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.

High Spatio-Temporal Resolution CYGNSS Soil Moisture Estimates Using Artificial Neural Networks

“…Furthermore, it can also be computed by substituting Equation 1into Equation (2) with P coh RL = P inc RL (i.e., equating the right-hand-sides of two equations to each other) and obtaining Γ RL (θ i ) as a function of σ RL [35] as follows:…”

“…In order to perform the calculation by using (4), the bistatic received power, P coh RL , needs to be substituted by a CYGNSS observation. Using either ddm_snr or the peak of the power_analog DDM was investigated by previous studies [22,23,27,28,35]. The ddm_snr product accounts for the peak DDM signal-to-noise ratio (SNR) and is computed as 10log 10 (S max /N avg ), where S max is the maximum value (in raw counts) in a single DDM bin and N avg is the average raw noise counts per bin [33].…”

The Use of SMAP-Reflectometry in Science Applications: Calibration and Capabilities

“…For example, TDS-1 SGR-ReSI measurements have been used to characterize ocean winds [6,7], sea surface height [8], soil moisture and vegetation [9,10], wetland inundation [11,12], sea ice detection and concentration [13][14][15][16], sea ice altimetry [17], and sea ice type classification [18]. CYGNSS measurements have been used to observe ocean wind speeds [19][20][21][22], soil moisture [23,24], wetlands inundation characterization and dynamics [25][26][27], and hurricane/tsunami-driven flooding [28,29].…”

Tropical Wetland (TropWet) Mapping Tool: The Automatic Detection of Open and Vegetated Waterbodies in Google Earth Engine for Tropical Wetlands

“…L-band radar imaging systems would provide the best potential for making these assessments [35,36,95]. Although it is encouraging to report accuracies associated with mapping inundated vegetation using TropWet equivalent to those reported by approaches that use L-band imagery [32,35] or GNSS-R [21,23,24], it should be reiterated that TropWet is limited to inundated grassland environments. However, L-band imagery archives are not currently ingested within GEE.…”

“…These examples demonstrate the potential for TropWet to provide policy makers with crucial information to help make national, regional, or continental scale decisions regarding wetland conservation, flood/disease hazard mapping, or mitigation against the impacts of ENSO.Landsat) with microwave systems (e.g., AMSR: Advanced Microwave Scanning Radiometer, SMAP: Soil Moisture Active Passive) [17,20]. Similarly, there is growing evidence that information from global navigation systems (GNSS-R: Global Navigation Satellite System Reflectometry) can provide timely and reliable classifications of wetlands by exploiting signals over both open water and vegetated water surfaces [21][22][23][24]. These approaches provide valuable tools for quantifying wetland dynamics at continental scales with important applications such as characterising greenhouse gas flux [17,18,24,25].…”