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Despite the growing number of remote-sensing products from satellite sensors, mapping of the combined spatial distribution and temporal variability of inundation in tropical wetlands remains challenging. An emerging innovative approach is offered by Global Navigation Satellite System reflectometry (GNSS-R), a concept that takes advantage of GNSS-transmitting satellites and independent radar receivers to provide bistatic radar observations of Earth's surface with large-scale coverage. The objective of this paper is to assess the capability of spaceborne GNSS reflections to characterize surface inundation dynamics in a complex wetlands environment in the Peruvian Amazon with respect to current state-of-the-art methods. This study examines contemporaneous ALOS2 PALSAR-2 L-band imaging radar, CYGNSS GNSS reflections, and ground measurements to assess associated advantages and challenges to mapping inundation dynamics, particularly in regions under dense tropical forest canopies. Three derivatives of CYGNSS Delay-Doppler maps (1) peak signal-to-noise ratio (SNR), (2) leading edge slope, and (3) trailing edge slope, demonstrated statistically significant logarithmic relationships with estimated flooded area percentages determined from SAR, with SNR exhibiting the strongest association. Aggregated Delay-Doppler maps SNR time series data examined for inundated regions undetected by imaging radar suggests GNSS-R exhibits a potentially greater sensitivity to inundation state beneath dense forest canopies relative to SAR. Results demonstrate the capability for mapping extent and dynamic wetlands ecosystems in complex tropical landscapes, alone or in combination with other remote-sensing techniques such as those based on imaging radar, contributing to enhanced mapping of these regions. However, several aspects of GNSS-R observations such as noise level, spatial resolution, and signal coherence need to be further examined.biogeochemical processes such as the generation of atmospheric methane and the outgassing of carbon dioxide [2,3]. These flooding regimes can be impacted by future changes in rainfall, evapotranspiration and land use. The ability to accurately monitor the current state and changes in inundation extent would enable further examination of potential climatological and anthropogenic tipping points in these wetlands regions.Satellite remote sensing is the only practical approach that can provide insight into the spatial and temporal dynamics of wetlands on a large scale on a continuous basis. Although they can offer high spatial resolution and sensitivity to photochemical properties of vegetation, optical sensors are severely limited in capturing inundation dynamics in tropical wetlands due to frequent cloud cover and difficulties detecting sub-canopy inundation [4,5]. Microwave sensors, on the other hand, are not significantly affected by clouds or changing solar illumination and are able to observe processes below vegetation canopy. However, tradeoffs exist in the use of microwave remote sensing with respect to...

The use of global navigation satellite system reflectometry (GNSS-R) measurements for classification of inundated wetlands is presented. With the launch of NASA’s Cyclone Global Navigation Satellite System (CYGNSS) mission, space-borne GNSS-R measurements have become available over ocean and land. CYGNSS covers latitudes between ±38°, providing measurements over tropical ecosystems and benefiting new studies of wetland inundation dynamics. The GNSS-R signal over inundated wetlands is driven mainly by coherent scattering associated with the presence of surface water, producing strong forward scattering and a distinctive bistatic scattering signature. This paper presents a methodology used to classify inundation in tropical wetlands using observables derived from GNSS-R measurements and ancillary data. The methodology employs a multiple decision tree randomized (MDTR) algorithm for classification and wetland inundation maps derived from the phased-array L-band synthetic aperture radar (PALSAR-2) as reference for training and validation. The development of an innovative GNSS-R wetland classification methodology is aimed to advance mapping of global wetland distribution and dynamics, which is critical for improved estimates of natural methane production. The results obtained in this manuscript demonstrate the ability of GNSS-R signals to detect inundation under dense vegetation over the Pacaya-Samiria Natural Reserve, a tropical wetland complex located in the Peruvian Amazon. Classification results report an accuracy of 69% for regions of inundated vegetation, 87% for open water regions, and 99% for non-inundated areas. Misclassification of inundated vegetation, primarily as non-inundated area, is likely related to the combination of two factors: partial inundation within the GNSS-R scattering area, and signal attenuation from dense overstory vegetation, resulting in a low signal.

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