Live fuel moisture content (LFMC) is an essential variable to model fire danger and behaviour. This paper presents the first application of deep learning to LFMC estimation based on the historical LFMC ground samples of the Globe-LFMC database, as a step towards operational daily LFMC mapping in the Contiguous United States (CONUS). One-year MODerate resolution Imaging Spectroradiometer (MODIS) time series preceding each LFMC sample were extracted as the primary data source for training. The proposed temporal convolutional neural network for LFMC (TempCNN-LFMC) comprises three 1-D convolutional layers that learn the multi-scale temporal dynamics (features) of one-year MODIS time series specific to LFMC estimation. The learned features, together with a few auxiliary variables (e.g., digital elevation model), are then passed to three fully connected layers to extract the non-linear relationships with LFMC. In the primary training and validation scenario, the neural network was trained using samples from 2002 to 2013 and then adopted to estimating the LFMC from 2014 to 2018, achieving an overall root mean square error (RMSE) of 25.57% and a correlation coefficient (R) of 0.74. Good consistency on spatial patterns and temporal trends of accuracy was observed. The trained model achieved a similar RMSE of 25.98%, 25.20% and 25.93% for forest, shrubland, and grassland, respectively, without requiring prior information on the vegetation type.

实时燃料水分含量 (LFMC) 是模拟火灾危险和行为的重要变量。本文基于 Globe-LFMC 数据库的历史 LFMC 地面样本，首次将深度学习应用于 LFMC 估计，作为在美国本土 (CONUS) 进行日常 LFMC 映射的一步。提取每个 LFMC 样本之前的一年中等分辨率成像光谱仪 (MODIS) 时间序列作为训练的主要数据源。为 LFMC 提出的时间卷积神经网络 (TempCNN-LFMC) 包括三个一维卷积层，它们学习特定于 LFMC 估计的一年 MODIS 时间序列的多尺度时间动态（特征）。学习到的特征，连同一些辅助变量（例如，数字高程模型），然后传递给三个全连接层以提取与 LFMC 的非线性关系。在主要训练和验证场景中，神经网络使用 2002 年至 2013 年的样本进行训练，然后用于估计 2014 年至 2018 年的 LFMC，实现了 25.57% 的总体均方根误差 (RMSE) 和相关系数 (R ) 的 0.74。观察到空间模式和准确性的时间趋势具有良好的一致性。训练后的模型分别为森林、灌木丛和草地实现了 25.98%、25.20% 和 25.93% 的类似 RMSE，而无需 总体均方根误差 (RMSE) 为 25.57%，相关系数 (R) 为 0.74。观察到空间模式和准确性的时间趋势具有良好的一致性。训练后的模型分别为森林、灌木丛和草地实现了 25.98%、25.20% 和 25.93% 的类似 RMSE，而无需 总体均方根误差 (RMSE) 为 25.57%，相关系数 (R) 为 0.74。观察到空间模式和准确性的时间趋势具有良好的一致性。训练后的模型分别为森林、灌木丛和草地实现了 25.98%、25.20% 和 25.93% 的类似 RMSE，而无需植被类型的先验信息。