Forest Fire Monitoring Based on Cloud Parameters: A Case Study in Southern Hunan

In recent years, global warming has increased forest fire frequency. Satellite remote sensing has become a key monitoring tool; however, cloud cover severely limits detection accuracy by causing omission and commission errors. To overcome this limitation, this study aims to use Himawari-8/9 multi-source data; this study introduces cloud optical thickness (COT) and cloud effective radius (CER) as core cloud parameters and incorporates surface downward shortwave radiation to develop a cloud-constrained XGBoost forest fire monitoring model. A multi-spectral threshold method with the cloud product (CLP) was applied for cloud identification and typing, and key cloud indicators were derived from COT and CER. A feature set comprising brightness temperatures, cloud parameters with neighborhood statistics, and radiation normalization features was constructed to train and compare XGBoost, random forest, and logistic regression models. Feature importance, partial dependence, and SHAP value analyses quantitatively revealed the nonlinear attenuation mechanisms of cloud parameters, and optimization strategies such as dynamic threshold adjustment were proposed. The XGBoost model achieved the best performance, with an overall AUC of 0.968 and a 9.0% improvement in F1-score over thick cloud regions after joint optimization and validation against the Himawari-8/9 WLF product. These results demonstrate that the proposed model effectively enhances forest fire monitoring accuracy under complex cloud conditions, providing a technical and theoretical basis for precise fire detection in cloudy environments. The novelty of this study lies in the explicit incorporation of COT and CER as physical constraints, the systematic elucidation of their nonlinear effects, and the provision of a technical and theoretical basis for precise fire detection under cloudy environments.