Synthetic aperture radar (SAR)

Synthetic aperture radar (or SAR) is the most common type of radar system used in remote sensing that can produce imagery similar to the multispectral images we covered in Week 3. Although SAR is a fascinating subject, it is also highly technical so we will only be able to briefly introduce it in this course!

SAR satellites fire a wide beam of radio waves at the Earth, at an angle as they orbit. This keeps any one part of the Earth’s surface in view over an extended period of time, cleverly simulating having a very long antenna to measure the signal that is reflected from the surface. The precise geometry is tricky to get your head round, so do not worry too much about it. It’s a bit like throwing a ball at a wall at an angle and then stepping to one side to catch it but loads of balls are being thrown all at once!

_{The small blue lake will stay in the radio beam for a while as the satellite flies along on its orbit, constantly receiving and processing the signal that is reflected back to it. This ‘synthetic aperture’ simulates an antennae several thousand metres in length! Courtesy of William Deadman.}

Different radio wavelengths are used by different SAR satellites. Shorter wavelengths produce higher resolution images, but longer wavelengths can penetrate vegetation, ice and even soil. All radar can penetrate cloud and so clear imagery can always be taken regardless of the weather conditions. As SAR satellites do not rely on radiation from the sun, they can also take images continuously, even at night.

_{Different types of SAR use different wavelengths – shorter wavelengths produce higher resolution data, but cannot penetrate as many materials. Based on an image courtesy of NASA.}

As the signal being fired and reflected at the Earth’s surface is radio/microwave radiation, SAR remote sensing does not produce recognisable colour images. Instead, various different components of the returned, and processed, signal can be displayed in RGB images as bands, just like the multispectral imagery we looked at last week.

_{SAR (top) and natural colour (bottom) image of the area surrounding the ‘Lost City of Ubar’. Pink trackways can be seen in the radar image leading to the desert settlement that, legend has it, was the home of the Queen of Sheba. Courtesy of NASA.}

To better understand these SAR images, we are going to look at Sentinel-1, one particular SAR mission, in more detail.

Sentinel-1

Sentinel-1 is the European Space Agency’s (ESA) SAR program. It consists of two satellites, Sentinel-1A and 1B, that were launched in 2014 and 2016. Together they can map the whole Earth in six days, operating regardless of the time of day or weather conditions. As with other ESA remote sensing data, Sentinel-1 data can be downloaded from the Copernicus Science Hub (which we used to download Sentinel-2 imagery in week 2).

_{One of the Sentinel-1 satellites. Courtesy of ESA.}

The Sentinel-1 satellites have various data collection modes, but the most common has a resolution of approximately 10m. Like most SAR systems, Sentinel-1 radio wave signal is transmitted at a particular orientation in relation to the satellite – this is called ‘polarisation’ – and it can be either vertical or horizontal. A returning signal wave either matches the outgoing signal or will have been switched to the opposite polarisation. This is usually indicated by a pair of letters (either V for Vertical or H for Horizontal) with the outgoing polarisation written first and the received polarisation signal second.

_{Sentinel-1 transmit/receive polarisation}

This is how SAR images are interpreted, as different surfaces scatter and reflect the SAR signal in different ways. The easiest way of showing this is with some examples!

_{Sentinel-1 VV band (top) and Sentinel-2 true colour (bottom) composites from 2022. Courtesy of ESA.}

Here are two satellite imagery composites of Beirut – a Sentinel-2 true colour image below, and a Sentinel-1 VV signal plot above. Comparing the two, you can see that different surfaces respond very differently to the radar signal from the Sentinel-1 satellite. Flat surfaces such as the sea and tarmac (for example the runways at Beirut airport in the bottom left of the image) reflect almost none of the signal back and appear dark, while buildings reflect it very strongly, and so appear bright. Uneven ground and areas of vegetation give a weak response (look at the parks in the city and the bare earth around the airport runways). The response from the rocky mountains is slightly stronger but varies according to the steepness of slope and the direction in which the slope is facing.

_{Sentinel-1 VH band plot from 2022. Courtesy of ESA.}

This imagery shows the strength of the VH signal – this is radiation that has changed its polarisation from vertical to horizontal. In very general terms rougher surfaces tend to switch the signal’s polarisation more. You can see from the imagery that the general pattern is similar, but the response from buildings is a little weaker, as these tend to reflect the signal back without altering the polarisation. Bare ground and areas of vegetation are a little stronger, as these surfaces are more likely to change the signal.

_{Sentinel-1 VV/VH plot from 2022. Courtesy of ESA.}

One way of seeing this more clearly is to simply divide the VV values by the VH values and then plot the result. As VV signal tends to be stronger than VH signal for buildings, these shine out in white. As the opposite is true for bare earth and areas of vegetation, these appear much darker. Look how much easier it is to distinguish areas of human habitation from bare rock in the mountains behind the city on the S1 plot compared to the S2 true colour image!

_{Sentinel-1 RGB plot made up of the three above images (top) and Sentinel-2 true colour (bottom) composite from 2022. Courtesy of ESA.}

We can use these three plots as bands in a RGB composite, just as we do with multispectral imagery. It is easy to see how we could use this sort of data to map the landscape, or even find archaeological sites!

_{A Sentinel-1 RGB composite of part of northern Iraq showing seasonal variation – red is winter, green is spring and blue is summer. Courtesy of ESA.}

There are lots of ways to visualise S1 and other SAR data. This example from northern Iraq plots the VV signal from different times of the year. Data from the winter months forms the red band, spring the green band and summer the blue band! There are most crops in the field in the winter, and these reflect the signal better than bare earth, so the fields appear red. The marshy areas seem to reflect the signal better in spring and summer, probably because more vegetation grows in the water during this part of the year, so these appear greeny-blue.

_{Do you notice the irregular black shapes in the fields – these are ancient settlement mounds! Can you see which ones have had modern houses built on the top of them? Buildings reflect the radar signal much better than the tells so they appear as brighter hotspots.}

We have only scratched the surface of synthetic aperture radar, but now we must move on to look at how radar-derived elevation data can be used in archaeology.