# Classifying Inundation in a Tropical Wetlands Complex with GNSS-R
## 6 Conclusions  
We successfully applied an inundation classification through a new implementation strategy, based on a multiple decision tree randomized (MDTR) algorithm and using CYGNSS data over seasonally inundated wetlands in the Pacaya-Samiria region of the Peruvian Amazon. Training and validation data were obtained from PALSAR-2-based reference inundation maps that were developed and evaluated with ground measurements collected during a field campaign in the region [10].  
Different combinations of GNSS-R observables were analyzed by building single decision trees (single DTs) from a standard CART classification algorithm (Table 3) and analyzing relative performance, selecting those that increased classification accuracy (corrected peak SNR, TES, LES, \(\Delta\), GLO 1) and discarding those that decreased it (GLO 2 and GLO 3). Among the observables selected, the combination of TES and corrected peak SNR resulted in a higher classification accuracy than using corrected peak SNR alone, with an increase of 5.3% for OW and 5% for FV. Observables LES, \(\Delta\), and GLO 1 together had lower impact (2% for OW, 1.5% for FV, and 1% for NF), but still contributed towards increasing classification accuracy. The combination of corrected peak SNR, TES, LES, \(\Delta\), GLO 1 and the ancillary datasets (DEM and biomass) into a single DT resulted in a classification accuracy of 60% for OW, 60.5% for FV, and 94.7% for NF.  
Once the set of GNSS-R observables was selected (corrected peak SNR, TES, LES, \(\Delta\), GLO 1), a GNSS-R-based inundation classification was generated using the MDTR algorithm. The randomization (input and data samples) and the extension (2500 trained single DT CART-based trees) of our algorithm allowed an increase in classification performance. The classification accuracy obtained was 86.5% for OW, 69% for FV, and 99% for NF. The use of the MDTR algorithm scheme versus the single DT scheme resulted in an increase of 26.5% for OW, 9% for FV, and 4.3% for NF.  
The work presented here demonstrates the utility of GNSS-R observables from CYGNSS data and ancillary datasets to characterize wetland inundation. Bistatic radar signals from GPS satellites or other signals of opportunity have great potential to fill observational gaps from other sensors, and potentially enhance and extend their utility for characterization of these critical biomes.  
**Author Contributions:** Conceptualization, N.R.-A. and E.P.; Formal analysis, N.R.-A.; Funding acquisition, E.P.; Investigation, N.R.-A., K.J., E.P. and K.C.M.; Methodology, N.R.-A. and E.P.; Project administration, E.P.; Software, N.R.-A.; Supervision, E.P.; Validation, N.R.-A. and K.J.; Visualization, N.R.-A.; Writing--original draft, N.R.-A.; Writing--review & editing, N.R.-A., K.J., E.P. and K.C.M.  
**Funding:** This research was funded by the Jet Propulsion Laboratory, under the R&TD program. The research carried out at the Jet Propulsion Laboratory (JPL), California Institute of Technology, was supported under a contract with the National Aeronautics and Space Administration (NASA).  
**Acknowledgments:** Portions of this work were conducted within the framework of the ALOS Kyoto & Carbon Initiative. PALSAR-2 data were provided by JAXA/EORC. Portions of this effort were supported by the NASA Earth and Space Science Fellowship (NESSF) program under grant 80 NSSC17K0387. We would like to thank JAXA/EORC and NESSF for the contribution to make this work complete. Also, we would like to thank CYGNSS mission for providing L1 dataset, v.2.1.  
**Conflicts of Interest:** The authors declare no conflict of interest.