融合多源卫星数据，自监督学习识别有害藻华种类与严重程度
Fusing Multi- and Hyperspectral Satellite Data for Harmful Algal Bloom Monitoring with Self-Supervised and Hierarchical Deep Learning
- 自监督学习融合多传感器反射率与太阳诱导荧光数据
- 在墨西哥湾和南加州验证，藻类总量与赤潮物种检测准确
- 无需标注数据，适合缺乏实地观测的全球藻华监测
我们提出一种自监督机器学习框架SIT-FUSE，利用多源极轨卫星仪器（VIIRS、MODIS、OLCI、OCI）的反射率数据与TROPOMI太阳诱导荧光（SIF）数据，实现有害藻华（HABs）的严重程度与物种分类。该框架无需各仪器的标注数据，通过自监督表征学习与分层深度聚类，将浮游植物细胞丰度与物种划分为可解释类别，并在2018-2025年墨西哥湾与南加州的现场数据上验证。结果与总浮游植物、凯伦氏藻（Karena brevis）及拟菱形藻属（Pseudo-nitzschia spp.）测量值高度一致。该研究推动了在地面观测稀缺区域的可扩展藻华监测，通过分层嵌入支持探索性分析，是自监督学习在地球生物地球化学领域迈向业务化的关键一步。
原文摘要 · Abstract (English)
We present a self-supervised machine learning framework for detecting and mapping the severity and speciation of harmful algal blooms (HABs) using multi-sensor satellite data. By fusing reflectance data from operational polar-orbiting satellite-based instruments (VIIRS, MODIS, OLCI, and OCI) with TROPOMI solar-induced fluorescence (SIF), our framework, called SIT-FUSE, generates HAB severity and speciation products without requiring per-instrument labeled datasets. The framework employs self-supervised representation learning and hierarchical deep clustering to segment phytoplankton cell abundance and species into interpretable classes, validated against in-situ data from the Gulf of Mexico and Southern California (2018-2025). Results show strong agreement with total phytoplankton, Karena brevis, and Pseudo-nitzschia spp. measurements. This work advances scalable HAB monitoring in environments where ground truth observations are limited, while enabling exploratory analysis via hierarchical embeddings - a critical step toward operationalizing self-supervised learning for global aquatic biogeochemistry.
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