# Classifying Inundation in a Tropical Wetlands Complex with GNSS-R
## 1 Introduction
In particular, Reference ([PERSON] et al., 2017) showed how the strength of the signal increased several dB as the signal track passed over forested/shrub wetlands, emergent wetlands, and a lake. The strongest signal response was observed for open water, indicating a coherent reflected signal. In Reference ([PERSON] et al., 2017), the authors used GPS reflections data observed by NASA's SMAP mission, with the SMAP radar receiver tuned to 1227.45 MHz (GPS L2C band), to do a first assessment of the relationship between SNR and percentage of inundation within the signal footprint over the Amazon basin. They analyzed reflections data observed by the SMAP radar in receive mode (SMAP-R), and also TDS-1, to investigate the ability of GNSS-R to sense inundation at small scales and quantitatively establish a relationship between the variations of peak SNR under inundated conditions. Other research has focused on using CYGNSS for detecting inundation over urban environments as a result of Hurricane Harvey affecting the city of Houston, in the state of Texas in the United States in 2017, and Hurricane Irma, affecting Cuba also in 2017 ([PERSON] et al., 2017), or using CYGNSS data to monitor flood inundation during typhoon and extreme precipitation events in southeast China in 2017 ([PERSON] et al., 2017). In [PERSON] et al. (2017), CYGNSS data were analyzed in combination with L-band SAR imagery from the Japanese Advanced Land Observing Satellite (ALOS-2) phased-array L-band synthetic aperture radar (PASAR-2) collections ([PERSON] et al., 2017) to assess GNSS-R capabilities as compared with L-band SAR over Pacaya-Samiria National Reserve, a large tropical wetlands complex in the Peruvian Amazon. A major field expedition was carried out to collect ground reference data, including forest biometry and inundated area extent, to support the remote sensing analysis associated with that comparative analysis. GNSS-R capabilities show that peak signal to noise ratio, leading edge slope, and trailing edge slope have statistically significant logarithmic relationships with estimated flooded area percentages determined from SAR, with the peak signal to noise ratio characterized as the most robust observable ([PERSON] et al., 2017). Time series data examined for inundated regions undetected by SAR suggested that GNSS-R measurements have a greater sensitivity to inundation conditions under dense vegetation canopies as compared to SAR. Results in [PERSON] et al. (2017) show that the median peak signal to noise ratio value observed is \(\sim\)14 dB for open water, 12.7 dB for flooded non-forest areas, 8.5 dB for flooded forest areas, and 2.2 dB for non-flooded areas. The results show an inverse relationship between the observed peak signal to noise ratio and the amount of canopy attenuation above the flooded surface. The conclusion of the study indicated that a classification algorithm could be built from GNSS-R observables ([PERSON] et al., 2017). In the present study we have developed a classification algorithm exploiting the sensitivities of multiple GNSS-R observables. In this paper, we have developed a classification methodology based on a combination of CYGNSS-derived GNSS-R observables and ancillary data. The selected area under study is the Pacaya-Samiria National Reserve, located in the Peruvian Amazon. It covers over 2 million hectares and is the most extensive wetlands preserve in the Amazon basin. Eightly percent of this region floods during the rainy season, which spans from January to April. We evaluated several GNSS-R observables as inputs to an inundation classification algorithm, including: peak SNR, trailing edge slope, leading edge slope, the width of the waveform, and a generalized linear observable.