# Classifying Inundation in a Tropical Wetlands Complex with GNSS-R
## 1 Introduction  
Wetlands cover a small portion (less than 5%) of Earth's ice-free land surface [1], yet they have major impacts on global biogeochemistry, hydrology, and biological diversity. They are the largest natural source of atmospheric methane, with their extent and inundation variability playing a large role in ecosystem dynamics, contributing roughly 20-40% of global methane emissions [2]. However, methane emissions from wetlands remain one of the principal sources of uncertainty in the global atmospheric methane budget, primarily because the distribution and evolution of atmospheric CH\({}_{4}\) sources and sinks are poorly resolved [3]. Climate and anthropogenic-driven changes to wetlands can potentially create significant feedbacks to atmospheric methane through effects on methanogenesis and release of large pools of soil carbon (Kassel and Gers, 2016; Gers, 2017). Despite the importance of these ecosystems in the global cycling of carbon and water and to current and future climate, their extent and dynamics remain poorly characterized and modeled, primarily because of the difficulty associated with characterizing the spatio-temporal complexity of these environments across vast, remote regions (Kassel and Gers, 2016; Gers, 2017; Gers, 2017; Gers, 2017). One of the main limitations in large-scale mapping of wetland ecosystems from Earth-orbiting remote sensing observations, especially in the tropics, is the almost constant presence of clouds. Optical instruments such as the Moderate Resolution Imaging Spectroradiometer (MODIS) have been used to map wetlands' extent and dynamics at spatial resolutions of 500 m-1 km, and with temporal repeats of up to 8 days (Kassel and Gers, 2016; Gers, 2016; Gers, 2016). However, cloud coverage is even more persistent during the high flood season, and acquiring cloud-free imagery becomes particularly difficult for weeks or even months at a time. Passive and active microwave remote sensing have provided viable capabilities for mapping wetland ecosystems and their inundation dynamics. Microwave sensors can observe Earth's surface day or night, minimally affected by cloud cover. In addition, microwave signals can penetrate vegetation canopies to some degree, depending on wavelength and vegetation density, providing assessment of below-canopy standing water conditions. The lower the microwave frequency (the longer the wavelength), the greater the penetration through the medium (vegetation) and the better the capability for mapping surface inundation beneath vegetation, which is especially difficult under dense forests. Microwave remote sensing technology has developed a legacy for characterizing inundated wetlands (Kassel and Gers, 2016; Gers, 2016; Gers, 2017; Gers, 2017). Passive microwave sensors (e.g., Special Sensor Microwave Imager, SSM/I, or Soil Moisture Active-Passive, SMAP) and radar scatterometers (e.g., Advanced Scatterometer, ASCAT, or, Quick Scatterometer, QuikSCAT) tend to have high temporal repeat (1-3 days) but coarse spatial resolution (\(\sim\)25-50 km), the latter limiting suitability for characterizing inundation dynamics in heterogeneous landscapes. On the other hand, synthetic aperture radars (SARs; e.g., Sentinel-1 A and B, PALSAR-2) are imaging sensors with high spatial resolution (\(\sim\)10-50 meters) but limited temporal fidelity at global scales, with the Sentinel-1 SARs recently supporting observations at timescales of \(\sim\)6-12 days. Several global-scale, microwave-based wetlands inundation datasets have been developed (Gers, 2017; Gers, 2017; Gers, 2017; Gers, 2017). Such datasets have been produced from coarse resolution active and passive microwave sensors with various degrees of temporal fidelity, providing dynamic, multi-year assessments of landscape inundated area fraction globally. Static datasets of wetlands distribution are also available.