Prediction of material composition in lateritic soil using spectroradiometer

• Published in: Materials today : proceedings 2023, Vol.88, p.152-159
• Main Authors: Gangannavar, Prasanna, Bhojaraja, B.E., Shetty, Thushar S
• Format: Article
• Language: English
• Subjects: Iron content; Material composition; Soil; Spectra; Spectroradiometer
• ISSN: 2214-7853; 2214-7853
• DOI: 10.1016/j.matpr.2023.06.402

•Laterite soil products are essential in coastal region construction.•Remote sensing techniques expedite large-scale identification of lateritic soil composition.•The study collected random soil samples from the study area for laboratory analysis.•Spectral signatures obtained from a spectroradiometer revealed significant variability in the material composition.•The study employed Vis-NIR reflectance spectroscopy, utilizing an Analytical Spectral Device (ASD) .•The application of Partial Least Squares Regression (PLSR) demonstrated moderate prediction accuracy.•The spectroradiometer, in combination with PLSR, can accurately predict the material composition in lateritic soil, offering an efficient approach with an efficiency rate of 70%. Laterite soil products are vital in coastal region construction. Conventional identification methods are slow and require significant labour. Remote sensing expedites large-scale identification, making it essential in the process. Soil samples were randomly collected from the study area for laboratory analysis to determine soil iron content. Additionally, spectral signatures were obtained from a spectroradiometer to assess iron content variability. The considerable variability in material composition underscores its significance as an indicator of soil characteristics. The iron content serves as a crucial indicator of soil development and can be utilized as an index for assessing material composition in soil. The iron content in soil samples is analysed using Analytical Spectral Device (ASD) Field Spec 4 Spectroradiometer, which operates within the spectral wavelength range of 350–2500 nm. The Vis-NIR reflectance spectroscopy is an innovative and efficient method for predicting soil iron content, requiring less effort compared to conventional techniques. The study uses Partial Least Squares Regression (PLSR), a statistical regression method employed to predict iron content in soil from spectral reflectance data. The application of PLSR resulted in moderate prediction accuracy, with coefficient of determination R2 value for calibration and validation are 0.74 and 0.73, respectively. The analysis revealed that the spectroradiometer in combination with PLSR can accurately predict the material composition in lateritic soil with an efficiency of 70%.