The FengYun (FY-3E) satellite was successfully launched on July 5, 2021. It is a polar-orbiting meteorological satellite and the world's first civilian morning-and-night orbit meteorological satellite. It carries a Medium-Resolution Spectral Imager-LL payload that delivers high-resolution OLR products. OLR can reflect more atmospheric change information, is more sensitive to temperature change, and can better reflect the entire surface atmospheric system, which is conducive to the in-depth understanding of the seismic sphere coupling module. OLR is widely used in seismic infrared anomaly extraction. The additive tectonic stress from the celestial tide-generating force (ATSCTF) seismic anomaly extraction algorithm has been continuously improved. However, the threshold of seismic thermal anomaly is mostly based on experience. The results of the quartile algorithm are objective, and it has some advantages in recognizing abnormal values. Whether the FY-3E can be used for seismic risk monitoring remains to be investigated and how. In this study, the 2023 Turkey double earthquakes are taken as an example, the seismic anomalies of FY-3E OLR data are extracted based on the ATSCTF algorithm, and the anomaly threshold is determined via the quartile method. The results show that the ATSCTF algorithm based on the quartile threshold method is suitable for FY-3E data, and FY-3E OLR data have a certain response to pre-earthquake radiation anomalies, which can be used for seismic infrared anomaly tracking. The comprehensive use of ascending and descending data can better serve the extraction of seismic anomalies. High-resolution temporal remote sensing data are more conducive to the extraction of seismic anomalies via ATSCTF. The research and practice of seismic risk monitoring based on the ATSCTF algorithm can be further strengthened.

Based on temporal variations in the celestial tidal force (CTF) at the hypocenter of the Ms 7.1 Yushu earthquake, we analyzed the relationship between the earthquake and the phase change in the CTF. Then, using atmospheric temperature data from the National Center for Environmental Prediction Final Operational Global Analysis (NCEP-FNL) dataset, we introduced a three-dimensional spatial distribution method to stratify atmospheric thermodynamic anomalies. We further explored the relationship between the CTF, atmospheric temperature, and seismic activity, and the results show that the phase change of the continuous enhancement of atmospheric temperature is temporally quasi-synchronous with the continuous change in the CTF from peak to trough and this method successfully captures the synchronous migration of atmospheric temperature anomalies associated with two adjacent earthquakes. As the Yushu earthquake approached, warming became concentrated, transitioning from a scattered distribution to the Ganzi–Yushu seismogenic fracture zone. After the earthquake, the warming distribution underwent significant bifurcation and offset steering, and a new significant earthquake occurred in Nerong in the new warming region formed along the Zadoi – Shanglaxiu seismogenic fault zone. The vertical multi-layer structures of the thermal anomaly show that the spatial expansion pattern, intensity, and extent of atmospheric warming with increasing altitude are consistent with the atmospheric thermodynamic properties of ground-to-atmosphere heating, leading to near-surface atmospheric warming, lifting, diffusion, and extinction, showing the primary warming of the subsurface heat source. In general, the CTF changes the tectonic environment, induces earthquakes, and changes the atmospheric temperature, and the temperature anomaly reflects that the tectonic stress in the seismogenic zones has entered a critical state. Therefore, the atmospheric warming data extracted using Tidal Force Potential Analysis (TFFA) can be used to monitor the stress state and indicate seismogenic regions for the earthquake induced by the CTF. The results of the synergistic time –space –intensity evolution of atmospheric warming due to seismic activity can also provide a new parameter that can complement studies of regional atmospheric changes due to the effects of tectonic activity.

Assessing wildfire susceptibility requires integrating environmental and anthropogenic factors to quantify the probability and vulnerability of fires in a given area. Many existing machine-learning models offer high predictive power but limited interpretability, restricting their utility for operational decision-making. This study is the first to apply the intrinsically interpretable deep network TabNet to wildfire susceptibility modeling. By fusing multi-source data and leveraging TabNet’s feature-mask matrix, we achieve accurate prediction and built-in explanation without relying on auxiliary tools. On a dataset of 133,811 samples, the proposed model achieves an Area Under the Curve (AUC) of 0.760, recall of 0.883, precision of 0.395, and an F1.5 score of 0.640, outperforming XGBoost (version 1.5.0) and other baseline models. The importance rankings derived from the feature-mask matrix align with the Shapley Additive Explanations (SHAP) results, confirming the reliability of the explanations. This approach combines predictive accuracy with transparency, providing a deployable framework for wildfire early warning, risk management, and ecosystem conservation.