Integrated Hybrid Space-Earth Observation Platforms Combining Satellite Remote Sensing and Ground-Based Sensors for Early Detection of Extreme Climate Events

Abstract

Natural hazards like floods, cyclones, heat waves, and droughts can be detrimental to human life, physical infrastructure, and natural ecosystem. Existing methods used for monitoring climate events based solely on satellite and/or ground-based observations fail to provide the adequate spatial-temporal resolution needed for efficient event detection. In this work, an Integrated Hybrid Space-Earth Observation System is introduced to leverage data from multiple satellites (e.g., optical sensors, SAR sensors, thermal sensors, GNSS) along with ground observations (weather station, hydrological sensors, soil moisture sensors, flux towers, IoT devices) to improve the efficiency of detecting/predicting climate events. The proposed framework utilizes a six-layer architecture which includes the following components: multi-source data collection, Edge computing, Core Data Processing, Deep Learning-based analysis, Early Warning dissemination, and Feedback Loop. The evaluation metrics indicate that the hybrid system surpasses the performances of the single source systems, attaining high precision, recall, and F1-score, with longer lead times and fewer false positive and false negative results. Through ablation analysis, it was discovered that the combination of satellite and ground-based observations improved detection accuracy and reduced mean detection time in addition to boosting its resistance to environmental variance. Accordingly, it is suggested that climate monitoring applications incorporate multi-sensor fusion and AI-based analytics to ensure timely alerts and informed decision making. Through its operational setup, the system allows for the quick communication of relevant information for decision-making purposes.