Conference Agenda

Desert regions such as Bir Safsaf in Egypt conceal a complex palaeohydrological history, with ancient riverbeds (palaeochannels) buried beneath arid surfaces. Mapping these features is essential for reconstructing past hydrological networks, understanding palaeoclimatic evolution, and supporting resource management in hyper-arid environments. However, their detection is challenging due to sediment cover, surface roughness, and the limited effectiveness of optical remote sensing.

This study investigates the use of polarimetric Synthetic Aperture Radar (SAR) data and differential entropy analysis to identify and map palaeochannels in the Bir Safsaf region. The methodology leverages the complementary capabilities of multiple SAR sensors: Sentinel-1 (C-band, VV and VH polarizations), ALOS PALSAR (L-band, HH and HV polarizations), and, prospectively, BIOMASS (P-band, pending availability of calibrated data). Each sensor offers distinct penetration depths and sensitivities to surface and subsurface features, while their polarimetric channels provide diverse information about scattering mechanisms.

Central to the approach is the computation of the differential entropy — a statistical measure of the complexity or randomness of radar backscatter distributions — applied to the polarimetric channels. For Sentinel-1, entropy is calculated using both VV and VH channels; for ALOS, HH and HV are used. Importantly, the methodology allows for the computation of differential entropy not only on individual channels but also on combinations of polarizations across different sensors (e.g., VV from Sentinel-1 with HH from ALOS). This cross-sensor, cross-polarization analysis enhances the sensitivity to subtle textural and structural variations that may indicate the presence of buried or relict fluvial features.

By exploiting the diversity of polarimetric information, the approach aims to improve the discrimination of palaeochannel signatures from the surrounding desert matrix. The fusion of entropy maps derived from various polarization combinations is expected to highlight features that may remain undetected in single-polarization or single-sensor analyses. This is particularly valuable in desert contexts, where surface and subsurface contrasts are often faint and spatially heterogeneous.

Ancient riverbeds in desert environments are often characterized by subtle textural and structural variations that are barely distinguishable from sensor noise in conventional SAR imagery. The features of interest frequently lie at or below the noise floor, making their detection particularly challenging. By leveraging differential entropy analysis across multiple polarimetric channels and sensor combinations, the proposed methodology enhances the signal-to-noise ratio (SNR), increasing the likelihood of revealing these faint palaeochannel signatures within the noisy radar background.

The workflow involves preprocessing and calibration of SAR data, computation of local differential entropy for each polarization and selected combinations, and the integration of these entropy maps to generate enhanced indicators of palaeochannel presence. The methodology is designed to be robust to surface cover variability and atmospheric conditions, making it suitable for application in hyper-arid regions where optical methods are limited.

The study will apply this framework to the Bir Safsaf region, with the integration of BIOMASS data considered if calibrated products become available by the time of the conference. As the research is ongoing, the abstract focuses on the methodological framework and the potential of polarimetric differential entropy analysis for desert geomorphology. Preliminary results and case studies will be presented if available.