Assessing models for prediction of some soil chemical properties from portable X-ray fluorescence (pXRF) spectrometry data in Brazilian Coastal Plains

• Published in: Geoderma Vol. 357; p. 113957
• Main Authors: Andrade, Renata, Silva, Sérgio Henrique Godinho, Weindorf, David C., Chakraborty, Somsubhra, Faria, Wilson Missina, Mesquita, Luiz Felipe, Guilherme, Luiz Roberto Guimarães, Curi, Nilton
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.01.2020
• Subjects: A horizons; algorithms; artificial intelligence; B horizons; Cation exchange capacity; coastal plains; Cohesive soils; Entisols; fluorescence; Kaolinitic soils; least squares; Machine learning algorithms; Oxisols; prediction; Soil organic matter; soil sampling; Spodosols; Total nitrogen; Ultisols; X-ray fluorescence spectroscopy; CEC; Machine learning algorithms; pXRF; Kaolinitic soils; RMSE; BCP; CR; PCA; SOM; CS; XGB; Cation exchange capacity; Total nitrogen; RF; Cohesive soils; OLS; Soil organic matter; TN; MSE

Portable X-ray fluorescence (pXRF) spectrometry is becoming increasingly popular for predicting soil properties worldwide. However, there are still very few works on this subject under tropical conditions. Therefore, the objectives of this study were to use pXRF data to characterize the Brazilian Co...