Step 3: The time series block
• Step 4: Run the workflow
• Step 5: Interpret the image
• Final Note: What do the other values refer to in the radar block?

Step 1: Select the relevant area on the map

• Clear the map using the X button. This ensures there are no other areas. 
• Draw an area of interest around the coastal city of Beira (about half way down the coast of

  Mozambique – use the ROADMAP view to find it).  The box should be about 100km wide

  (there is a scale at the bottom of the map). Most of the flooding happens to the west of

  Beira.

• It should have the colour associated with Area 1 in the Users Areas list.  

The quick way to see this workflow in action is to find it in the Workflow Library. It is called "S1 TYPHOON IDAI FLOODING". 

Step 2: Set up the radar data block

• Select the radar block, and choose dates from 2019-01-01 to 2019-06-01.
• Leave the default selection of Sentinel 1 as the source of the imagery. 
• Choose Both for the orbit.  
• Choose VV for all of the RGB options. (See below for a description of what these other options mean.)
• And finally, select a layer name. 

Step 3: The time series block

• From the ANALYSIS toolbox, bring in the TIME SERIES block into the workspace.
• Now drag out the visualisation block in the existing workflow and put it into the time series block in its place. When you drag in the time series block make sure it “snaps” in place. 
• And then attach it to the radar block.
• Select "monthly" on the time series block.
• It should look like this:

Step 4: Run the workflow

• When you run this workflow it will generate a time series, with each image a monthly average of the Sentinel 1 data.
• Press the play button to run the images in sequence as an animation, or click on the timeline bar to choose a particular month.

Step 5: Interpret the image

• When you run the animation, the flooding appears as the black area in the month of March. 
• The extent of the area flooded inland is expressed by the areas that go from bright greys (before) to blacks (after)
• You can experiment by choosing Weekly for the time interval on the time series block. You will notice that the coverage of data is not sufficient to guarantee weekly data (which is why we went for monthly) but you can narrow down the flooding to the week of 19-25th and the contrast is much higher (as not so much averaging).
• The increased coverage over the event immediately after is because ESA collects more data when a major event like this occurs.
• The extent of the flooding will look like this:

Final Note: What do the other values refer to in the radar block?

• The option to choose between dB and DN refers to the way the image is scaled. DN=digital number, which is the linear scaling of the radar data.  dB=deciBels, which is a logarithmic scaling of the data and often makes it easier to see the features in the image. 
• The VV and V options in the RGB output refer to the polarisation channel.  Radar signals can be transmitted or received as either horizontal (H) or vertical (V) waves.  The VV channel is both transmitting and receiving in V, whereas the VH channel is transmitting V and receiving in H. (the "cross-polarisation"). 
• The Composite option allows you to combine multiple dates together to create a new image. In the above example, you could choose, say, 2007 to 2011 in one block, and using the Mean option create an image layer that is the average of those five years.  The other options would give you the minimum or maximum value, or the median. 
•  Since radar can see in the dark, you get images on both the daytime and nighttime sides of the Earth as the satellites orbits. Since the orbit goes up and over the poles, one side is going Northwards (ascending) and the other is traveling southwards (descending).  Sometimes there are good reasons to choose only one or the other, but in this instance we want as many images as possible, so we choose Both. 

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