Calibration Sample Selection Influences Soil Property Prediction Using Vis-NIR, MIR, pXRF, and Fused Spectral Data
Agronomy, cilt.16, sa.19, 2026 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 16 Sayı: 19
- Basım Tarihi: 2026
- Doi Numarası: 10.3390/agronomy16191911
- Dergi Adı: Agronomy
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, CAB Abstracts, Geobase, Directory of Open Access Journals, Academic Search Ultimate (EBSCO), Natural Science Collection (ProQuest)
- Anahtar Kelimeler: continued Latin hypercube, k-means clustering, machine learning, proximal soil sensing, soil spectroscopy
- Eskişehir Osmangazi Üniversitesi Adresli: Evet
Özet
Visible near-infrared (Vis-NIR), mid-infrared (MIR), and portable X-ray fluorescence (pXRF) spectroscopy have been widely used as cost-effective, rapid, and non-destructive techniques for predicting soil properties. However, the influence of calibration sample selection on prediction performance across different soil spectral sensors and their fused datasets has not been systematically evaluated. This study aims to compare four calibration sample selection strategies, including random sampling (RS), Kennard-Stone (KS), conditioned Latin hypercube sampling (cLHS), and k-means clustering, using Vis-NIR, MIR, pXRF, and their combinations for the prediction of sand, silt, clay, electrical conductivity (EC), pH, soil organic carbon (SOC), and CaCO3 using Cubist and PLSR models. Individual and combined Vis-NIR (350–2500 nm), MIR (4000–400 cm−1), and pXRF (0–45 keV) spectra were obtained from 200 soil samples under pasture and cropland, collected from the surface layer (0–20 cm). The results demonstrated that each soil property, except CaCO3, was significantly affected (p < 0.05) by land-use differences (pasture vs. cropland). Model performance in predicting soil properties varied with both calibration sample selection method and spectral data source. The highest prediction accuracies were obtained using RS for sand (R2 = 0.62), k-means for silt (R2 = 0.65) and pH (R2 = 0.49), KS for clay (R2 = 0.73), SOC (R2 = 0.96), and CaCO3 (R2 = 0.96), and cLHS for EC (R2 = 0.80). MIR generally produced the highest prediction accuracy among the individual spectral datasets for most soil properties, while fused Vis-NIR + MIR spectra improved EC prediction and fused Vis-NIR + pXRF spectra obtained higher prediction accuracy for pH. Calibration sample selection effects were higher for EC and pH, particularly in pXRF and fused spectral datasets. In contrast, SOC was consistently the least affected by calibration sample selection, whereas sand, silt, clay, and CaCO3 generally showed low to moderate variability across the different calibration sample selection strategies. The results demonstrate that the effect of calibration sampling strategy is strongly sensor-dependent and remains significant even when spectral datasets are fused.