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A soil sensing mechanism to reach carbon flux at a country scale.

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Title: A soil sensing mechanism to reach carbon flux at a country scale.
Authors: Rodríguez‐Albarracín, Heidy Soledad1 (AUTHOR), Demattê, José A. M.1,2 (AUTHOR) jamdemat@usp.br, Neto, Miguel Palacio Pelaez Carvalho1 (AUTHOR), Rosin, Nícolas Augusto1,3 (AUTHOR), Cerri, Carlos Eduardo Pellegrino1,2 (AUTHOR), da Silva, Carlos Antonio4 (AUTHOR), de Sousa Lima, José R.5 (AUTHOR), de Souza, Eduardo S.6 (AUTHOR), Moitinho, Mara Regina7 (AUTHOR), Darghan Contreras, Aquiles Enrique8 (AUTHOR), Teodoro, Paulo Eduardo9 (AUTHOR), Ratke, Rafael Felippe9 (AUTHOR), dos Santos, Uemeson José10 (AUTHOR), Oresca, Denizard6 (AUTHOR)
Source: Soil Science Society of America Journal. May/Jun2026, Vol. 90 Issue 3, p1-25. 25p.
Subjects: Carbon sequestration, Reflectance spectroscopy, Carbon in soils, Machine learning, Digital soil mapping, Climate change mitigation, Biomes
Abstract: 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 R2 of 0.35 was fitted for FCO2 and a spatial error model with R2 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 Atlântica, 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. Plain Language Summary: Soil is the largest terrestrial carbon reservoir and can be either a source or a 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 properties of the soil. Our objective was to estimate and spatialize net ecosystem production (NEP) for the Brazilian territory, based on visible, near infrared‐shortwave infrared and mid‐infrared reflectance spectroscopy, digital soil mapping, and machine learning. We created models to predict FCO2 and carbon sequestration potential using the physical, chemical, and microbiological properties of the soil as covariates. The NEP map highlights negative balances in the Cerrado, Atlantic Forest, Caatinga, and Amazon biomes, with a 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 carbon fixation potential in agroecosystems and contribute to climate change mitigation. [ABSTRACT FROM AUTHOR]
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Database: Engineering Source
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Abstract: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 R2 of 0.35 was fitted for FCO2 and a spatial error model with R2 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 Atlântica, 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. Plain Language Summary: Soil is the largest terrestrial carbon reservoir and can be either a source or a 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 properties of the soil. Our objective was to estimate and spatialize net ecosystem production (NEP) for the Brazilian territory, based on visible, near infrared‐shortwave infrared and mid‐infrared reflectance spectroscopy, digital soil mapping, and machine learning. We created models to predict FCO2 and carbon sequestration potential using the physical, chemical, and microbiological properties of the soil as covariates. The NEP map highlights negative balances in the Cerrado, Atlantic Forest, Caatinga, and Amazon biomes, with a 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 carbon fixation potential in agroecosystems and contribute to climate change mitigation. [ABSTRACT FROM AUTHOR]
ISSN:03615995
DOI:10.1002/saj2.70258