Moisture is one of the most important factors affecting soil reflectance spectra. However, provisional and dynamic behavior of soil moisture (SM) in salty soils reduce the capability of field spectrometry in the estimation of soil properties. This study aims minimising the effect of SM on the accuracy of visible and near-infrared (VNIR) spectra estimation of clay, calcium carbonate (CaCO₃)_, and organic carbon (OC) content in semi-arid soils by adopting External parameter orthogonalization (EPO). Spectral reflectance of disturbed soil samples was measured for seven moisture contents (i.e. air dried, 6%, 12%, 18%, 24%, 30%, and 36%) and five levels of electerical conductivity (EC) (i.e. < 1, 4, 8, 12 and 16 dS/m). The EPO algorithm was customized for four textural classes. The performances of EPO algorithm was evaluated through partial least squares–backpropagation neural network (PLS–BPNN). The optimum number of components for the EPO matrix was determined to be four. Results showed that geometric parameters (area, depth, and width) of diagnostic absorption features (AF) in 550 nm, 2200 nm, and 2340 nm were affected by SM, and the interaction between SM and reflectance was not completely equivalent through different texture classes. The EPO correction showed an improvement of prediction accuracy of clay (RPIQ improved from 1.82 to 2.93), CaCO₃ (RPIQ improved from 1.96 to 2.73), and OC (RPIQ improved from 1.94 to 3.44). Also better results have been gained by the customization of EPO for different texture classes. The performances of the EPO algorithm for clay and OC modeling dropped by increasing EC. This suggests that further studies are required to develop a method for eliminating the effects of the external parameters caused by increased salt levels in the soil.

水分是影响土壤反射光谱的最重要因素之一。然而，咸性土壤中土壤水分 (SM) 的临时和动态行为降低了现场光谱法在估计土壤特性方面的能力。本研究旨在最小化SM的对粘土，碳酸钙的可见和近红外（VNIR）谱估计的精度的影响，碳酸钙（CaCO ₃）_，采用外参数正交化（EPO）法测定半干旱土壤中有机碳（OC）含量。测量了七种水分含量（即风干、6%、12%、18%、24%、30% 和 36%）和五种电导率 (EC) 水平（即 < 1， 4、8、12 和 16 dS/m)。EPO 算法针对四个纹理类别进行了定制。通过偏最小二乘-反向传播神经网络（PLS-BPNN）评估EPO算法的性能。EPO 基质的最佳组分数被确定为四个。结果表明，550 nm、2200 nm 和 2340 nm 诊断吸收特征 (AF) 的几何参数（面积、深度和宽度）受 SM 影响，并且通过不同的纹理类，SM 和反射率之间的相互作用并不完全等效。EPO 校正显示粘土预测精度提高（RPIQ 从 1.82 提高到 2.93），CaCO₃（RPIQ 从 1.96 提高到 2.73）和 OC（RPIQ 从 1.94 提高到 3.44）。通过为不同的纹理类别定制 EPO，也获得了更好的结果。用于粘土和 OC 建模的 EPO 算法的性能随着 EC 的增加而下降。这表明需要进一步的研究来开发一种方法来消除由土壤中盐分增加引起的外部参数的影响。