Thermo-CR: real-time physics-based cloud shadow removal via thermodynamic atmospheric modelling and multi-source...

Spaceborne optical sensors provide continuous Earth observation, but atmospheric interference still limits their practical reliability. On average, clouds cover 67% of the Earth's surface. This constant coverage degrades the data continuity needed for precision agriculture, disaster monitoring, and proactive Internet of Things (IoT) systems. Recent deep generative networks produce visually appealing cloud-free images. However, when faced with thick clouds ([Formula: see text] opacity), these models often hallucinate topologies. They synthesize statistical guesses instead of recovering the actual ground reflectance. For high-stakes telemetry, predictable failure is safer than an undetected hallucination. This paper introduces Thermo-Cloud Removal (Thermo-CR), a real-time cloud removal framework. It integrates Radiative Transfer inversion, weather-driven transmission estimates, geographic priors, and multi-scale fusion to restore optical imagery without requiring Synthetic Aperture Radar (SAR). Thermo-CR treats the cloudy atmosphere as a thermodynamic medium. By pulling live meteorological telemetry (Relative Humidity (RH) and Temperature (T)) through the Open-Meteo REST API, the system calculates optical depth and performs a deterministic inversion of the Radiative Transfer Model. Pure inverse models amplify noise under extreme occlusion ([Formula: see text]). To prevent this, we apply a Global Positioning System (GPS)-anchored multi-scale fusion with clear-sky temporal priors. We evaluated Thermo-CR on a synthetically occluded paired dataset covering varied topologies (Amazon, London, Seattle). The system degrades predictably under 90% cloud cover and avoids structural hallucination. It achieves an average Structural Similarity Index Measure (SSIM) of 0.9925 and a Peak Signal-to-Noise Ratio (PSNR) of 55.94 dB in under 13 milliseconds per frame, outperforming standard Dark Channel baselines.