Visible and near infrared spectroscopy for predicting texture in forest soil: an application in southern Italy

Abstract

Texture is a primary variable affecting the total amount of carbon stock in the soil. The standard methods for determining soil texture, however, are still conducted manually and are largely time-consuming. Reflectance spectroscopy in the visible, near infrared (Vis-NIR, 350-2500 nm) spectral region could be an alternative to standard laboratory methods. The aim of this paper was to develop calibration models based on laboratory Vis-NIR spectroscopy and PLSR analysis to estimate the texture (sand: 2-0.05 mm; silt: 0.05-0.002 mm; clay: <0.002 mm) in a forest area of southern Italy. An additional objective was to produce continuous maps of sand, silt and clay through a geostatistical approach. Soil samples were collected at 235 locations in the study area, and then dried, sieved at 2 mm and analyzed in laboratory for soil texture and Vis-NIR spectroscopic measurements. Spectra showed that soil samples could be spectrally separable on the basis of classes of texture. To establish the relationships between spectral reflectance and soil texture (sand, silt and clay) partial least squared regression (PLSR) analysis was applied to 175 soil samples, while the remaining 60 samples were used to validate the models. The optimum number of factors to be retained in the calibration models was determined by leave-one-out cross-validation. Results of cross validation of calibration models indicated that the models fitted quite well and the values of R2 ranged between a minimum value of 0.74% for silt and a maximum value of 0.84 for sand content. Results for validation were satisfactory for sand content (R2=0.81) and clay content (R2=0.80) and less satisfactory for silt content (R2=0.70). Geostatistics coupled with Vis-NIR reflectance spectroscopy allowed us to produce continuous maps of sand, silt and clay, which are of critical importance for understanding and managing forest soils.

Keywords

• Forest Soils
• Soil Texture
• Vis-NIR Spectroscopy
• Geostatistics
• Southern Italy