Prediction of soil textural constituents using reflected spectra in eastern Mazandaran Province

Abstract

Textural components of soil play an essential role in erodibility and should be considered in many projects of conservation and environmental modeling processes. Traditional methods of determining soil texture are usually laborious, expensive and time consuming along with destructive effects on the environment. Meanwhile, spectroscopic technology using the spectral features and signatures from the whole reflected spectra of soil surface promises a competent method to study soil constituents. To investigate this issue, 113 points were selected and sampled randomly from 0-15 cm of soil surface in eastern parts of Mazandaran Province, Iran. Samples were haphazardly divided into 91 for model building and 22 for final verification and accuracy assessment processes. Applying the enhanced PLS-algorithm plus the FLOOCV approach along with spectral transformations and pre-processing, the modeling of each textural components were accomplished. Spectrally, sand and clay fractions were modeled with high accuracy as: R2c= 0.89, RMSEc= 7.42, SEc= 7.46 for sand and R2c= 0.82, RMSEc= 6.88, SEc= 6.92 for the clay content. Whereas, the silt predictive model was slightly weaker than the other constituents. The most effective spectral ranges involved in the modeling process, were also detected and recognized based on beta & spectral weight analyses and Marten’s uncertainty test. Additionally, the most influential spectroscopic ranges included were the visible, NIR and SWIR regions with the specified wavelengths. In general, the efficacy of spectroscopic technology in soil texture studies has been proven by this research. Using the computed spectral models, we are able to study the soil textural components at large scales faster, safer, timelier and also cheaper. That is absolutely true and applicable using the regionalized remotely sensed data but requires further investigation in different geographical regions.