Title: “SAR OBSERVATION OF GREEN ALGAE USING DL” Speaker: Dr Guo Yuan, Visiting student, Dipartimento di Ingegneria, Università di Napoli Parthenope Abstract: The talk is to describe a textural-enhanced deep learning (DL) model based on the classic U-net framework for green algae detection in Sentinel-1 synthetic aperture radar (SAR) imagery. The framework is applied to monitor the green tide in the Yellow Sea from 2019 to 2021 and to shed light on the relationship between green tide interannual variation and two primary environmental factors: nitrate concentration and sea surface temperature (SST). The interannual variation is characterized via three crucial indexes: bloom duration, coverage area, and nearshore damage. The detection results reveal that the bloom duration is the longest (shortest) in 2019 (2020), corresponding to the biggest (smallest) coverage area in 2019 (2020). In addition, the nearshore damage is the heaviest (lightest) in 2021 (2020). We also found that the interannual variation of green tide scales is partly related to the available nitrate concentration and SST variation in algae- distributed regions. Dipartimento di Ingegneria, Centro Direzionale, isola C4, Napoli, Campania, Italy
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