Soil water repellency (SWR) is an important soil physical property that may restrict water infiltration and soil water retention. Common laboratory and field techniques for assessing SWR are laborious, time-consuming, and costly. Meanwhile, Visible–Near-Infrared (Vis-NIR) spectroscopy has been reported as a rapid, cost-effective, and alternative technique to estimate several soil properties. To investigate the efficacy of this technique for predicting SWR indices [soil water repellency index (RI) and soil–water contact angle (β)] at dry condition, 100 soil samples collected from farmlands, orchards, rangelands and forests of Zrêbar lake watershed in Kurdistan province, Iran, were measured by Vis-NIR spectroscopy within the 350–2500 nm range. The Savitzky–Golay first derivative method was applied for denoising the spectral data. The RI and β were measured by the intrinsic sorptivity method (using water and ethanol as absorbing liquids). The other basic soil properties were also measured by standard laboratory methods. Stepwise multiple linear regression (SMLR) and partial least squares regression (PLSR) were utilized to establish pedo-transfer functions (PTFs) and spectro-transfer functions (STFs) using basic soil properties and spectral absorbance data, respectively, to estimate soil organic matter (SOM) content and SWR indices. We obtained good predictions for SOM with R² = 0.67 and RPIQ (the ratio of performance to interquartile range) = 1.92 using the PLSR-based STF. The results also revealed that although the SMLR-based STFs achieved slightly better estimates of the SWR indices (RI and β) than the SMLR-based PTFs (R² values of 0.28 to 0.39 vs. 0.19 to 0.23, respectively); but, overall, none of these transfer functions for estimating these indices showed acceptable predictive capability. However, the PLSR-based STFs could provide a reasonable prediction for the studied SWR indices (R² > 0.52 and RPIQ > 2.27). The majority of important adsorption bands in the Vis-NIR PLSR models for the SWR prediction was related to both the quantity and quality of SOM. Overall, the results demonstrated that the Vis-NIR PLSR could be applied to predict SOM and SWR indices rapidly, non-destructively, and with fair accuracy.

土壤拒水性 (SWR) 是一种重要的土壤物理性质，可能会限制水分渗透和土壤保水。用于评估 SWR 的常用实验室和现场技术费力、费时且成本高昂。同时，据报道，可见-近红外 (Vis-NIR) 光谱是一种快速、经济且替代的技术，可用于评估多种土壤特性。研究该技术在预测 SWR 指数 [土壤拒水性指数 (RI) 和土壤-水接触角 ( β)] 在干燥条件下，从伊朗库尔德斯坦省 Zrêbar 湖流域的农田、果园、牧场和森林收集的 100 份土壤样品在 350-2500 nm 范围内通过可见光近红外光谱仪进行了测量。Savitzky-Golay 一阶导数方法被应用于去噪光谱数据。RI 和β通过固有吸附法（使用水和乙醇作为吸收液）测量。其他基本土壤特性也通过标准实验室方法测量。利用逐步多元线性回归 (SMLR) 和偏最小二乘回归 (PLSR) 分别使用基本土壤特性和光谱吸光度数据建立土壤传递函数 (PTF) 和光谱传递函数 (STF)，以估计土壤有机质(SOM) 内容和 SWR 指数。我们 使用基于 PLSR 的 STF获得了对 SOM 的良好预测，其中R ² = 0.67 和 RPIQ（性能与四分位距的比率）= 1.92。结果还表明，尽管基于 SMLR 的 STF 对 SWR 指数（RI 和β) 比基于 SMLR 的 PTF（R ²值分别为 0.28 至 0.39 与 0.19 至 0.23）；但是，总的来说，这些用于估计这些指数的传递函数都没有显示出可接受的预测能力。然而，基于 PLSR 的 STF 可以为研究的 SWR 指数（R ²  > 0.52 和 RPIQ > 2.27）提供合理的预测。用于 SWR 预测的 Vis-NIR PLSR 模型中的大多数重要吸附带与 SOM 的数量和质量有关。总体而言，结果表明 Vis-NIR PLSR 可用于快速、无损且准确地预测 SOM 和 SWR 指数。