A Comparative Study on Sentinel-2 Cloud Detection Algorithms in Marine Environments

In the realm of satellite-based marine monitoring, accurate cloud detection is crucial for ensuring the reliability of satellite imagery analysis. Addressing this challenge, our paper presents a comprehensive evaluation of four cloud detection algorithms (Fmask, SEN2COR, KappaMask, and S2Cloudless) based on MARIDA dataset, which includes annotations for marine debris, sea surface features, and cloud classes over marine environments in Sentinel-2 (S2) data. We introduce a new class, Thin Cloud, and significantly extend existing annotations to encompass diverse cloud characteristics. Our analysis employs an evaluation protocol with varying degrees of classification granularity, assessing algorithm performance across Cloud, Thin Cloud, Cloud Shadow, and Clear categories. The results reveal Fmask’s proficiency in binary Cloud/ Clear detection, while KappaMask demonstrates consistent performance across all scenarios, including complex Cloud Shadow distinctions. We also identify specific limitations of each algorithm, such as SEN2COR’s underper-formance in dense cloudy regions and Fmask’s challenges in identifying cloud shadows. Our findings provide valuable insights into the effectiveness of these algorithms in marine cloud detection, highlighting the need for improved accuracy in cloud masking techniques for marine monitoring systems.