Reflectance study of soil silt using proximal sensing in Northern Iran

Abstract

The study of silt fractions using the traditional methods, especially on large scales, is time-consuming, laborious, and costly. The present work intends to investigate the spectral behaviors of the soil silt fraction using reflectance spectroscopy technology. Accordingly, 128 soil samples were collected from 20cm of soil surface of Mazandaran province, northern Iran. First, the sample set was subdivided into calibration and validation subsets. Spectral signatures of silt components were detected utilizing the PLSR algorithm and Cross-Validation technique. The final model with 4 LFs was calibrated with these specs: Rc: 0.55, RMSEc: 8.31%, RPDc: 1.20, and RPIQc:1.71 and was eventually selected as the best model for studying the soil silt of Mazandaran province. The obtained spectral wavebands with the highest correlation coefficients (R(CCmax)) indicate the high impact as the independent predictors in the processes of modeling. Finally, the capability of the proximal sensing technology (VNIR-PS) was proved in examining the silt content of Mazandaran province. Also, the most influential spectral domains and ranges were detected and recognized. Our findings can be used as a basis for studying silt content on a large scale by applying the upscaling process via airborne/satellite hyperspectral data. Subject classification codes: Soil Conservation, Proximal Soil Sensing, Soil Spectral Modeling