Conference Agenda
Overview and details of the sessions of this conference. Please select a date or location to show only sessions at that day or location. Please select a single session for detailed view (with abstracts and downloads if available).
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Daily Overview |
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📌Poster Session and Networking Aperitivo 🍷 Location: Lower Lobby | |
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Novel insights of the water cycle and flood dynamics using BIOMASS Cal/Val data 1: Luxembourg Institute of Science and Technology, Luxembourg; 2: INRAE; 3: Collecte Localisation Satellites Monitoring flood dynamics within forested and densely vegetated regions remains a major challenge due to the limited penetration of conventional radar frequencies through canopy layers. This study investigates the comparative potential of P-band synthetic aperture radar (SAR) observations from the ESA BIOMASS mission and C-band data from Sentinel-1 for detecting flooded areas beneath vegetation cover. The analysis is conducted within the BIOMASS Calibration and Validation (Cal/Val) framework, aiming to assess how multi-frequency SAR observations can enhance hydrological and ecohydrological monitoring in complex environments. P-band radar, operating at longer wavelengths, provides deeper penetration through vegetation canopies and upper soil layers compared to C-band sensors. This characteristic enables BIOMASS to capture hydrological features that are often obscured in Sentinel-1 imagery, such as subsurface and under-canopy inundation. By comparing backscatter and polarimetric responses from BIOMASS and Sentinel-1, this study quantifies differences in sensitivity to vegetation water content, soil moisture, and surface water extent. Temporal and spatial variations in SAR backscatter are analyzed to evaluate the complementary roles of both frequencies in characterizing flood dynamics, particularly in forested floodplains and wetland ecosystems. The comparison is extended to SWOT High-Rate products (with Ka-band Interferometry) providing water surface elevation and extent. Flood detection methods, including change detection, coherence analysis, and polarimetric decomposition, are applied to both datasets. The integration of these techniques enables improved discrimination between open water, flooded vegetation, and dry land surfaces. In particular, change detection using time-series P-band data enhances the identification of inundated zones beneath vegetation, while Sentinel-1 contributes high temporal resolution for near-real-time monitoring. The combination of multi-frequency SAR data, supported by ancillary datasets such as optical imagery, topography, and in-situ hydrological measurements, allows for a comprehensive assessment of surface and subsurface water processes. | |
