Evaluating Flood Damage to Paddy Rice Fields Using PlanetScope and Sentinel-1 Data in North-Western Nigeria: Towards Potential Climate Adaptation Strategies

• Published in: Remote sensing (Basel, Switzerland) Switzerland), 2024-10, Vol.16 (19), p.3657
• Main Authors: Ibrahim, Sa’ad, Balzter, Heiko
• Format: Article
• Language: English
• Subjects: Adaptation; Agricultural land; Artificial satellites in remote sensing; Cereal crops; Climate adaptation; Climate change; Crops; Cultivation; damage; Damage assessment; Damage tolerance; Developing countries; Disasters; Earth observations (from space); Emergency preparedness; Environmental aspects; Evaluation; Flood damage; Flood mapping; Floods; Food security; Food supply; Grain cultivation; Hydrologic data; Image resolution; Land cover; Land use; LDCs; Learning algorithms; loss; Machine learning; Mapping; Monitoring methods; Natural disaster damage; NDWI; Observational learning; PlanetScope; Remote monitoring; Remote sensing; Rice; Rice fields; Satellite imagery; Satellite observation; Sensors; Sentinel-1; Spatial data; Subsistence agriculture; Synthetic aperture radar
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs16193657

Floods are significant global disasters, but their impact in developing countries is greater due to the lower shock tolerance, many subsistence farmers, land fragmentation, poor adaptation strategies, and low technical capacity, which worsen food security and livelihoods. Therefore, accurate and timely monitoring of flooded crop areas is crucial for both disaster impact assessments and adaptation strategies. However, most existing methods for monitoring flooded crops using remote sensing focus solely on estimating the flood damage, neglecting the need for adaptation decisions. To address these issues, we have developed an approach to mapping flooded rice fields using Earth observation and machine learning. This approach integrates high-resolution multispectral satellite images with Sentinel-1 data. We have demonstrated the reliability and applicability of this approach by using a manually labelled dataset related to a devastating flood event in north-western Nigeria. Additionally, we have developed a land suitability model to evaluate potential areas for paddy rice cultivation. Our crop extent and land use/land cover classifications achieved an overall accuracy of between 93% and 95%, while our flood mapping achieved an overall accuracy of 99%. Our findings indicate that the flood event caused damage to almost 60% of the paddy rice fields. Based on the land suitability assessment, our results indicate that more land is suitable for cultivation during natural floods than is currently being used. We propose several recommendations as adaptation measures for stakeholders to improve livelihoods and mitigate flood disasters. This study highlights the importance of integrating multispectral and synthetic aperture radar (SAR) data for flood crop mapping using machine learning. Decision-makers will benefit from the flood crop mapping framework developed in this study in a number of spatial planning applications.