Recognition of targets by early warning satellites is increasingly crucial in national defense and military applications. This is closely linked to the infrared radiation characteristics of the Earth’s background, which highlights the need to establish an accurate model for such radiation. Surface types and weather conditions significantly affect infrared radiation transmission. Therefore, it is necessary to incorporate actual environmental factors when simulating the Earth’s background infrared characteristics. This is particularly important in scenarios such as forest fires, which greatly alter these characteristics and pose additional challenges to simulation. While extensive research has been conducted by domestic and international scholars on simulating the infrared radiation characteristics of typical areas, there remains a gap in addressing forest fire scenes. Therefore, to more accurately and efficiently identify target infrared radiation characteristics, it is essential to conduct simulations specifically focused on the Earth’s background under forest fire conditions.

Based on observational characteristics of the Earth’s background within satellite field of view, a parameter law for forest fire scenes is determined using satellite remote sensing data. Surface parameters are established for varying fire intensities such as temperature and emissivity, along with atmospheric parameters like VIS, water vapor content, and CO₂ concentration. An extreme scene parameter level model is developed. The cellular automata model used to simulate forest fire scenes is enhanced in three aspects: cell state, cell neighborhood, and cell rules, to effectively simulate large-scale mixed pixels within the satellite’s field of view. By employing the extreme scene parameter level model, we calculate radiation brightness under different fire scenes using MODTRAN and store these values in a SQLite database. This approach establishes an infrared radiation simulation model specifically for forest fire scenes within the satellite’s field of view.

Through simulation images, it is evident that forest fires significantly increase the radiation brightness of the region, and the area affected by fire expands with its scale (Fig. 8,Fig. 9). In the 2?3 μm band, fires intensify atmospheric backscattering and surface temperatures, leading to a notable rise in irradiance at the fire site. This complicates the identification of surface types in non-burning areas. The maximum irradiance in small-scale forest fire scenes is 58.6 times higher than that in no-fire ones. Irradiance increases slightly in medium-scale forest fires. In large-scale fires, however, additional ignition points do not significantly increase maximum irradiance, as medium-scale fires already cover the entire area. In the 8?14 μm band, radiation primarily originates from the surface, and fires release particles that enhance surface radiation absorption. Consequently, changes in maximum irradiance across the detection area are less pronounced compared to the short-wave segment. The maximum irradiance in the small-scale forest fire scene is 1.11×10³ W /m², which is only 1% higher than that in the no-fire scene. In the large forest fire scene with the highest regional irradiance, the maximum irradiance is 1.16×10³ W /m², representing a mere 6% increase compared to the no-fire scene. Regarding the duration of forest fires, in the initial stages, as the fire persists, fire points spread rapidly, expanding the fire area within the detection zone, thereby increasing overall irradiance [Fig. 9(c)?(d), Fig. 10(a)?(b)]. As ignition time progresses further, the fire area within the detection zone does not significantly expand. This is because combustible materials in the original fire site are consumed, transitioning the fire from combustion to completion of combustion. The transition leads to gradual decreases in surface temperature and irradiance (Fig. 10). Areas previously unaffected by fire may ignite due to diffusion, leading to increased surface temperatures and irradiance.

In the study of infrared radiation from forest fire scenes on Earth, satellite remote sensing data is utilized to establish parameter rules based on observed characteristics of the Earth’s background. These rules determine surface parameters such as surface temperature and emissivity, as well as atmospheric parameters like VIS, water vapor content, and CO₂ concentration, tailored to different fire intensities. An extreme scenario parameter model is developed for these parameters. The cellular automata model used for simulating forest fire scenes in images is enhanced in three key aspects: cell state, cell neighborhood, and cell rules. These improvements enable the model to accurately simulate large-scale mixed pixels within the satellite’s field of view. Using the parameter model for extreme scenarios, the radiant brightness under varying fire conditions is calculated using MODTRAN. This approach allows for the simulation of infrared radiation images of Earth’s background during forest fire scenes across arbitrary bands from 2 to 14 μm, with a wavelength resolution of 0.1 μm. The radiation data is stored in an SQLite database. Thus, an infrared radiation simulation model is established for forest fires within the field of view. This model considers numerical changes in surface temperature, surface emissivity, aerosols, and other parameters across various ignition stages. Not only do the simulation results enable comparative studies between areas affected by forest fires and those without within the satellite detection field of view, but also simulate infrared radiation scenes under different forest fire conditions by selecting varying fire scales and spread time. This simulation realizes the infrared radiation simulation of Earth’s background within the detection area during forest fire scenes, thus providing higher application value.