Wind erosion potential can be assessed using the Threshold Friction Velocity (TFV) of the soil, which is not always easy to measure, especially on regional and global scales. To overcome this difficulty, the spectroscopy technique can provide a useful approach in estimating the TFV as an alternative for time-consuming wind tunnel studies in the field. In this study, we evaluated the potential of Vis-NIR spectroscopy in predicting the TFV and some TFV-related soil properties using Partial Least Square Regression (PLSR) and the Support Vector Regression (SVR). We also developed a Point Spectrotransfer Function (PSTF) using Multiple Linear Regression (MLR) to predict the TFV based on diagnostic wavelengths and compared it to the derived Pedotransfer Function (PTF). For this purpose, 300 in-situ wind tunnel tests were performed in the Fars Province, Iran and the spectral reflectance of soil samples were analysed using a spectrophotometer apparatus. The 10 best key wavelengths resulting from the correlation analysis between the TFV and the spectral reflectance were 750, 1342, 1446, 1578, 1746, 1939, 2072, 2162, 2217, and 2338 nm which were mostly located in the short-wavelength infrared (SWIR) area. The derived PSTF performed better than the PTF for the TFV estimation (R² = 0.94, RMSE = 0.71). Results of the predictive models revealed that machine learning using the SVR had a significantly (P < 0.01) higher prediction accuracy for the TFV estimation (R² = 0.85, RMSE = 0.45, RPD = 2.50, and RPIQ = 4.06) than the PLSR (R² = 0.68, RMSE = 1.01, RPD = 1.72, and RPIQ = 2.64). The same results were obtained for the soil moisture, clay and CaCO₃ content. This study proved that reflectance spectroscopy coupled with the machine learning algorithm is a promising technique for large-scale assessment of wind erosion.

可以使用土壤的阈值摩擦速度（TFV）来评估风蚀的可能性，该阈值并不总是易于测量的，尤其是在区域和全球范围内。为了克服这个困难，光谱技术可以提供一种有用的方法来估算TFV，作为现场耗时的风洞研究的替代方法。在这项研究中，我们使用偏最小二乘回归（PLSR）和支持向量回归（SVR）评估了Vis-NIR光谱在预测TFV和与TFV相关的某些土壤特性方面的潜力。我们还开发了使用多重线性回归（MLR）的点光谱传递函数（PSTF），以基于诊断波长预测TFV，并将其与派生的Pedotransfer函数（PTF）进行了比较。为此，原位300在伊朗的Fars省进行了风洞测试，并使用分光光度计对土壤样品的光谱反射率进行了分析。通过TFV和光谱反射率之间的相关分析得出的10个最佳关键波长为750、1342、1446、1578、1746、1939、2072、2162、2217和2338 nm，这些波长主要位于短波红外（ SWIR）区域。对于TFV估计，导出的PSTF的性能优于PTF（R ²  = 0.94，RMSE = 0.71）。预测模型的结果表明，使用SVR进行机器学习的TFV估计（R ²  = 0.85，RMSE = 0.45，RPD = 2.50和RPIQ = 4.06）的预测准确性显着（P <0.01）更高（P <0.01）。R ² = 0.68，RMSE = 1.01，RPD = 1.72和RPIQ = 2.64）。对于土壤水分，粘土和CaCO ₃含量也获得了相同的结果。这项研究证明，反射光谱技术与机器学习算法相结合是一种用于风蚀大规模评估的有前途的技术。