Advancing African Mangrove Forest Mapping Using Super-Resolution and Semantic Segmentation of Multi-Date Multi-Sensor Satellite Imagery

Remote sensing is essential for mapping mangrove forest ecosystems, yet accuracy and spatial resolution remain constrained, particularly in Africa, where mangroves are fragmented, reference data and cloud-free images are scarce. The state-of-the-art of mangrove forest mapping relies on native-resolution, single-date imagery and machine learning. This study evaluates deep learning methods using aggregated multi-date satellite imagery to improve mangrove mapping and national-scale area estimation. Semantic segmentation and super-resolution techniques were applied to Sentinel‑2 (S2) and Sentinel‑1 (S1) images, with training data derived from field observations and high-resolution drone and satellite imagery. Coastal regions of Tanzania and Ghana were used as representative study areas for eastern and western Africa, respectively. Results show strong classification performance, with S2 imagery alone achieving an Intersection over Union (IoU) of 85.8%. Adding S1 provided modest gains (86.3%), while super-resolution significantly improved the accuracy (88.5%). The combined super-resolved S2 and S1 dataset yielded the highest performance (89.2%). Wall-to-wall mapping and statistically rigorous validation and area estimation produced accurate results with quantified uncertainty. Tanzania showed high consistency (F1 = 96%; 1207 km², 95% CI [1151.7, 1263]), while Ghana exhibited slightly higher uncertainty (F1 = 92%; 92.6 km², 95% CI [87.5, 97.7]). Integrating deep learning with multi-date, multi-sensor fusion and super-resolution enables accurate mangrove mapping and area estimation for improved coastal management and restoration.