This thesis addresses the critical challenge of flood detection in conflict-affected regions characterized by severe limitations in ground truth data, with a specific focus on South Sudan in collaboration with the United Nations Global Service Centre. Within the broader context of the Anthropocene and the intensification of climate-driven hydrological extremes, conventional pixel-based classification approaches often fail to capture the spatial continuity of flooding processes and remain highly sensitive to noise and uncertainty inherent in satellite observations. To address the dual challenges of small data regimes and multi-source sensor fusion, this research proposes a Bayesian spatial modeling framework based on Integrated Nested Laplace Approximation combined with Stochastic Partial Differential Equations (INLA-SPDE). By conceptualizing flood occurrence as a latent spatial process rather than a collection of independent discrete events, the model coherently integrates heterogeneous Earth Observation data (including Sentinel-1 SAR backscatter and Sentinel-2 optical spectral indices) together with physically interpretable static topographic covariates. A harmonized dataset covering ten Areas of Interest (AOIs) in South Sudan was specifically constructed for this study, comprising 130 flood rasters used for both training and validation . The results demonstrate that the proposed INLA-SPDE framework produces robust probabilistic flood susceptibility maps with outstanding predictive performance (AUC = 0.9949). This work constitutes, to the best of our knowledge, the first implementation of a binomial INLA-SPDE model for satellite-based natural hazard detection, advancing the field from deterministic flood mapping toward principled, evidence-based probabilistic inference in support of humanitarian decision-making.

Treball de Final de Màster Universitari Erasmus Mundus en Tecnologia Geoespacial (Pla de 2022). Codi: SJL042. Curs acadèmic 2025-2026