Fusion of Gamma-rays and portable X-ray fluorescence spectral data to measure extractable potassium in soils


Nawar S., Richard F., Kassim A. M., TEKİN Y., Mouazen A. M.

Soil and Tillage Research, vol.223, 2022 (SCI-Expanded) identifier

  • Publication Type: Article / Article
  • Volume: 223
  • Publication Date: 2022
  • Doi Number: 10.1016/j.still.2022.105472
  • Journal Name: Soil and Tillage Research
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, BIOSIS, CAB Abstracts, Environment Index, Pollution Abstracts, Veterinary Science Database
  • Keywords: Data fusion, Partial least squares regression, Proximal soil sensing, Soil potassium
  • Bursa Uludag University Affiliated: Yes

Abstract

© 2022 Elsevier B.V.The detection and mapping of plant-extractable potassium (Ka) using proximal soil sensing and data fusion (DF) techniques are essential to optimise K2O fertiliser application, improve crop yield, and minimise environmental and financial costs. This work evaluates the potential of combined use of portable gamma ray and x-ray fluorescence spectroscopy for in field detection and mapping of Ka. After subjected to various pre-processing methods, spectral data were split into calibration (75%) and validation (25%) sets, and single sensor and DF models were developed using partial least squares regression (PLSR). Maps of Ka were used to present spatial variability of potassium across an 8.4 ha Voor de Hoeves target field, in Flanders, Belgium. Results showed that the gamma-ray PLSR model using wet soils had greater predictive ability with coefficient of determination (R2) = 0.71, ratio of performance deviation (RPD) = 1.89, root mean square error (RMSE) = 31.7 mg kg-1, and ratio of performance to interquartile range (RPIQ) = 2.36 than the corresponding wet-XRF PLSR model (R2 = 0.61, RPD = 1.64, RMSE = 48.8 mg kg-1 and RPIQ = 1.84). The DF PLSR model on wet soils, resulted in a more accurate Ka predictive ability (R2 = 0.75, RPD = 2.03, RMSE = 31.3 mg kg-1 and RPIQ = 2.79), compared to the individual gamma ray or XRF sensors in wet soils. The best accuracy was obtained with XRF spectrometer using dry samples (R2 = 0.77, RPD = 2.14, RMSE = 26.5 mg kg-1 and RPIQ = 3.39). All Ka prediction maps showed spatial similarity to the corresponding measured maps in the target field. In conclusion, since DF increased the Ka prediction accuracy compared to the single sensor models using wet soils, it is recommended to be adopted in future studies as a potential solution for Ka measurement, mapping, and ultimately for site-specific K2O fertilisation management. The XRF analysis of dry spectra is recommended as the best method for accurate measurement of Ka.