Multi-frequency and polarimetric radar backscatter signatures for discrimination between agricultural crops at the Flevoland experimental test site

We describe the calibration and analysis of multi-frequency, multi-polarization radar backscatter signatures over an agriculture test site in the Netherlands. The calibration procedure involved two stages: in the first stage, polarimetric and radiometric calibrations (ignoring noise) were carried out using square-base trihedral corner reflector signatures and some properties of the clutter background. In the second stage, a novel algorithm was used to estimate the noise level in the polarimetric data channels by using the measured signature of an idealized rough surface with Bragg scattering (the ocean in this case). This estimated noise level was then used to correct the measured backscatter signatures from the agriculture fields. We examine the significance of several key parameters extracted from the calibrated and noise-corrected backscatter signatures. The significance is assessed in terms of the ability to uniquely separate among classes from 13 different backscatter types selected from the test site data, including eleven different crops, one forest and one ocean area. Using the parameters with the highest separation for a given class, we use a hierarchical algorithm to classify the entire image. We find that many classes, including ocean, forest, potato, and beet, can be identified with high reliability, while the classes for which no single parameter exhibits sufficient separation have higher rates of misclassification. We expect that modified decision criteria involving simultaneous consideration of several parameters increase performance for these classes.