Advanced Deep Learning Approach for Accurate Upwelling Detection Along Morocco’s Atlantic Coast Using SST Imagery

Abstract

The study of coastal upwelling through the analysis of sea surface temperature (SST) satellite imagery has been a valuable approach because of its efficiency and practicality. Building on inception and residual structures, we introduce IncepResup-Net, a novel deep learning model for identifying upwelling regions along Morocco’s Atlantic coast. This model effectively addresses limitations in recent methods targeting the same upwelling system and outperforms them by more accurately detecting true upwelling areas, thereby minimizing false positives. Applied to SST data spanning from 2000 to 2022, IncepResup-Net demonstrates superior performance over traditional and contemporary deep learning models, marked by its precise segmentation capabilities and robustness in real-world detection scenarios. Our findings highlight the model’s effectiveness in leveraging SST imagery for upwelling detection, establishing a new benchmark in the application of deep learning within geoscience and remote sensing fields.