Proximal and remote sensing techniques in the optical domain are cost-effective alternatives to standard soil property characterization methods. However, the extent of light penetration into the soil sample, also termed soil information depth, is not well understood. In this study a new analytical model that links the particle size distribution and soil reflectance in the near infrared (NIR) and shortwave infrared (SWIR) bands of the electromagnetic spectrum is introduced. The model enables the partitioning of measured reflectance spectra into surface and volume (subsurface) contributions, thereby yielding insights about the soil information depth. The model simulations indicate that the surface reflectance contribution to the total reflectance is significantly higher than the volume reflectance contribution for a broad range of soils that vastly differ in texture, mineralogical composition and organic matter contents. The ratio of volume to total reflectance is higher for sandy soils than for clayey soils, especially at longer optical wavelengths, but the ratio rarely exceeds 15%. Therefore, the light reflection from dry soils is predominantly a surface phenomenon and the information depth in most soils rarely exceeds 1 mm. The results of this study reveal an intimate physical relationship between soil reflectance and the particle size distribution in the NIR/SWIR range, which opens a potential new avenue for retrieval of the particle size distribution from remotely sensed reflectance via a universal process-based approach.

光学领域的近距离和遥感技术是标准土壤特性表征方法的经济高效替代方案。然而，光穿透到土壤样品中的程度，也被称为土壤信息深度，还没有被很好地理解。在这项研究中，引入了一种新的分析模型，该模型将电磁光谱的近红外（NIR）和短波红外（SWIR）波段中的粒径分布和土壤反射率联系起来。该模型可以将测得的反射光谱划分为表面和体积（地下）成分，从而获得有关土壤信息深度的见解。模型仿真表明，对于质地，矿物组成和有机物含量差异很大的多种土壤，表面反射率对总反射率的贡献明显高于体积反射率的贡献。沙质土壤的体积与全反射率之比高于黏土，尤其是在较长的光波长下，但该比率很少超过15％。因此，干燥土壤的光反射主要是表面现象，大多数土壤中的信息深度很少超过1 mm。这项研究的结果揭示了土壤反射率与NIR / SWIR范围内的粒径分布之间密切的物理关系，