Predicting field capacity, wilting point, and the other physical properties of soils using hyperspectral reflectance spectroscopy: two different statistical approaches

• Published in: Environmental monitoring and assessment 2014-08, Vol.186 (8), p.5077-5088
• Main Authors: Arslan, Hakan, Tasan, Mehmet, Yildirim, Demet, Koksal, Eyüp Selim, Cemek, Bilal
• Format: Article
• Language: English
• Subjects: Aluminum Silicates - chemistry; Atmospheric Protection/Air Quality Control/Air Pollution; Calibration; Clay; correlation; Discriminant analysis; Earth and Environmental Science; Ecology; Ecotoxicology; Environment; Environmental Management; Environmental monitoring; Environmental Monitoring - methods; equations; Field capacity; Irrigation; Laboratories; Mean square errors; Methods; Models, Statistical; Monitoring/Environmental Analysis; Multivariate Analysis; new combination; Physical properties; prediction; Reflectance; Regression analysis; Sand; Silicon Dioxide - chemistry; Silt; Soil - chemistry; Soil analysis; Soil contamination; Soil physical properties; soil sampling; Spectroscopy; Spectrum Analysis; Studies; Wavelengths; Wilting; Wilting point
• ISSN: 0167-6369; 1573-2959
• DOI: 10.1007/s10661-014-3761-2

In this study, we examined the ability of reflectance spectroscopy to predict some of the most important soil parameters for irrigation such as field capacity (FC), wilting point (WP), clay, sand, and silt content. FC and WP were determined for 305 soil samples. In addition to these soil analyses, clay, silt, and sand contents of 145 soil samples were detected. Raw spectral reflectance (raw) of these soil samples, between 350 and 2,500-nm wavelengths, was measured. In addition, first order derivatives of the reflectance (first) were calculated. Two different statistical approaches were used in detecting soil properties from hyperspectral data. Models were evaluated using the correlation of coefficient (r), coefficient of determination (R ²), root mean square error (RMSE), and residual prediction deviation (RPD). In the first method, two appropriate wavelengths were selected for raw reflectance and first derivative separately for each soil property. Selection of wavelengths was carried out based on the highest positive and negative correlations between soil property and raw reflectance or first order derivatives. By means of detected wavelengths, new combinations for each soil property were calculated using rationing, differencing, normalized differencing, and multiple regression techniques. Of these techniques, multiple regression provided the best correlation (P