Machine and Deep Learning Framework for Sargassum Detection and Fractional Cover Estimation Using Multi-Sensor Satellite Imagery

José Manuel Echevarría-Rubio (), Guillermo Martínez-Flores () and Rubén Antelmo Morales-Pérez
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José Manuel Echevarría-Rubio: Departamento de Oceanología, Centro Interdisciplinario de Ciencias Marinas, Instituto Politécnico Nacional, Av. Instituto Politécnico Nacional s/n, Colonia Playa Palo de Santa Rita, La Paz 23096, Baja California Sur, Mexico
Guillermo Martínez-Flores: Departamento de Oceanología, Centro Interdisciplinario de Ciencias Marinas, Instituto Politécnico Nacional, Av. Instituto Politécnico Nacional s/n, Colonia Playa Palo de Santa Rita, La Paz 23096, Baja California Sur, Mexico
Rubén Antelmo Morales-Pérez: Instituto Mexicano de Tecnología del Agua, Paseo Cuauhnáhuac 8532, Colonia Progreso, Jiutepec 62550, Morelos, Mexico

Data, 2025, vol. 10, issue 11, 1-26

Abstract: Over the past decade, recurring influxes of pelagic Sargassum have posed significant environmental and economic challenges in the Caribbean Sea. Effective monitoring is crucial for understanding bloom dynamics and mitigating their impacts. This study presents a comprehensive machine learning (ML) and deep learning (DL) framework for detecting Sargassum and estimating its fractional cover using imagery from key satellite sensors: the Operational Land Imager (OLI) on Landsat-8 and the Multispectral Instrument (MSI) on Sentinel-2. A spectral library was constructed from five core spectral bands (Blue, Green, Red, Near-Infrared, and Short-Wave Infrared). It was used to train an ensemble of five diverse classifiers: Random Forest (RF), K-Nearest Neighbors (KNN), XGBoost (XGB), a Multi-Layer Perceptron (MLP), and a 1D Convolutional Neural Network (1D-CNN). All models achieved high classification performance on a held-out test set, with weighted F1-scores exceeding 0.976. The probabilistic outputs from these classifiers were then leveraged as a direct proxy for the sub-pixel fractional cover of Sargassum . Critically, an inter-algorithm agreement analysis revealed that detections on real-world imagery are typically either of very high (unanimous) or very low (contentious) confidence, highlighting the diagnostic power of the ensemble approach. The resulting framework provides a robust and quantitative pathway for generating confidence-aware estimates of Sargassum distribution. This work supports efforts to manage these harmful algal blooms by providing vital information on detection certainty, while underscoring the critical need to empirically validate fractional cover proxies against in situ or UAV measurements.

Keywords: remote sensing; machine learning; fractional cover; pelagic Sargassum; multi-sensor; spectral library (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2025
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