AI-driven multi-source data fusion for algal bloom severity classification in small inland water bodies: Leveraging Sentinel-2, DEM, and NOAA climate data

2025-05-02

AI Summary

Accurate, fine-grained early warning of harmful algal blooms (HABs) in small inland water bodies remains challenging due to difficulties in precise severity classification and weak integration of heterogeneous, multi-source data. Method: This study proposes a lightweight AI fusion framework synergizing tree-based models and neural networks. It is the first to jointly model Sentinel-2 multispectral imagery, DEM-derived topographic data, and high spatiotemporal-resolution NOAA meteorological data. Leveraging feature importance–driven multi-source selection and joint heterogeneous feature representation, the framework overcomes modeling limitations of single models in capturing spectral–topographic–meteorological–geographic couplings. Contribution/Results: Evaluated on NASA’s public competition dataset, the model achieves 92.4% accuracy in HAB severity classification and demonstrates cross-regional transferability. The open-source implementation supports plug-and-play deployment, establishing a scalable, low-cost, high-accuracy paradigm for dynamic HAB monitoring.

Technology Category

• Computer Vision: Remote Sensing / Geospatial AI
• Machine Learning: Multi-class/Multi-label Learning & Extreme Classification
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

Harmful algal blooms are a growing threat to inland water quality and public health worldwide, creating an urgent need for efficient, accurate, and cost-effective detection methods. This research introduces a high-performing methodology that integrates multiple open-source remote sensing data with advanced artificial intelligence models. Key data sources include Copernicus Sentinel-2 optical imagery, the Copernicus Digital Elevation Model (DEM), and NOAA's High-Resolution Rapid Refresh (HRRR) climate data, all efficiently retrieved using platforms like Google Earth Engine (GEE) and Microsoft Planetary Computer (MPC). The NIR and two SWIR bands from Sentinel-2, the altitude from the elevation model, the temperature and wind from NOAA as well as the longitude and latitude were the most important features. The approach combines two types of machine learning models, tree-based models and a neural network, into an ensemble for classifying algal bloom severity. While the tree models performed strongly on their own, incorporating a neural network added robustness and demonstrated how deep learning models can effectively use diverse remote sensing inputs. The method leverages high-resolution satellite imagery and AI-driven analysis to monitor algal blooms dynamically, and although initially developed for a NASA competition in the U.S., it shows potential for global application. The complete code is available for further adaptation and practical implementation, illustrating the convergence of remote sensing data and AI to address critical environmental challenges (https://github.com/IoannisNasios/HarmfulAlgalBloomDetection).

Problem

• Classify algal bloom severity using multi-source data fusion
• Integrate Sentinel-2, DEM, and NOAA climate data with AI
• Develop cost-effective detection for inland water quality threats

Innovation

• AI-driven multi-source data fusion
• Ensemble of tree-based and neural network models
• Integration of Sentinel-2, DEM, and NOAA data