Tools developed to map Flood Extent by utilizing GNSS-R

Brandi Downs (downs.152@buckeyemail.osu.edu)¹, Albert J. Kettner², Bruce Chapman³, G. Robert Brakenridge², Andrew O’Brien¹, and Cinzia Zuffada<sup>3

1</sup>) ElectroScience Laboratory at The Ohio State University, Columbus
²) DFO Flood Observatory, INSTAAR, University of Colorado Boulder
³) Jet Propulsion Laboratory, California Institute of Technology, Pasadena

When using this materials from this study, please site: B. Downs, A. J. Kettner, B. Chapman, G. Robert Brakenridge, A. O’Brien and C. Zuffada, 2023. Assessing the Relative Performance of GNSS-R Flood Extent Observations: Case Study in South Sudan, in IEEE Transactions on Geoscience and Remote Sensing, doi: 10.1109/TGRS.2023.3237461.

Here we present GNSS-R measurements from the Cyclone Global Navigation Satellite System (CYGNSS) that were used to retrieve surface water extent. CYGNSS is a constellation of 8 microsatellites in low Earth orbit carrying GPS-reflection processing receivers and down-looking antennas that measure several bistatic reflections at a time along varying tracks, sampling the latitudinal bandwidth +/- 38 degrees everywhere (Ruf et al., 2016). Using recent flooding in South Sudan as a case study (Figure 1), we have quantified the performance of using GNSS-R measurements map surface with a set of representative operational products (VIIRS, MODIS and C-band Sentinel-1). Our applied method detects 35.4% more surface water than Sentinel-1 while the VIIRS- and MODIS-based products underestimate by 4.8 and 83.7%, respectively (see figure 2).

Figure 1. Overview of the study area

Figure 2. CYGNSS data accumulated over one day (a), one week (b), and one month (c), for South Sudan for October 2021 in order to illustrate the quasirandom track-based sampling of GNSS-R. (d) CYGNSS reflected power for the month of May, 2021, when flooding just started.

Figure 3. Surface water extent maps during the flooding in October 2021 detected using MODIS (a), VIIRS (b), Sentinel-1 (c), and CYGNESS (d).

future work may include: a) enhancing current operational flood products and assist decision-makers and emergency; b) development of an operational methodology for flood inundation detection and mapping utilizing GNSS-R measurements; c) extending the analysis to include additional time periods and regions to understand how these methods and parameters respond to changing seasons, soils, and vegetation; d) refining thresholds; and e) producing a global flood product to estimate potential amount and fraction of currently undetected.

Scripts developed for the presented study can be downloaded at GitHub: https://github.com/BrandiDowns/cygnss-flood-monitoring

1. Downs, B., Kettner, A.J., Chapman, B., Brakenridge, G.R., O’Brien, A., and Zuffada, C., 2023. Assessing the Relative Performance of GNSS-R Flood Extent Observations: Case Study in South Sudan, in IEEE Transactions on Geoscience and Remote Sensing, doi: 10.1109/TGRS.2023.3237461.
2. Ruf, C. S., Atlas, R., Chang, P. S., Clarizia, M. P., Garrison, J. L., Gleason, S., Katzberg, S. J., Jelenak, Z., Johnson, J. T., Majumdar, S. J., O’brien, A., Posselt, D. J., Ridley, A. J., Rose, R. J., and Zavorotny, V.U., 2016. New Ocean Winds Satellite Mission to Probe Hurricanes and Tropical Convection, Bulletin of the American Meteorological Society, 97(3), 385-395. 10.1175/BAMS-D-14-00218.1

This research was supported by NASA ROSES 2017 Project NNH17ZDA001N, by award 80NM0018D0004 to Jet Propulsion Laboratory, California Institute of Technology, and award 80NSSC18K0426 to the University of Colorado.