article · Geocarto International
Monitoring surface water dynamics across arid regions in Africa relies increasingly on multi-date satellite data, which provides continuous, precise, and long-term observations over time. Remote sensing options span varying spatial resolutions, where high-resolution multispectral sensors offer superior detail at higher costs, alongside dual-sensor approaches combining optical satellites such as Sentinel-2 and Landsat, radar platforms like Sentinel-1 and RADARSAT, and uncrewed aerial vehicles. Traditional index-based algorithms, including the normalised difference water index, modified normalised difference water index, and automated water extraction index, remain widely used to delineate surface water bodies. In parallel, advanced machine learning tools such as support vector machines, Random Forest, deep learning, and recurrent transformer networks demonstrate strong capabilities. Future progress requires combining high-resolution imagery and physical models with artificial intelligence and online big data processing platforms.
Arid environments in Africa face acute water security challenges, making accurate tracking of surface water essential. Understanding the strengths and trade-offs of different satellite sensors, analytical indices, and machine learning methods allows environmental managers and policymakers to track water availability, assess seasonal fluctuations, and implement better-informed water conservation and resource management strategies.
The review highlights tools relevant to water resource monitoring, hydrological planning, and environmental analytics. Potential users include regional water authorities, agricultural planners, and geospatial software developers. As an overview of existing satellite sensors and algorithms rather than a standalone software product, this work sits at an early conceptual stage, identifying how integrating deep learning with online big data platforms could enable commercial-grade water monitoring services.
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This review presents a comprehensive examination of recent advancements in utilizing multi-date satellite data to analyze spatial and temporal variations in seasonal and inter-annual surface water dynamics within arid environments of Africa. Remote sensing offers continuous, precise, and long-term datasets for surface water research. Various sensors with differing spatial resolutions are discussed, with high-resolution multispectral sensors providing superior spatial resolution but at higher costs. Conversely, dual-sensor approaches, incuding optical sensors (Sentinel-2 and Landsat), radar satellites (Sentinel-1 and RADARSAT) and UAVs were investigated. The review further examines the efficiency and applicability of traditional algorithms such as the modified normalized difference water index (MNDWI), normalized difference water index (NDWI), and automated water extraction index (AWEI) in detecting and delineating surface water resources. Additionally, machine learning (ML) algorithms, including support vector machines (SVM), Random Forest (RF), deep learning and emerging methodologies like recurrent tranformer networks, have been explored. Therefore, we recommend that future research endeavours focus on leveraging high-resolution satellite imagery and integrating physical models with deep learning techniques, artificial intelligence, and online big data processing platforms to improve surface water mapping capabilities.
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DOI: 10.1080/10106049.2024.2347935
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