Fusing Multi‐ and Hyperspectral Satellite Data for Harmful Algal Bloom Monitoring With Self‐Supervised and Hierarchical Deep Learning

Abstract 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 data sets. 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.