Determining the soil moisture content (SMC) distribution is indispensable for field management, especially in arid and semiarid regions. Effective parameters can contribute to the optimization of SMC models for accurate SMC prediction. Therefore, in this study, the relationships between the SMC and multifractal parameters (D₂, D_v, D_s, A_v, and F_v denote the correlation dimension, property of small probability and large probability, spectrum width, and symmetry of spectrum shape, respectively) of the soil particle size distribution (PSD) were explored in the hilly loess region of China. A grid method was adopted (20 m × 20 m, total = 384 points) to sample the SMC in the 0–40 cm and 40–80 cm soil layers on the second, eighth, and twelfth days after the first rainfall, which were defined as the early sampling (ES), medium sampling (MS), and late sampling (LS), respectively. We found that the variation in the SMC explained by the multifractal parameters increased as SMC decreased, where they accounted for 40.07% and 75.75% of the SMC in the 0–40 cm and 40–80 cm soil layers in LS, respectively. The variation in the SMC in the 0–40 cm explained by the multifractal parameters was lower than that in the 40–80 cm soil layer in all sampling stages. The equations fitted for the whole soil layer were significant in all sampling stages (P < 0.01). A_v (31.95%) and D_v (13.59%) had negative correlations with SMC, and their relative importance values with respect to SMC were high in all sampling stages (P < 0.05). SMC at higher values had a positive correlation with F_v, whereas SMC at lower values had a significant positive correlation with D₂ and negative correlation with D_s , and these two parameters in the MS and LS stages explained more than 75% of the variation in the SMC in the 40–80 cm soil layer. These results suggest that the characteristics of the soil PSD can be described in detail by the multifractal parameters, which can directly reflect the SMC. Thus, we conclude that SMC is closely related to the multifractal parameters of the PSD (A_v, D_v, D_s, D₂, and F_v) and it can be applied to optimize SMC models.

确定土壤水分含量（SMC）的分布对于田间管理是必不可少的，尤其是在干旱和半干旱地区。有效参数可有助于优化SMC模型以实现准确的SMC预测。因此，在这项研究中，SMC与多重分形参数（D ₂，D _v，D _s，A _v和F _v分别研究了黄土丘陵区土壤粒径分布（PSD）的相关维数，小概率和大概率性质，谱宽和谱形对称性。采用网格方法（20 m×20 m，总计= 384点）在第一次降雨后第二，第八和第十二天在0–40 cm和40–80 cm土层中对SMC进行采样，分别是分别定义为早期采样（ES），中等采样（MS）和晚期采样（LS）。我们发现，由多重分形参数解释的SMC的变化随SMC的减少而增加，在LS的0–40 cm和40–80 cm土层中，它们分别占SMC的40.07％和75.75％。在所有采样阶段，由多重分形参数解释的SMC在0–40 cm的变化均低于40–80 cm土层的变化。在所有采样阶段，适用于整个土壤层的方程都很重要（P  <0.01）。甲_v（31.95％）和d _v（13.59％）有负相关与SMC，以及它们相对于SMC相对重要性值在所有的采样阶段高（P  <0.05）。较高的SMC与F _v呈正相关，而较低的SMC与D ₂呈正相关，与D _s呈负相关_。，MS和LS阶段的这两个参数解释了40-80 cm土层中SMC的变化超过75％。这些结果表明，可以通过直接反映SMC的多重分形参数来详细描述土壤PSD的特征。因此，我们得出结论，SMC与PSD的多重分形参数（A _v，D _v，D _s，D ₂和F _v）密切相关，可以用于优化SMC模型。