A physics-based remote sensing framework for forest fire smoke detection toward early fire warning

• Physics-based remote sensing for early forest fire smoke detection. • MD-based smoke inversion reveals statistical–physical correlations. • Dual-threshold criterion converts empirical to probabilistic limits. • Multi-scale validation with laboratory, UAV, and satellite data. Forest fire smoke significantly disrupts regional carbon cycles and degrades air quality, highlighting an urgent need for highly sensitive detection and inversion models to improve early warning systems. This study proposes a physics-based remote sensing framework for smoke detection and concentration inversion, founded on scattering–absorption theory. By employing multi-dimensional spectral point cluster analysis based on a smoke scattering–absorption model, we introduce the Mahalanobis Distance (MD) as a physically interpretable measure of spectral deviation. A physical relationship between MD and smoke concentration is derived, which serves as the foundation for a dual-threshold detection algorithm and an inversion model. The proposed approach is validated through laboratory experiments, UAV-based simulations, and satellite observations. Key findings include: (1) Smoke clusters migrate quasi-linearly from background to the ideal smoke point with increasing concentration, while their spatial extent contracts exponentially; (2) MD exhibits a double-exponential correlation with smoke concentration (RMSE = 0.18 g/m 2 , R 2 = 0.80); (3) Detection accuracy reaches 0.89–0.97 (UAV) and 0.93 (satellite), with fire localization errors of 0.52 ± 0.35 m and 45.47 ± 42.22 m, respectively. These results confirm that spectral variation is a core parameter for smoke retrieval, and MD provides a robust, physically grounded measure for detection and inversion. The proposed model overcomes key limitations of empirical threshold methods and offers a scalable solution for early forest fire monitoring across platforms.