空中野火扑救不仅需预测火势蔓延，还需在运行与环境不确定性下设计有效的干预策略。本文提出一种空中野火扑救建模与优化框架，将混合神经元胞自动机（neural-cellular automaton）火灾模型与基于梯度的目标化空中投掷方案设计相结合。该火灾模型利用地形、可燃物及风场数据预测空间异质性的火势蔓延行为；干预模块则确定二值化的投掷动作，并以连续变量表征投掷位置与朝向参数，映射至仿真网格。水与阻燃剂分别建模，体现其不同的抑制效应：前者实现对活跃燃烧的即时削弱，后者实现对未来蔓延的持续抑制。为评估所生成扑救方案的鲁棒性，我们通过蒙特卡洛采样每日火灾状态实现来量化偶然不确定性（aleatoric uncertainty），并通过空间相关预测误差扰动量化认知不确定性（epistemic uncertainty）。基于2020年熊火（Bear Fire）的案例研究表明，该框架可生成协调一致的空中扑救调度方案，以减少总过火面积，并支持面向不确定性的野火干预策略分析。

Aerial wildfire suppression requires not only predicting fire spread, but also designing effective intervention strategies under operational and environmental uncertainty. We present a modeling and optimization framework for aerial wildfire suppression that combines a hybrid neural-cellular automaton wildfire model with gradient-based design of targeted aerial drops. The wildfire model predicts spatially varying spread behavior from terrain, fuel, and wind data, while the intervention module determines binary drop actions with continuous-valued location and orientation parameters mapped to the simulation grid. Water and retardant are represented with distinct suppression effects, corresponding to immediate reduction of active burning and persistent reduction of future spread. To evaluate the robustness of the resulting suppression plans, we quantify both aleatoric uncertainty through Monte Carlo sampling of daily fire-state realizations and epistemic uncertainty through spatially correlated prediction-error perturbations. A case study based on the 2020 Bear Fire shows that the framework can generate coherent aerial suppression schedules for reducing total fire-affected area and can support uncertainty-aware analysis of wildfire intervention strategies.