Cabral, Pedro da Costa BritoPla Bañón, FilibertoCosta, Hugo Alexandre Gomes daLateef, Lukumon Olaitan2024-03-262025-03-012024-02-29http://hdl.handle.net/10362/165449Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial TechnologiesThe results of this research have been published in the Journal of Environmental Management: https://doi.org/10.1016/j.jenvman.2025.125542Flooding is a global recurring disaster that is hindering crop production and food security. Smallholder farmers in Nigeria suffer severe damages and vulnerable to flooding because most practice rain-fed agriculture. Also, Optical satellite images for flood mapping face cloud interference and free Synthetic Aperture Radar (SAR) lacks temporal frequency to capture flooding dynamics. This research used the integration of time-series PlanetScope and Sentinel-1 images to develop flood progression and extent layers for the 2020 and 2022 flooding in Hadejia, Nigeria. Cropland damage and recovery status was assessed using post-flooding Sentinel-2 images and integration of survey data from the farmers. The result revealed that 58% and 66% of the total cropland was flooded in 2020 and 2022, respectively. The recovered cropland in 2020 was estimated to be 13% of the flooded cropland while 16% recovered in 2022. This study proved the effectiveness of the integration of optical and radar time-series images for detailed and refined flooding mapping. This result is crucial for a comprehensive damage assessment on crop production, which can facilitate transparent and data-driven post-disaster action plans, and valuable for policy making in spatial planning to achieve the Sustainable Development Goals (SDGs) of zero hunger (SDG2) and climate action (SDG13).engFloodRemote SensingSatellite ImageCroplandDamage AssessmentCrop RecoveryRandom ForestSDG 2 - Zero hungerSDG 13 - Climate actionmaster thesis203561317