Analysis of CYGNSS coherent reflectivity over land for the characterization of pan-tropical inundation dynamics - Archive ouverte HAL Access content directly
Journal Articles Remote Sensing of Environment Year : 2022

Analysis of CYGNSS coherent reflectivity over land for the characterization of pan-tropical inundation dynamics

(1) , (2) , (3) , (4) , (4)


The monitoring of flood and wetland dynamics at global scale is hampered by several limitations, including a reduced data availability in tropical areas due to the presence of clouds affecting visible and infrared imagery, or low spatial and/or temporal resolutions affecting passive and active microwave Earth Observation (EO) data. As a consequence, surface water extent estimates and their temporal variations remain challenging especially in equatorial river basins. Global Navigation Satellite System Reflectometry (GNSS-R) L-band signals recorded onboard Cyclone GNSS (CYGNSS) mission, composed of 8 Low Elevation Orbit (LEO) satellites, provide infor-mation on surface properties at high temporal resolution from 2017 up to now. CYGNSS bistatic observations were analyzed for detecting permanent water and seasonal floodplains over the full coverage of the mission, from 40 degrees S to 40 degrees N. We computed CYGNSS reflectivity associated to the coherent component of the received power, that was gridded at 0.1 degrees spatial resolution with a 7-day time sampling afterwards. Several statistical metrics were derived from CYGNSS reflectivity, including the weighted mean and standard deviation, the median and the 90th percentile (respectively rmean, rstd, rmedian and r90%) in each pixel. These parameters were clustered using the K -means algorithm with an implementation of the Dynamic Time Warping (DTW) similarity measure. They were compared to static inundation maps, and to dynamic estimations of surface water extent both at the global and regional scales, using the Global Inundation Extent from Multi-Satellites (GIEMS) and MODIS-based products. The difference between r90% and rmedian shows the best sensitivity to the presence of water. The river streams and lakes are correctly detected, and a strong seasonality is identified in CYGNSS reflectivity over the largest floodplains, with the exception of the Cuvette Centrale of Congo which is covered by dense vegetation. This seasonal reflectivity signal correlates well with inundation maps: Pearson's correlation coefficient between rmedian and surface water extent from both GIEMS and MODIS is over 0.8 in the largest floodplains. The spatial patterns of reflectivity are consistent with static inundation maps: at the time of maximum flooding extent, a spatial correlation coefficient around 0.75 with rmedian is obtained for several basins. We also evaluated the dependence of CYGNSS-derived clusters and reflectivity on the dominant land cover type and on the density of Above Groud Biomass (AGB) in the pixel. On the one hand, misclassifications of flooded pixels were observed over vegetated regions, probably due to uncertainties related to the attenuation by the vegetation in both CYGNSS and reference datasets. On the other hand, flooded pixels with a mean AGB up to similar to 300 Mg/ha were correctly detected with the clustering. High reflectivity values are also observed over rocky soils in arid regions and create false alarms. Finally, strong winds on large lakes cause surface roughness, and lower reflectivity values are observed in this case which weaken the detection of open water. While these constraints are to be taken in account and corrected in a future model, a pan-tropical mapping of surface water extent dynamics using CYGNSS can be envisaged.
Not file

Dates and versions

hal-03845659 , version 1 (09-11-2022)



Pierre Zeiger, Frédéric Frappart, José Darrozes, Catherine Prigent, Carlos Jiménez. Analysis of CYGNSS coherent reflectivity over land for the characterization of pan-tropical inundation dynamics. Remote Sensing of Environment, 2022, 282, pp.113278. ⟨10.1016/j.rse.2022.113278⟩. ⟨hal-03845659⟩
0 View
0 Download



Gmail Facebook Twitter LinkedIn More