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.
Highlights
This article is an open access articleFloodplains and wetlands cover at least 12.1 × 106 km2 (~8%) of the land surfaces of the Earth [1,2]
Where H is the height of the center of mass of the satellite above the ellipsoid estimated using the Precise Orbit Determination (POD) technique, R is the altimeter range, ∆Rpropagation and
Contrary to SAR or multi-spectral images, this type of sensor seems to be unable to discriminate between open water and water under vegetations in the floodplains
Summary
Floodplains and wetlands cover at least 12.1 × 106 km (~8%) of the land surfaces of the Earth [1,2] They play a major role in the water cycle through river flow variability, flood mitigation, groundwater recharge and water quality improvement [2,3,4]. They were identified as one of the most productive ecosystems as well as a major contributor of biodiversity within a landscape [5,6,7,8,9,10]. Before the launch of NASA/CNES Surface Water and Ocean Topography (SWOT) in 2022, which will provide surface water elevation over inland water bodies at a spatial resolution of 100 m [17], anomalies of surface water storage are currently derived by: (i) estimating water level changes using an interferometric synthetic aperture radar (SAR) (InSAR) [18,19], (ii) filling a digital elevation model (DEM) with surface water extent estimates from remotely sensed observations through a hypsometric curve [20,21], (iii) combining surface extent products derived from satellite images with radar altimetry based water levels [22,23], or (iv) solving the water balance equation combining various remotely sensed observations (i.e., anomalies of terrestrial water storage (TWS) from the Gravity Recovery And Climate Experiment (GRACE), water levels from radar altimetry, rainfall from Global Precipitation Climatology
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