Title:	A soil sensing mechanism to reach carbon flux at a country scale.
Authors:	RODRÍGUEZ‐ALBARRACÍN, H. S. DEMATTÊ, J. A. M. CARVALHO NETO, M. P. P. ROSIN, N. A. CERRI, C. E. P. SILVA, C. A. da LIMA, J. R. de S. SOUZA, E. S. de MOITINHO, M. R. CONTRERAS, A. E. D. TEODORO, P. E. RATKE, R. F. SANTOS, U. J. dos ORESCA, D.
Affiliation:	HEIDY SOLEDAD RODRÍGUEZ‐ALBARRACÍN, UNIVERSIDADE DE SÃO PAULO JOSÉ A. M. DEMATTÊ, UNIVERSIDADE DE SÃO PAULO MIGUEL PALACIO PELAEZ CARVALHO NETO, UNIVERSIDADE DE SÃO PAULO NICOLAS AUGUSTO ROSIN, CNPS CARLOS EDUARDO PELLEGRINO CERRI, UNIVERSIDADE DE SÃO PAULO CARLOS ANTONIO DA SILVA, UNIVERSIDADE DO ESTADO DE MATO GROSSO JOSÉ R. DE SOUSA LIMA, UNIVERSIDADE FEDERAL DO AGRESTE DE PERNAMBUCO EDUARDO S. DE SOUZA, UNIVERSIDADE FEDERAL RURAL DE PERNAMBUCO MARA REGINA MOITINHO, UNIVERSITY OF FLORIDA AQUILES ENRIQUE DARGHAN CONTRERAS, UNIVERSIDAD NACIONAL DE COLOMBIA PAULO EDUARDO TEODORO, UNIVERSIDADE FEDERAL DE MATO GROSSO DO SUL RAFAEL FELIPPE RATKE, UNIVERSIDADE FEDERAL DE MATO GROSSO DO SUL UEMESON JOSÉ DOS SANTOS, INSTITUTO FEDERAL DE EDUCAÇÃO, CIÊNCIA E TECNOLOGIA DO PARÁ DENIZARD ORESCA, UNIVERSIDADE FEDERAL RURAL DE PERNAMBUCO.
Date Issued:	2026
Citation:	Soil Science Society of America Journal, v. 90, n. 3, e70258, May/Jun. 2026.
Description:	Soil is the largest terrestrial carbon reservoir and can be a source or sink of CO2 for the atmosphere, depending on management practices. CO2 emissions from the soil surface (FCO2) are directly related to the biological and physicochemical soil properties. Our objective was to estimate and spatialize the net ecosystem production (NEP) for the Brazilian territory, using visible (400-700 nm), near infrared (700-1100 nm), shortwave infrared (1100-2500 nm), (and mid-infrared (2500-25,000 nm, 4000-400 cm- 1) reflectance spectroscopy, digital soil mapping, and machine learning. We created FCO2 and carbon sequestration potential prediction models using soil physical, chemical, and microbiological properties as covariates, while the spatialization was based on a bare soil image, relief, climate, and soil mineralogy. A multivariate regression model with R 2 of 0.35 was fitted for FCO2 and a spatial error model with R 2 0.76 for carbon sequestration. The accuracy of the spatialization ranged from 0.41 to 0.76, with a correlation of 0.56 in an external validation. The NEP map highlights negative balances in the Cerrado, Mata Atl & acirc;ntica, Caatinga, and Amazon biomes, with strong influence of mineralogy, where soils rich in iron oxides are below their carbon-storage capacity. Our methodology can be used as an approximation of the C fixation potential in agroecosystems and contribute to climate change mitigation.
NAL Thesaurus:	Near-infrared spectroscopy Digital elevation models Carbon sequestration
Keywords:	Modelo digital de elevação Aprendizado de máquina Seqüestro de carbono Espectroscopia no infravermelho próximo Mapeamento digital do solo Machine Learning Digital Soil Mapping
DOI:	https://doi.org/10.1002/saj2.70258
Type of Material:	Artigo de periódico
Access:	openAccess
Appears in Collections:	Artigo em periódico indexado (CNPS)