# GNSS-R Observations of Marine Plastic Litter in a Water Flume: An Experimental Study
## 4 Discussion  
Table 4 summarizes the main descriptors of all scenarios, and includes a brief description of their main parameters. Numerical results (dB or degrees) are color-coded according to their detectability (gray: not detectable, blue/red: detectable negative/positive changes). As it can be appreciated, while for clean water there are detectable changes in the mean received power as a function of the elevation angle, and most notable a RR polarization, all changes in the average reflection coefficient with respect to their corresponding clean water references (same conditions) are undetectable; there are only small changes in the phase. However, this situation is not encountered in spaceborne GNSS-R receivers, as they have to perform long incoherent averages, and phase information is lost. In addition to the difficulties to detect the small changes in magnitude, absolute calibration errors of GNSS-Reflectometers may prevent the correct interpretation of the results. Under the presence of litter, in most cases, the RL reflectivity fluctuations increase (e.g., bottles, nets and both, pellets, Styrofoam, caps and lids... ), but in some other cases it decreases or it decreases for some elevation angles and/or rms surface height (e.g., JONSWAP SWH = 9 cm, and elevation angle = 45\({}^{\circ}\)). At RR polarization, the standard deviation, kurtosis and skewness tend to decrease. The behavior for the phase follows that of the reflectivity fluctuations. The kurtosis behavior is very erratic (either it increases or decreases), but changes are significant. For bottles and fixed net, food wraps and bags, straws, and pellets, the phase of the reflection coefficients is linearly correlated. In Section 3 it was discussed that debris pieces are difficult to observe, and even with the sensitivity of our system, and holding the debris in the middle of the Fresnel zone in a flat water surface, they were not directly detectable. However, when the water is roughened, they may either flatten the surface (Figure 12, red ellipses), as in the case of nets or plastic bags, while in others they can create small waves, thus increasing the short waves roughness (Figure 12, blue ellipses). This is illustrated in Figure 13: when a long wave crest (almost vertical long glittering strip) reaches an ensemble of debris (in this case bottles and plastic net) scattered waves are originated are depart from them describing concentric wavefronts, as illustrated in Figure 13, translating into small fluctuations in the received signal power (zooms in Figure 12). Table 5 summarizes the sensitivities of the reflectivity amplitude and phase for all scenarios. The most promising observable is the fluctuation (standard deviation) of the reflectivity, which decreases sharply when there are waves, and increases a bit when capillary waves are present. Not surprisingly, apart from the changes when the water surface becomes rough (first row), the types of marine litter than offer the highest chances to be detected are bottles and fixed net, food wraps and bags, and nets, as they are the ones that dampen the most the water surface (long waves) despite some of them also induce smaller concentric waves when a wave crest impinges over them (Figure 13). The detectable changes that can be distinguished as follows: (1) in some cases in the std, although it requires a very good absolute calibration, or (2) in the kurtosis of the amplitude and/or the phase, and (3) in some occasions, in the skewness as well. In this sense, the proposed observables: the kurtosis and the skewness of the amplitude PDF (and the phase PDF as well, if feasible at all) can provide an alternative, robust means to detect these anomalies in the received signal, and potentially detect marine plastic litter. Using the [PERSON] (1954) [17] model, the effect of surface roughness and the presence of oil on the surface was incorporated in the Zavorotny-Voronovich equation. The peak of the Delay Doppler Map (DDM) was then computed as a function of elevation angle, and wind speed.