article · International Journal of Remote Sensing
Satellite radar backscatter and altimetry data can track changing water levels across river stretches covered by dense vegetation. In the central Congo Basin, backscattering coefficient values from satellite radar imagery distinguished open water, forest, and aquatic plants such as macrophytes and herbaceous vegetation. Combining these radar backscatter signals with satellite altimetry measurements allowed the creation of detailed multi-temporal water level maps across parts of the main river stem. Validation against independent satellite elevation measurements achieved a root mean square difference of 67.27 centimetres at a 100-metre spatial resolution. The approach successfully captures seasonal water level changes in waterways blanketed by floating or emergent plants. These resulting datasets also offer baseline validation records to prepare for upcoming satellite missions tracking surface water topography.
Monitoring water variations in heavily vegetated rivers like the Congo is difficult with conventional ground gauges. Using radar satellites to track water heights through seasonal aquatic plants improves large-scale hydrological modelling and flood risk assessment. This aids environmental monitoring across remote river basins that lack extensive ground-based hydrological infrastructure.
This applied research provides a calibrated remote sensing method for environmental monitoring organisations, hydrological planners, and satellite mission teams. It enables water level estimation over remote, vegetated waterways without ground stations, serving as validation data for orbital hydrology programmes. The technology sits at an applied validation stage, having demonstrated feasibility in a challenging natural environment, but requires integration into operational flood forecasting or data service platforms before direct commercial deployment.
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Previous studies using synthetic aperture radar (SAR) backscattering coefficients have been used to distinguish vegetation types, to monitor flood conditions, and to assess soil moisture variations over the wetlands. Here, we attempted to estimate spatio-temporal water level variations over the central Congo mainstem covered with aquatic plants using the backscattering coefficients from the Advanced Land Observing Satellite (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR) Scanning SAR (ScanSAR) images and water levels from Envisat altimetry data. First, permanent open water, forest, macrophytes, and herbaceous plants have been classified over the central Congo Basin based on statistics of the backscattering coefficient values. Second, we generated multi-temporal water level maps over part of the Congo mainstem based on the relationship between Envisat altimetry-derived river-level changes and PALSAR ScanSAR backscattering coefficient variations. Finally, the water level maps were validated with Ice, Cloud and land Elevation Satellite (ICESat) altimetry-derived water levels. We obtained overall root mean square difference (RMSD) of 67.27 cm at 100-m scale resolution of PALSAR ScanSAR. Our study shows that we can obtain reasonable estimates of water levels of the rivers covered with seasonally floating or emergent macrophytes from backscattering coefficients. Furthermore, it is expected that the generated water level maps can be used as a ‘true’ data set to perform pre-launch study of the Surface Water Ocean Topography (SWOT) mission to be launched in 2021.
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DOI: 10.1080/01431161.2017.1371867
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