The Synthetic Aperture Radar or SAR sensors are active sensors. This means that they emit a signal (called a wave front) in the direction of the surface being observed and register the part of the signal that is reflected back by the surface. This enables them to register mathematically complex images. For each pixel one gets information about the amplitude (signal intensity), which is linked directly to the reflectivity and the geometry of the observed area, and about the phase, which is linked to, among other things, the optical path taken by the radar wave between the sensor and observed surface.
During the image acquisition period the two satellites, ERS-1 and ERS-2, were placed on the same orbit so as to cover the same area 24 hours apart. The great stability of the two satellites’ orbits and a computerised superimposition process enabled this mission, which was dubbed the ‘Tandem Mission’, to get information about the phase shifts between the two images’ wave fronts. Phasimetry or interferometry covers the measurement techniques based on radar image phase information.
The following products are used:

• The interferogram measures the relative difference in the optical paths of the two SAR images’ wave fronts. It enables one to determine the third dimension of the scenes observed and to generate altimetric images of the terrain (images of the terrain’s elevation). These images were not used directly in our project

• The intensity image is proportional to the intensity of the signal sent back by the target. It is not an interferometric product per se, for it is calculated separately for each of the two images in the pair.

• The coherence image for its part, measures the degree of correlation between the two complex images. This measurement can be used to determine the target’s characteristics.

Accurate to a few centimetres!
The presence of vegetation weakens the correlation of the echoes registered by the Tandem satellites. The more lush the vegetation, the greater this lack of correlation, which is expressed by lower coherence. This particularity is used to measure the vegetation.

Indeed, the strong correlation between the mean coherence values for each plot of wheat and the heights of the plants measured in the field enabled us to construct a model for estimating the wheat field’s height by a simple linear regression. This model’s accuracy is such that it can be used to measure crop height with an absolute mean error of only ±7 cm!

We also saw a strong correlation between coherence and plant height for sugar beets, potato plants, and maize. This finding shows that it is possible to develop similar models for determining plant height for these crops.

As the figure of the potato field shows, ground coverage by the crop is also closely correlated with the level of interferometric coherence. The cover could thus be estimated by simply measuring coherence.

You can even see the tractor!
Interferometric coherence is strongly influenced by changes in the target’s geometry between the Tandem satellites’ data acquisition times. Any activity that changes the soil structure or vegetation’s structure, for example, tilling the soil, ploughing, harvesting, etc., will cause the coherence to drop and thus be detectable. So, based on this principle, it was possible to observe the sowing of a few plots of sugar beets on 5 April 1996, thanks to the corresponding coherence image.

Here we see low coherence for Plots 2, 3 and 6. These fields were sown between the two Tandem acquisitions and the resulting change in the soil structure explains the drop in coherence. Fields 1 and 5 were sown later and show a higher level of coherence. However, the most impressive case is that of Plot 4, where we see two different hues. These two different levels of coherence indicate that the field was being sown as the second image in the pair was being acquired, for the soil structure of half of the plot (dark part) had already been altered. We could thus even tell where the tractor was at the time of the satellite’s pass!

Magenta maize
Interferometric coherence images have been used to recognise crops with excellent results. Two types of combination of images were analysed in our study, that is, a combination of coherence images taken at different times and a combination of coherence and intensity images for a pair of dates.

We must point out that we did not consider all crops in our study. So, while all the maize fields show up magenta, we cannot conclude that all magenta fields are maize. Similarly, all blue plots of land are not necessarily grain fields.

This figure shows a colour composite made from the Tandem Mission’s paired images of 13 and 14 June 1996.

The strong contrast in this image may be interpreted as follows:

- The town of Gembloux (in the bottom left-hand corner) and a few villages scattered around the image appear in yellow because of their strong signal intensities and coherence.

- The railroad is also clearly identified by the yellow line linking Ottignies and Gembloux, amongst other places.

- The forests are coloured green. This is the result of their low coherence, the small changes in intensity between the two acquisitions, and their relatively high average intensities.

- The farmland offers a great diversity of colours.

- The sugarbeet and potato fields appear in green and show low coherence, strong intensity, and a relatively large drop in intensity between the first and second images taken by the Tandem satellite pair.

- The wheat and winter barley are seen in blue. This corresponds to low coherence, very low intensity, and a smaller change in intensity than that seen for the sugarbeets.

- In mid-June the maize fields are less developed than the other crops. They thus exhibit a higher level of coherence. Their signal intensities are low and change little between acquisitions. They are magenta in the image.