Coastal algal bloom monitoring requires frequent, spatially detailed, and globally consistent observations, provided by Landsat-8/9 and Sentinel-2 A/B/C. Together, these missions offer over a decade of medium-resolution multispectral imagery with near-global coverage every 2-3 days, enabling the detection of fragmented bloom structures not resolvable by coarse ocean-color sensors. However, their use in aquatic environments remains challenging due to limited spectral coverage and a lack of harmonized reflectance products. As an alternative to traditional bio-optical methods, deep learning-based image classification offers a data-driven approach that can overcome many of these limitations. This study presents the first successful implementation of vision transformer-based coastal algal bloom mapping using 30-m Landsat-Sentinel-2 images. A globally distributed bloom patch dataset was generated across bloom-prone coastal hotspots worldwide. Four transformer-based architectures were compared against a standard convolutional baseline for fine-scale bloom detection, and assessed under different optical water types and atmospheric and surface conditions. All deep learning models showed strong capabilities in detecting floating bloom areas, with omission and commission errors of 8-65%. Under cloud and glint stress in a time series, the Swin Transformer outperformed traditional spectral-index approaches, which produced widespread false positives, effectively avoiding cloud- and glint-affected pixels. Comparisons with MODIS-derived products further highlighted the benefits of higher spatial resolution in detecting fragmented and irregularly affected blooms. Our findings support deep learning as a reliable tool for medium-resolution, consistent monitoring of floating algal blooms in dynamic coastal environments.

翻译：沿海藻华监测需要频繁、空间详细且全球一致的观测数据，这由Landsat-8/9和Sentinel-2 A/B/C提供。这些任务共同提供了超过十年的中等分辨率多光谱影像，每2-3天覆盖近全球范围，能够检测粗分辨率水色传感器无法分辨的碎片化藻华结构。然而，由于光谱覆盖有限且缺乏统一的反射率产品，这些数据在水体环境中的应用仍面临挑战。作为传统生物光学方法的替代方案，基于深度学习的图像分类提供了一种数据驱动的方法，可以克服其中许多局限性。本研究首次成功实现了基于视觉Transformer的30米分辨率Landsat-Sentinel-2影像沿海藻华制图。我们在全球藻华易发沿海热点区域构建了一个全球分布的藻华斑块数据集。将四种基于Transformer的架构与标准卷积基线模型进行对比，评估其在精细尺度藻华检测中的性能，以及在不同光学水类型、大气和表面条件下的表现。所有深度学习模型在检测漂浮藻华区域时均表现出强大能力，遗漏误差和误报误差为8-65%。在时间序列中的云和耀光干扰条件下，Swin Transformer优于传统光谱指数方法（后者会产生大量误报），有效避免了受云和耀光影响的像素。与MODIS衍生产品的比较进一步凸显了更高空间分辨率在检测碎片化和不规则受影响藻华方面的优势。我们的研究结果支持深度学习作为动态沿海环境中中等分辨率、一致性漂浮藻华监测的可靠工具。