Wildland fire decision support systems require accurate predictions of wildland fire spread. Fuel moisture content (FMC) is one of the important parameters controlling the rate of spread of wildland fire. However, dead FMC measurements are provided by a relatively sparse network of remote automatic weather stations (RAWS), while live FMC is relatively infrequently measured manually. We developed a high resolution, gridded, real-time FMC data sets that did not previously exist for assimilation into operational wildland fire prediction systems based on ML. We used surface observations of live and dead FMC to train machine learning models to estimate FMC based on satellite observations. Moderate Resolution Imaging Spectrometer Terra and Aqua reflectances are used to predict the live and dead FMC measured by the Wildland Fire Assessment System and RAWS). We evaluate multiple machine learning methods including multiple linear regression, random forests (RFs), gradient boosted regress...

中文翻译：

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通过使用机器学习构建网格化的燃料水分含量产品来增强野火蔓延建模

野火决策支持系统需要对野火蔓延的准确预测。燃料水分含量（FMC）是控制野火蔓延速率的重要参数之一。但是，死区FMC测量是由相对稀疏的远程自动气象站（RAWS）网络提供的，而实时FMC很少手动进行测量。我们开发了高分辨率，网格化的实时FMC数据集，该数据集以前不存在，无法同化为基于ML的可操作野火预测系统。我们使用了生死FMC的表面观测来训练机器学习模型，以基于卫星观测来估计FMC。中等分辨率成像光谱仪的Terra和Aqua反射率可用于预测由Wildland Fire Assessment System和RAWS测量的生与死FMC。我们评估多种机器学习方法，包括多元线性回归，随机森林（RF），梯度提升回归...