Development of a Nationwide Soil Spectrum Library and Digital Soil Assessment Based on Archived Soil Samples Using Pedometrics and Spectral Data

Determining soil parameters is essential for rational soil use, sustainable soil management, cost-effective monitoring, and collecting baseline data for targeted soil mapping.
The aim of our research is to perform non-destructive spectroradiometric measurements on the archived soil sample bank of the HUN-REN ATK Institute for Soil Sciences, which includes comprehensive laboratory background data. This initiative seeks to develop a nationwide soil spectrum library that spatially represents the entirety of Hungary’s land cover and soil types, encompassing thousands of data points. This digital database facilitates the identification of correlations between traditionally measured soil properties and spectral characteristics. The ultimate objective is to enable the cost-effective and rapid estimation of certain soil parameters—such as soil organic matter (SOM) content, CaCO3, and pH—that are otherwise difficult, time-consuming, or expensive to measure.

The spectral database is built on two key pillars. The first comprises 5,500 soil samples collected from agricultural lands in 2011–2012 as part of the Hungarian Soil Degradation Observation System (HSDS). The second consists of 2,000 soil samples gathered from tree plantations and control areas (including pastures, fallow lands, and agricultural plots) across the Great Hungarian Plain between 2012–2014. This approach has enabled the successful inclusion of a wide range of land cover types in Hungary, spanning multiple soil layers.

Spectral measurements were performed using an ASD Field Spec 4 spectroradiometer, focusing on the visible–near-infrared region of the electromagnetic spectrum. Reflectance values were measured across a wavelength range of 350 to 2500 nm, covering 2,151 spectral bands. The recorded reflectance values underwent consistent pre-processing, which included steps such as conversion to absorbance, splice correction, noise reduction, and smoothing. Further, additional data scenarios were generated by applying advanced processing techniques, including standard normal variate (SNV) transformation, detrending, and first- and second-order derivatives.

The relationships between soil properties and soil spectra were analyzed using various machine learning techniques—such as Generalized Linear Models (GLM), Distributed Random Forest (DRF), Gradient Boosting Machine (GBM), and Deep Learning Neural Networks (DLN)—implemented in an R programming environment using the 'h2o' package.

Initial results based on the HSDS database show that SOM and CaCO₃ contents are best estimated using the DRF model with absorbance and first-derivative spectra (R² = 0.705, RMSE = 0.528 for SOM, and R² = 0.632, RMSE = 5.756 for CaCO₃). For soil pH estimation, the DLN model achieved an R² of 0.677 and RMSE of 0.483 when using absorbance, second-derivative spectra, and SNV transformation.

Regarding the forestry soil database, preliminary results are presented in this poster, as investigations are still ongoing. These efforts are being supplemented with XRF data, which are expected to enhance estimation accuracy when combined with spectral data.