Portable X-ray fluorescence spectrometry library for Brazilian soils: chemical characterization and prediction of soil particle size fractions across all biomes

Sensor-based soil data libraries have proven valuable for a wide range of applications. Given the increasing adoption of portable X-ray fluorescence (pXRF) spectroscopy for soil-related studies, we aimed to create a library of soils characterized by pXRF and develop prediction models for soil particle size fractions (clay, silt, and sand contents) based on 16,989 samples collected at multiple soil horizons, from all Brazilian biomes (Amazon, Caatinga, Cerrado, Atlantic Forest, Pantanal, and Pampa), under multiple climate zones, land uses and derived from several parent materials. Random Forest (RF), Support Vector Machine (SVM), Gradient Boosting Machine (GBM), and Genetic Programming (GP) were used to train the models. The dataset was split into training (13,593) and independent validation (3,396) sets. The accuracy of models was assessed via root mean square error (RMSE) and coefficient of determination (R 2 ) values. Sand, silt and clay contents ranged from 0 to 100%, 0 to 96%, and 0 to 93%, respectively. Strong correlations were found between particle size fractions and Fe and Si contents, but some strong correlations were biome-dependent, such as those between K and silt (0.77) in Pantanal. Random Forest outperformed the other algorithms, achieving RMSE and R 2 values, respectively, of 8.2% and 0.89 for sand, 5.6% and 0.71 for silt, and 6.3% and 0.89 for clay contents. This pXRF library resulted in adequate predictions for sand and clay contents. This national library may contribute to establish a global library of soils characterized via pXRF. This dataset should be continuously increased to support soil chemical characterization and develop prediction models for varied soil properties.

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Publication Details

Journal
CATENA
Published
2026-09-07
DOI
https://doi.org/10.1016/j.catena.2026.110531
Primary Topic
Soil Geostatistics and Mapping
Type
article
Field-Weighted Citation Impact
0.00

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article

Portable X-ray fluorescence spectrometry library for Brazilian soils: chemical characterization and prediction of soil particle size fractions across all biomes

J. B. V. Gomes, Felipe Haenel Gomes, José Coelho de Araújo Filho, Elias Frank de Araújo et al.
CATENA
Soil Geostatistics and Mapping
article

Portable X-ray fluorescence spectrometry library for Brazilian soils: chemical characterization and prediction of soil particle size fractions across all biomes

J. B. V. Gomes, Felipe Haenel Gomes, José Coelho de Araújo Filho, Elias Frank de Araújo, João Carlos Ker, Gustavo Ribas Curcio, Vinícius Teixeira Andrade, Junior César Avanzi, Alceu Linares Pádua, Wenceslau Geraldes Teixeira, Nilvan Carvalho Melo, Pablo Vidal‐Torrado, Milson Evaldo Serafim, Lucas Rezende Gomide, Cecílio Frois Caldeira Júnior, Luiz Marcos Silva Matos, Alberto Vasconcellos Inda, Sharlles Christian Moreira Dias, Helon Hébano de Freitas Sousa, Maria Aparecida Pereira Pierangeli, Samara Martins Barbosa, Marco Aurélio Carbone Carneiro, Antônio Rodrigues Fernandes, Carlos Henrique Lima de Matos, Igor Alexandre de Souza, Marcelo Mancini, Ronny Sobreira Barbosa, Luiz Roberto Guimarães Guilherme, Luiz Felipe Mesquita, Walane Maria Pereira de Mello Ivo, Paula Godinho Ribeiro, Sílvio Júnio Ramos, Tiago Rodrigues Tavares, Carolina Malala Martins Souza, José Francirlei de Oliveira, Walbert Júnior Reis dos Santos, Anita Fernanda dos Santos Teixeira, Fernanda Almeida Bócoli, Sérgio Henrique Godinho Silva, Bárbara Pereira Christófaro Silva, Jolimar Antônio Schiavo, Gladyston Rodrigues Carvalho, Eduane José de Pádua, David C. Weindorf, Elizio Ferreira Frade, Regla Toujaguez, Rafaella Tatiane Silva de Sá, Jaime Antonio de Almeida, Natalia Salazar Baquero, Bruno Montoani Silva, Jussara Silva Dantas, Michele Duarte de Menezes, Renata Andrade Reis, Fábio Luiz Melquiades, Somsubhra Chakraborty, Marcos Gervasio Pereira, Felipe Soares da Silva, Mateus Rosas Ribeiro Filho, Rogério de Andrade Braga, Henrique Peres Cardoso, Sebastião Andrade, Daiane Batista Silva, José A.M. Demattê, Marcelo de Almeida Guimarães, Graziela Moraes de Cesare Barbosa, Humberto Fontes, Nilton Curi
article en

Abstract

Sensor-based soil data libraries have proven valuable for a wide range of applications. Given the increasing adoption of portable X-ray fluorescence (pXRF) spectroscopy for soil-related studies, we aimed to create a library of soils characterized by pXRF and develop prediction models for soil particle size fractions (clay, silt, and sand contents) based on 16,989 samples collected at multiple soil horizons, from all Brazilian biomes (Amazon, Caatinga, Cerrado, Atlantic Forest, Pantanal, and Pampa), under multiple climate zones, land uses and derived from several parent materials. Random Forest (RF), Support Vector Machine (SVM), Gradient Boosting Machine (GBM), and Genetic Programming (GP) were used to train the models. The dataset was split into training (13,593) and independent validation (3,396) sets. The accuracy of models was assessed via root mean square error (RMSE) and coefficient of determination (R 2 ) values. Sand, silt and clay contents ranged from 0 to 100%, 0 to 96%, and 0 to 93%, respectively. Strong correlations were found between particle size fractions and Fe and Si contents, but some strong correlations were biome-dependent, such as those between K and silt (0.77) in Pantanal. Random Forest outperformed the other algorithms, achieving RMSE and R 2 values, respectively, of 8.2% and 0.89 for sand, 5.6% and 0.71 for silt, and 6.3% and 0.89 for clay contents. This pXRF library resulted in adequate predictions for sand and clay contents. This national library may contribute to establish a global library of soils characterized via pXRF. This dataset should be continuously increased to support soil chemical characterization and develop prediction models for varied soil properties.

CATENAVol. 274
Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, Conselho Nacional de Desenvolvimento Científico e Tecnológico, Fundação de Amparo à Pesquisa do Estado de Minas Gerais
Life in Land
Openalex Percentile: Top 18%
Soil Geostatistics and Mapping
DOIOpen Access PDFOpenAlex
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