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Soil water is a critical resource, and as such is the focus of considerable physical research. Characterization of the distribution and spatial variability of soil water content (SWC) offers important agronomic and environmental information. Estimation of non-stationary and non-linear SWC distribution at different scales is a research challenge. Based on this context, we performed a case study on the Chinese Loess Plateau, with objectives of investigating spatial variability of SWC and soil properties (i.e., soil particle composition, organic matter and bulk density), and determining multi-scale correlations between SWC and soil properties. A total of 86 in situ sampling sites were selected and 516 soil samples (0–60 cm depth with an interval of 10 cm) were collected in May and June of 2019 along the Yangling-Wugong-Qianxian transect, with a length of 25.5 km, in a typical wheat-c rotation region of the Chinese Loess Plateau. Classical statistics and empirical mode decomposition (EMD) method were applied to evaluate characteristics of the overall and scale-specific spatial variation of SWC, and to explore scale-specific correlations between SWC and soil properties. Results showed that the spatial variability of SWC along the Yangling-Wugong-Qianxian transect was medium to weak, with a variability coefficient range of 0.06–0.18, and it was gradually decreased as scale increased. We categorized the overall SWC for each soil layer under an intrinsic mode function (IMF) number based on the scale of occurrence, and found that the component IMF1 exhibited the largest contribution rates of 36.45％–56.70％. Additionally, by using EMD method, we categorized the general variation of SWC under different numbers of IMFs according to occurrence scale, and the results showed that the calculated scales among SWC for each soil layer increased in correspondence with higher IMF numbers. Approximately 78.00％ of the total variance of SWC was extracted in IMF1 and IMF2. Generally, soil texture was the dominant control on SWC, and the influence of the three types of soil properties (soil particle composition, organic matter and bulk density) was more prominent at larger scales along the sampling transect. The influential factors of soil water spatial distribution can be identified and ranked on the basis of the decomposed signal from the current approach, thereby providing critical information for other researchers and natural resource managers.