| چکیده انگلیسی مقاله |
Sand content is closely related to soil quality and plant growth. As well, sand fraction is one of the most important soil textural segments which should be highlighted for environmental modeling operations and digital soil mapping projects. On one hand; identification, mapping and monitoring of sand content over wide scales using traditional sampling and common lab analysis procedures is time-consuming and costly, probably due to its vast spatial variability. With the advent of Lab. Diffuse reflectance Spectroscopy (LDRS) which exploits the fundamental vibration, overtones and combination of functional groups for soil investigation, and so, that became a promising tool related. The present research intends to predict sand content utilizing the mentioned proximal soil sensing tech. Thus, in accord with supplementary data layers (geology, pedology, landuse and etc.) and stratified randomized sampling method, eventually, 128 samples from 20cm of soil surface of Mazandaran province (scattered parts), were gathered. First of all, sample-set subdivided into two subsets: calibration subset with 96 and validation subset with 32 samples. Afterwards, using the multivariate regression analysis-PLSR method with leave-one-out cross-validation technique and some preprocessing algorithms such as: spectral averaging (spectra reduction method), smoothing and 1st derivative (Savitzky-Golay derivation algorithm), the definitive calibration model with two & four latent vectors and RP, R2P, RMSEP, RPDP and RPIQP respectively: 0.83 and 0.82, 0.68 and 0.67, 8.68 and 8.83%, 1.78 and 1.75, 2.45 and 2.41, were validated(using standalone validation subset) and spotted as the most appropriate model for the sand content prediction in the study region. Lastly, the VNIR-DRS potentiality for sand content estimation in Mazandaran soils has proven. Also it is feasible to upscaling the sand prediction process utilizing the principal resulted model and key spectral domains(recognized) via airborne/satellite hyperspectral data, which emphatically shows the LDRS importance as a commencement point for characterizing the informative optical wavelengths, likewise, that will be the infrastructure for spaceborne data modeling (upscaling process). |
