ArticleEnvironmental monitoring and assessment2025
Decoding spatiotemporal dynamics of suspended sediment and vegetation in shallow reservoirs with Sentinel-2 and ANNs: A case study of Lake Tisza, Hungary.
Article in Environmental monitoring and assessment, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Shallow reservoirs on large rivers are highly dynamic systems vulnerable to sediment accumulation, eutrophication, and water quality deterioration, posing significant threats to their storage capacity, hydropower generation, and ecological balance. Remote sensing offers a powerful tool for monitoring and enhancing the understanding of suspended sediment dynamics and vegetation distribution. However, many existing studies relied on traditional, less accurate approaches in their analysis. This study aimed to leverage Sentinel-2 imagery (285 images; 2017-2024) and machine learning, specifically artificial neural networks (ANN), to investigate the spatiotemporal distribution of suspended sediment concentration (SSC) and vegetation coverage in the Kisköre Dam reservoir, Tisza River, Hungary. The models showed a promising performance, with the SSC model achieving an R
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