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

• URL: http://arxiv.org/abs/2510.02763v2
• Date: Fri, 10 Oct 2025 19:04:32 GMT
• Title: Fusing Multi- and Hyperspectral Satellite Data for Harmful Algal Bloom Monitoring with Self-Supervised and Hierarchical Deep Learning
• Authors: Nicholas LaHaye, Kelly M. Luis, Michelle M. Gierach,
• Abstract summary: We present a self-supervised machine learning framework for detecting and mapping harmful algal bloom severity and speciation.<n>By fusing reflectance data from operational instruments with TROPOMI solar-induced fluorescence (SIF), our framework, called SIT-FUSE, generates HAB severity and speciation products without requiring per-instrument labeled datasets.<n>The framework employs self-supervised representation learning, hierarchical deep clustering to segment phytoplankton concentrations and speciations into interpretable classes, validated against in-situ data from the Gulf of Mexico and Southern California.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present a self-supervised machine learning framework for detecting and mapping harmful algal bloom (HAB) severity and speciation using multi-sensor satellite data. By fusing reflectance data from operational instruments (VIIRS, MODIS, Sentinel-3, PACE) 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, hierarchical deep clustering to segment phytoplankton concentrations and speciations 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, Karenia brevis, Alexandrium spp., and Pseudo-nitzschia spp. measurements. This work advances scalable HAB monitoring in label-scarce environments while enabling exploratory analysis via hierarchical embeddings: a critical step toward operationalizing self-supervised learning for global aquatic biogeochemistry.

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