Cross-Sensor Temporal Fusion for Crop Monitoring Using Weak-Signal Attention and State-Space Modeling
Keywords:
Cross-sensor fusion, temporal modeling, weak-signal attention, state-space models, crop monitoring, remote sensing, agricultural AI, sustainabilityAbstract
Accurate and timely crop monitoring is essential for global food security, sustainable agriculture, and climate adaptation. Recent advances in Earth observation have produced a rich and heterogeneous archive of satellite and aerial sensor data, including multispectral, hyperspectral, synthetic aperture radar, thermal infrared, and LiDAR modalities. However, the effective exploitation of these diverse data streams remains constrained by challenges of temporal irregularity, spatial resolution mismatches, and the presence of weak signals that precede critical phenological transitions. This paper introduces a comprehensive system-level framework for cross-sensor temporal fusion that integrates weak-signal attention mechanisms with state-space modeling to address these limitations. The proposed architecture operates as a layered pipeline: first, multi-sensor data are aligned and normalized through a unified preprocessing stage; second, a weak-signal attention module selectively amplities subtle spectral and temporal anomalies that are indicative of early stress, nutrient deficiency, or water deficit; third, a state-space model captures the continuous-time dynamics of crop development, accommodating irregular revisit intervals and sensor gaps. The system is designed to be deployed on hybrid cloud-edge infrastructures, balancing computational load with latency requirements for near-real-time applications. The paper further examines structural trade-offs inherent in sensor selection, model complexity, and data governance, and discusses implications for robustness under adversarial conditions, sustainability of large-scale AI inference, and equity in access to precision agriculture technologies. Policy recommendations are offered to promote open data standards, ensure algorithmic fairness, and align monitoring capabilities with global agricultural mandates. This work contributes both a conceptual architecture and a critical evaluation of the socio-technical conditions under which cross-sensor fusion can fulfill its potential.
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