Information depth of NIR/SWIR soil reflectance spectroscopy

REMOTE SENSING OF ENVIRONMENT

卷:256

Article

Norouzi, Sarem¹  Sadeghi, Morteza²  Liaghat, Abdolmajid¹  Tuller, Markus³  Jones, Scott B.²  Ebrahimian, Hamed¹ 

[1] Univ Tehran, Dept Irrigat & Reclamat Engn, Karaj, Iran

[2] Utah State Univ, Dept Plants Soils & Climate, Logan, UT 84322 USA

[3] Univ Arizona, Dept Environm Sci, Tucson, AZ USA

关键词: Soil reflectance spectrum;    Particle size distribution;    Information depth;    Optical remote sensing;   

DOI  :  10.1016/j.rse.2021.112315

来源: Elsevier

【 摘 要 】

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.

【 授权许可】

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