# Classifying Inundation in a Tropical Wetlands Complex with GNSS-R
## 1 Introduction
For example, using a variety of existing maps, data, and information, the World Wildlife Fund (WWF) and the Center for Environmental Systems Research, University of Kassel, Germany created the Global Lakes and Wetlands Database (GLWD) ([PERSON] and [PERSON], 2016), which is a static product that includes an estimate of wetland size, but does not report seasonal changes. Other available global flood detection maps, such as the Dartmouth Flood Observatory, used data from the radar scatterometer QuikSCAT while it was operational, and calculated polarization ratios to measure wetland flooding and drying at weekly intervals, but with a spatial resolution of \(\sim\)25 km, which underestimates small areas of flooded vegetation ([PERSON] and [PERSON], 2016). As tropical wetlands play a critical role in the cycling of carbon, there is a need to reliably characterize surface inundation in these biomes, including regions of inundation beneath dense vegetation. Multiple studies have shown that L-band spaceborne SAR sensors, largely because of their ability to provide observations of inundation beneath vegetation canopies, provide the most suitable radiometric frequency of choice currently available on remote sensing satellites for mapping flooded forests ([PERSON] and [PERSON], 2016; Gers, 2017; Gers, 2017; Gers, 2017). Nevertheless, focused field campaigns combining airborne L-band SAR imagery with mappings of surface inundation collected in situ have elucidated limitations in the ability of L-band radar backscatter to map surface water in dense tropical forest environments ([PERSON] and [PERSON], 2016). Global navigation satellite systems-reflectometry (GNSS-R) measurements provide an emerging and promising capability for remote sensing of inundated wetlands. With the launch of the Cyclone Global Navigation Satellite System (CYGNSS) mission in late 2016 ([PERSON] and [PERSON], 2017; Gers, 2017), high-repeat, bistatic L-bandradar measurements are being collected over the tropics, providing a new type of sustained radar observing capability. CYGNSS consists of a constellation of eight satellites, each capturing the forward scattered signal associated with the signals transmitted from the constellation of global positioning system (GPS) satellites and reflected from Earth's surface. Each satellite in the CYGNSS constellation is capable of receiving the forward scattered energy from up to four GPS satellites simultaneously. With an orbital inclination of about 35\({}^{\circ}\), the CYGNSS observatory provides nearly gap-free coverage of Earth's tropical latitudes, with a mean revisit time of seven hours and a median of three hours. CYGNSS functions with a bistatic receiving configuration, receiving the forward scattered signals from GPS satellites after they are reflected from the Earth's surface, as opposed to monostatic radar sensors that transmit a microwave pulse and receive the associated backscattered signal from the Earth's surface. CYGNSS was designed to provide measurements of surface wind speed over the tropical oceans and support improvements in tropical cyclone monitoring and forecasting. However, CYGNSS collections also include the land surface, and provide opportunities for discovering science associated with characterization of tropical terrestrial environments. The use of GNSS-R measurements for wetlands detection was first established by [PERSON] et al. (2017), where the authors showed the potential capability of GPS reflections to delineate and map inundated wetland extent using measurements from the TechDemoSat (TDS-1) satellite ([PERSON] et al., 2017). In Reference ([PERSON] et al., 2017), the TDS-1 peak signal to noise ratio (SNR) and its strength were analyzed qualitatively across rice fields in the Ebro delta in Catalonia, Spain, and across regions of the Mississippi basin to demonstrate GNSS-R ability to detect surface inundation.