Rapid land use prediction via portable X-ray fluorescence (pXRF) data on the dried lakebed of Avlan Lake in Turkey


Geoderma Regional, vol.28, 2022 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 28
  • Publication Date: 2022
  • Doi Number: 10.1016/j.geodrs.2021.e00464
  • Journal Name: Geoderma Regional
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, INSPEC
  • Keywords: Entisols and inceptisols, Avlan Lake, Soil spectra, Proximal sensor, Decision tree, Soil spatial variability, Machine learning algorithm, CHARACTERIZING SOILS, NIR, SPECTROMETRY, CONTAMINATION, REFLECTANCE, SPECTROSCOPY, WETLAND
  • Eskisehir Osmangazi University Affiliated: Yes


© 2021The rapid, non-destructive, and low-cost characterization of soil by using portable X-ray fluorescence (pXRF) has been increasingly attractive among soil scientists for soil pedogenesis research. Therefore, pXRF can aid to characterize by measuring elemental concentration, weathering indices, soil spectra for agricultural and lakebed areas. This study aimed to characterize the agricultural area (AA) and lakebed area (LA) through elemental concentration, weathering indices, and soil spectra based on pXRF spectral behavior and to evaluate the prediction and characterization performance of decision tree models for AA and LA. In total 108 soil samples (36 locations) were collected from three fixed depth intervals such as 0–20, 20–40, and 40–60 cm in both AA and LA in Elmalı, Antalya, Turkey. All soil samples measured using pXRF to obtain elemental concentrations and soil spectra and results of input variables were randomly separated into 70% for calibration and 30% for validation. Savitzky-Golay Filter was applied for spectral data preprocessing. The area under the ROC curve (AUC) and validation accuracy were used to compare the prediction performance of seven different models. The results showed that the AA had the highest Mg, Si, Ca, Ti, P, Mn, Zn, Sr, and Zr concentrations compared with LA. The prediction performance of AUC (0.79) and validation accuracy (0.77) for elemental concentrations were acceptable, but other models were less satisfactory compared to the best model. In particular, P, Sr, and Fe were robust indicators to separate AA and LA using the decision tree model. These results suggested that the possibility of using pXRF for soil characterization and high prediction performance of AA and LA. This study highlights that elemental concentrations, weathering indices, and soil spectra based on pXRF measurements can be used as a useful proximal sensor to predict, characterize, separate, and investigate an understanding of soil characterization in AA and LA.