Soil particle size prediction using Vis-NIR and pXRF spectra in a semiarid agricultural ecosystem in Central Anatolia of Türkiye

GÖZÜKARA G., Akça E., Dengiz O., Kapur S., Adak A.

Catena, vol.217, 2022 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 217
  • Publication Date: 2022
  • Doi Number: 10.1016/j.catena.2022.106514
  • Journal Name: Catena
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Aquatic Science & Fisheries Abstracts (ASFA), BIOSIS, CAB Abstracts, Environment Index, Veterinary Science Database, DIALNET, Civil Engineering Abstracts
  • Keywords: Soil profile wall, Grid sampling, Interpolation methods, Machine learning algorithms, INFRARED REFLECTANCE SPECTROSCOPY, ORGANIC-MATTER, TOTAL NITROGEN, TEXTURE, CARBON, MODEL, CLAY, SPECTROMETRY, HYDROMETER, MOISTURE
  • Eskisehir Osmangazi University Affiliated: Yes


© 2022 Elsevier B.V.The recent technologies employed for rapid, cost-effective, and non-destructive prediction of soil particle size distribution (clay, sand, and silt) are becoming increasingly interesting among soil scientists. Our aims were to explore the effect of surface, profile wall, and surface + profile wall on prediction accuracy using individual and combined both soil spectra (Vis-NIR and pXRF) with machine learning algorithms for sand, silt, and clay. In total, 191 soil samples were collected from the soil surface (0–30 cm) and profile wall (1 m × 1 m) from cultivated fields in Eskisehir, Central Anatolia of Türkiye. The pXRF (0–45 keV) and Vis-NIR (350–2500 nm) spectroradiometers were used to obtain soil spectra from sieved soil samples. The prediction accuracy of each soil particle size was evaluated by 54 models to explore the predictive performance. The five machine learning algorithms (elastic net, lasso, random forest, ridge, and support vector machine-linear) were applied with calibration (70% soil samples) and validation (30% soil samples) data set for each soil particle size.Results showed the dominant clay mineral in the A and C horizons is chlorite. Moderate and high prediction accuracy for sand (R2 = 0.56–0.84) and clay (R2 = 0.61–0.80), whereas only moderate prediction accuracy for silt (R2 = 0.47–0.55) using both soil spectra in the surface, profile wall, and surface + profile wall. The highest prediction accuracy for each soil particle size was achieved in the soil profile wall using Vis-NIR spectra with elastic net, which outperformed other samplings such as individual pXRF, combined both soil spectra, and other machine learning algorithms. In addition, the prediction accuracy of clay was more affected by sampling strategies compared to sand and silt. We concluded that individual Vis-NIR spectroradiometer can be utilized to achieve the highest prediction accuracy for sand, silt, and clay ratio in semiarid ecosystems for soil surveys and land use studies.