Classifying Inundation in a Tropical Wetlands Complex with GNSS-R

Abstract

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.