Automatic Detection of Inland Water Bodies along Altimetry Tracks for Estimating Surface Water Storage Variations in the Congo Basin

Abstract

Surface water storage in floodplains and wetlands is poorly known from regional to global scales, in spite of its importance in the hydrological and the carbon balances, as the wet areas are an important water compartment which delays water transfer, modifies the sediment transport through sedimentation and erosion processes, and are a source for greenhouse gases. Remote sensing is a powerful tool for monitoring temporal variations in both the extent, level, and volume, of water using the synergy between satellite images and radar altimetry. Estimating water levels over flooded area using radar altimetry observation is difficult. In this study, an unsupervised classification approach is applied on the radar altimetry backscattering coefficients to discriminate between flooded and non-flooded areas in the Cuvette Centrale of Congo. Good detection of water (open water, permanent and seasonal inundation) is above 0.9 using radar altimetry backscattering from ENVISAT and Jason-2. Based on these results, the time series of water levels were automatically produced. They exhibit temporal variations in good agreement with the hydrological regime of the Cuvette Centrale. Comparisons against a manually generated time series of water levels from the same missions at the same locations show a very good agreement between the two processes (i.e., RMSE ≤ 0.25 m in more than 80%/90% of the cases and R ≥ 0.95 in more than 95%/75% of the cases for ENVISAT and Jason-2, respectively). The use of the time series of water levels over rivers and wetlands improves the spatial pattern of the annual amplitude of water storage in the Cuvette Centrale. It also leads to a decrease by a factor of four for the surface water estimates in this area, compared with a case where only time series over rivers are considered.

Water Resources in Africa under Global Change: Monitoring Surface Waters from Space

“…For instance, the estimates of Yuan et al ( 2017b ) can differ up to 50% (average of ~ 30%) with the estimates from Lee et al ( 2015 ). Frappart et al ( 2021 ) suggested that the densification of the VS network over the Cuvette Centrale by including water elevation variations over the floodplains could impact the surface water storage by a factor four as compared to if only VSs over the rivers are used.…”

Representation of Spatial Variability of the Water Fluxes over the Congo Basin Region

“…The Congo Basin is one of the major river basins in the tropics. The atmospheric model datasets and satellite observations [ 52 , 53 ] play an important role in characterizing the basin’s hydro-climatological behavior since in situ hydrometeorological measurements are sparse in this region. However, it is not clear how the model resolution impacts the analysis and the understanding of the Congo Basin’s hydrology.…”

Les forêts du bassin du Congo : État des Forêts 2021

“…Des travaux sont en cours pour améliorer la cartographie des types de forêts et des tourbières. Les scientifiques du CIRAD exploitent des séries temporelles d'images acquises par des capteurs SAR (celui de l'altimètre Poséidon de Jason-2, développé en commun par la NASA et le CNES et le capteur SAR PALSAR-2 du satellite ALOS de JAXA), ce qui va leur permettre de faire une nouvelle description des différents types de forêts en fonction de leur inondation au fil du temps (Betbeder et al 2013 ;Frappart et al 2021). On a cherché par le passé à caractériser les tourbes à l'aide de données RADAR, notamment en détectant la formation de tourbières bombées (Siegert and Jaenicke 2008).…”