Live fuel moisture content (LFMC) is an important environmental indicator used to measure vegetation conditions and monitor for high fire risk conditions. However, LFMC is challenging to measure on a wide scale, thus reliable models for estimating LFMC are needed. Therefore, this paper proposes a new deep learning architecture for LFMC estimation. The architecture comprises an ensemble of temporal convolutional neural networks that learn from year-long time series of meteorological and reflectance data, and a few auxiliary inputs including the climate zone. LFMC estimation models are designed for two training and evaluation scenarios, one for sites where historical LFMC measurements are available (within-site), the other for sites without historical LFMC measurements (out-of-site). The models were trained and evaluated using a large database of LFMC samples measured in the field from 2001 to 2017 and achieved an RMSE of 20.87% for the within-site scenario and 25.36% for the out-of-site scenario.

活燃料水分含量 (LFMC) 是一项重要的环境指标，用于测量植被状况和监测高火灾风险状况。然而，LFMC 难以大规模测量，因此需要可靠的模型来估计 LFMC。因此，本文提出了一种用于 LFMC 估计的新深度学习架构。该架构包括一组时间卷积神经网络，这些网络从一年的气象和反射率数据时间序列中学习，以及一些辅助输入，包括气候区。LFMC 估计模型是为两种训练和评估场景设计的，一种用于具有历史 LFMC 测量值的站点（站点内），另一种用于没有历史 LFMC 测量值的站点（站点外）。