Prediction of Selected Soil Parameters Based On Reflectance Measurements: a Case Study In a Hungarian Catchment Area.

The changing environment, energy management and the related global problems highlighted the significance of the agricultural and other environmental functions of soils. Reliable information on various soil properties and their changes are essential in the understanding of soil processes, in land use planning and conservation and also in modeling global environmental cycles. In our work we present a quick, cost-effective and environment friendly method to determine and monitor key soil properties.

160 sampling plots were randomized in a 100 km² area, and were respectively sampled to a depth of 1 meter, with 20 cm segmentation. Samples were taken from a hilly region, both agricultural and forested areas were represented in the sampling.

The methodology is based on reflectance measurements in the visible (VIS), near-infrared (NIR) and short wave-infrared (SWIR) region of the electromagnetic spectrum. For the quantitative estimation of soil organic matter content, calcium carbonate and pH from laboratory reflectance data different chemometric models (Multiple Linear Regression, Partial Least Squares Regression and Principal Component Regression) were calibrated and validated. To achieve the best modeling results the reflectance data were transformed applying different methods widely used in chemometric modeling (calculation of absorbance, standardization, normalization, first and second order derivatives). The results indicate that reflectance spectroscopy is an effective way to characterize large numbers of the key soil parameters.