Abstract: Forest fires tend to cause severe damage to the structures and functions of ecosystems. Burned areas that have not yet fully recovered from fires carry crucial post-fire information, holding significant implications for ecological monitoring and restoration. To address the challenge of insufficient accuracy in burned area identification under complex terrain conditions, this study proposed a high-precision information extraction method for burned areas that integrates sun-canopy-sensor (SCS) + C (SCS+C) terrain correction with random forest (RF) feature optimization. Based on the wide-field-view (WFV) images from the GF-1 satellite, a multi-dimensional feature set was constructed by integrating texture features and spectral indices. Through SCS+C terrain correction and RF feature optimization, the relative importance of feature subsets was assessed to identify the optimal feature combination for efficient information extraction of burned areas. The results indicate that SCS+C terrain correction significantly mitigated the shadow effect, avoiding misclassifying shadow areas as burned areas. The RF feature optimization retained feature subsets with higher contributions and eliminated redundant features, mitigating misclassification and omissions in the information extraction and effectively enhancing the model accuracy. The combination of terrain correction and RF feature optimization yielded extraction accuracy (including overall accuracy, user accuracy, and producer accuracy) of greater than 91% across multiple typical regions. In the information extraction of burned areas from 2019 to 2023 in Liaoning Province, the proposed method yielded overall classification accuracies of individual years ranging from 79.63% to 83.87% (average: 81.90%), with Kappa coefficients varying between 0.503 9 and 0.806 2 (average: 0.707 3). These results further validate the universality, stability, and efficiency of the proposed method in the information extraction of burned areas. This study provides reliable technical support for post-fire ecological monitoring and restoration.