The existing research into soil component inversion based on spectroscopic techniques has mainly focused on traditional statistical learning. However, the most prominent drawback of this approach is the difficulty in obtaining the soil components’ sensitive bands with explanatory inversion mechanisms. Whether for soil organic matter inversion or soil heavy metal inversion, there is still a lack of inversion models based on the physical theory of remote sensing. Hence, in this paper, an inversion model based on thickness correction using Kubelka-Munk (K-M) theory is proposed. Firstly, in this study, a soil thickness observation experiment based on K-M theory was undertaken. The impact of the soil thickness and the material of the container on the spectra was explored by selecting different experimental samples with different background container materials. A modified K-M thickness model was then developed by combining indoor spectral data. This allows the corresponding scattering coefficients and absorption coefficients for soil samples with different organic matter contents to be calculated. The optimal organic matter inversion model can then be constructed by the scattering coefficients, with the sensitive band at 2.197 μm. The results obtained in this study demonstrate the feasibility and superiority of the proposed method and further explain the sensitive bands of soil organic matter in hyperspectral data, with a determination coefficient accuracy of up to 0.97. The experimental results also demonstrate that the recommended soil thickness for soil samples should be more than 7 mm. In addition, when selecting background container materials, materials with obvious reflectance peak and valley characteristics should be avoided.

现有的基于光谱技术的土壤成分反演研究主要集中在传统的统计学习上。然而，这种方法最突出的缺点是难以通过解释性反演机制获得土壤成分的敏感带。无论是土壤有机质反演还是土壤重金属反演，目前还缺乏基于遥感物理理论的反演模型。因此，本文提出了一种基于Kubelka-Munk（KM）理论的厚度校正反演模型。首先，在本研究中，进行了基于KM理论的土壤厚度观测实验。通过选择具有不同背景容器材料的不同实验样品，探讨了土壤厚度和容器材料对光谱的影响。然后通过结合室内光谱数据开发了修改后的 KM 厚度模型。这允许计算具有不同有机质含量的土壤样品的相应散射系数和吸收系数。通过散射系数可以构建最优的有机质反演模型，敏感波段为2.197 μm。本研究获得的结果证明了所提方法的可行性和优越性，进一步解释了高光谱数据中土壤有机质的敏感波段，测定系数精度高达0.97。实验结果还表明，推荐的土样土厚度应大于 7 mm。此外，在选择背景容器材料时，应避免使用具有明显反射峰谷特征的材料。