Live fuel moisture content (LFMC) is a key factor that determines the flammability of vegetation in ecosystems. Soil moisture (SM) is one of the variables that is known to influence plant water use. The present study analyses the LFMC-SM relationship over Australia using gridded, remote sensing-based LFMC and land surface model-based SM products. A lag-correlation analysis conducted over 60 selected sites shows that the strength of the relationship between LFMC and SM varies from site to site and, in general, is moderately strong (median lag-correlation of ~0.5). However, the strength of the relationship changes with vegetation type and also with soil profile depth. At all the sites, SM is found to be a leading indicator of LFMC. The lag also varies with the location and is found to range from days to months. Based on the location-based correlation analysis, we identify the 0-35 cm SM profile (SM_0-35cm) to be the best predictor of LFMC. We developed a simple model to predict daily LFMC, where it is hypothesised that daily variations in LFMC from its annual cycle can be predicted using daily deviations from the annual cycle in SM_0-35cm. The annual cycles of LFMC and SM_0-35cm are modelled using Fourier cosine series. The averaged (over 60 sites) correlation obtained for the validation period is 0.74 when a time-lag of 14 days is assumed at all locations. When the model is applied nationally at a 5 km grid, the normalised root mean squared error for the validation period is found to be less than 25% in general. The results from the present study highlight a modelling strategy that can be used to address a critical gap in the forecast of spatially and temporally continuous LFMC at regional scales in advance for operational fire management applications.

活燃料含水量 (LFMC) 是决定生态系统中植被可燃性的关键因素。土壤水分 (SM) 是已知影响植物用水的变量之一。本研究使用网格化、基于遥感的 LFMC 和基于地表模型的 SM 产品分析了澳大利亚的 LFMC-SM 关系。对 60 个选定站点进行的滞后相关分析表明，LFMC 和 SM 之间的关系强度因站点而异，并且一般来说，中等强度（中值滞后相关为 ~0.5）。然而，这种关系的强度随植被类型和土壤剖面深度而变化。在所有站点，SM 被发现是 LFMC 的领先指标。滞后也随位置而变化，范围从几天到几个月不等。_0-35cm ) 是_LFMC的最佳预测指标。我们开发了一个简单的模型来预测每日 LFMC，其中假设可以使用 SM _{0-35cm 中}与年度周期的每日偏差来预测 LFMC 与其年度周期的每日变化。LFMC 和 SM 年周期_0-35cm使用傅立叶余弦级数建模。当假设所有地点的时间滞后为 14 天时，验证期获得的平均（超过 60 个地点）相关系数为 0.74。当该模型在全国范围内应用于 5 公里网格时，发现验证期的归一化均方根误差一般小于 25%。本研究的结果突出了一种建模策略，该策略可用于解决区域尺度上空间和时间连续 LFMC 预测中的关键差距，以用于运营火灾管理应用。