Deep learning of the particulate and mineral-associated organic carbon fractions using a compositional transform and mid-infrared spectroscopy
We need soil organic carbon (SOC) and the SOC fractions, the particulate and mineral-associated organic carbon (POC, MAOC), to understand SOC dynamics. They have implications for soil management, carbon sequestration and climate change mitigation. However, conventional laboratory measurements of the...
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Elsevier
2025-03-01
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| author | Mingxi Zhang Zefang Shen Lewis Walden Farid Sepanta Zhongkui Luo Lei Gao Oscar Serrano Raphael A. Viscarra Rossel |
| author_facet | Mingxi Zhang Zefang Shen Lewis Walden Farid Sepanta Zhongkui Luo Lei Gao Oscar Serrano Raphael A. Viscarra Rossel |
| author_sort | Mingxi Zhang |
| collection | DOAJ |
| description | We need soil organic carbon (SOC) and the SOC fractions, the particulate and mineral-associated organic carbon (POC, MAOC), to understand SOC dynamics. They have implications for soil management, carbon sequestration and climate change mitigation. However, conventional laboratory measurements of the SOC fractions, which involve physical or chemical separations, are elaborate, time-consuming and expensive. Mid-infrared (MIR) spectroscopy combined with multivariate modelling can alleviate these limitations because the method can estimate SOC and its fractions rapidly, cost-effectively and accurately. Previous spectroscopic modelling has mostly ignored the compositional nature of the SOC fractions (i.e. SOC = ∑fractions), causing discrepancies in the estimation such that the sum of the fractions does not equal the total SOC. We recorded the MIR spectra (4000–450 cm−1) of 397 soil samples from across Australia and then performed a granulometric fractionation to derive three SOC fractions, the POC in the macroaggregates (250–2000μm, POCmac), POC in the micro-aggregates (50–250μm, POCmic), and MAOC (<50μm). We used the centred log ratio (CLR) method to transform the data compositionally and then modelled POCmac, POCmic, POC (POCmac + POCmic), and MAOC with the spectra, using convolutional neural networks (CNN) and cubist for benchmarking. We interpreted the models using the SHapley Additive exPlanations (SHAP) values and a land use classification of the data. Modelling the CLR-transformed SOC fractions with CNN maintained the composition of the fractions and improved the accuracy of the estimates (Lin’s concordance correlation coefficient (ρc) of 0.58, 0.86, and 0.94 for the POCmac, POCmic, and MAOC), compared to CLR with cubist (ρc of 0.49, 0.84, and 0.87 for the POCmac, POCmic, and MAOC) and cubist with no compositional transformation (ρc of 0.53, 0.85, and 0.88 for the POCmac, POCmic, and MAOC). The SHAP values reflected the compositional modelling and identified important organic and inorganic functional groups that differed by fraction and land use. Our approach can complement conventional physical SOC fractionations and improve the cost-effectiveness of the measurements, especially when there are many samples to measure, thus enhancing our understanding of SOC dynamics. |
| format | Article |
| id | doaj-art-c1bdc9e624d04c0aa47eb33c47f71fb1 |
| institution | DOAJ |
| issn | 1872-6259 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
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| series | Geoderma |
| spelling | doaj-art-c1bdc9e624d04c0aa47eb33c47f71fb12025-08-20T02:47:37ZengElsevierGeoderma1872-62592025-03-0145511720710.1016/j.geoderma.2025.117207Deep learning of the particulate and mineral-associated organic carbon fractions using a compositional transform and mid-infrared spectroscopyMingxi Zhang0Zefang Shen1Lewis Walden2Farid Sepanta3Zhongkui Luo4Lei Gao5Oscar Serrano6Raphael A. Viscarra Rossel7Soil & Landscape Science, School of Molecular & Life Sciences, Faculty of Science & Engineering, Curtin University, GPO Box U1987, Perth WA 6845, AustraliaSoil & Landscape Science, School of Molecular & Life Sciences, Faculty of Science & Engineering, Curtin University, GPO Box U1987, Perth WA 6845, AustraliaSoil & Landscape Science, School of Molecular & Life Sciences, Faculty of Science & Engineering, Curtin University, GPO Box U1987, Perth WA 6845, AustraliaSoil & Landscape Science, School of Molecular & Life Sciences, Faculty of Science & Engineering, Curtin University, GPO Box U1987, Perth WA 6845, AustraliaCollege of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, ChinaEnvironment Business Unit, Commonwealth Scientific and Industrial Research Organisation, Waite Campus, Urrbrae, SA 5064, AustraliaCentro de Estudios Avanzados de Blanes, Consejo Superior de Investigaciones Científicas, Blanes 17300, Spain; School of Science & Centre for Marine Ecosystems Research, Edith Cowan University, Joondalup, WA 6027, AustraliaSoil & Landscape Science, School of Molecular & Life Sciences, Faculty of Science & Engineering, Curtin University, GPO Box U1987, Perth WA 6845, Australia; Corresponding author.We need soil organic carbon (SOC) and the SOC fractions, the particulate and mineral-associated organic carbon (POC, MAOC), to understand SOC dynamics. They have implications for soil management, carbon sequestration and climate change mitigation. However, conventional laboratory measurements of the SOC fractions, which involve physical or chemical separations, are elaborate, time-consuming and expensive. Mid-infrared (MIR) spectroscopy combined with multivariate modelling can alleviate these limitations because the method can estimate SOC and its fractions rapidly, cost-effectively and accurately. Previous spectroscopic modelling has mostly ignored the compositional nature of the SOC fractions (i.e. SOC = ∑fractions), causing discrepancies in the estimation such that the sum of the fractions does not equal the total SOC. We recorded the MIR spectra (4000–450 cm−1) of 397 soil samples from across Australia and then performed a granulometric fractionation to derive three SOC fractions, the POC in the macroaggregates (250–2000μm, POCmac), POC in the micro-aggregates (50–250μm, POCmic), and MAOC (<50μm). We used the centred log ratio (CLR) method to transform the data compositionally and then modelled POCmac, POCmic, POC (POCmac + POCmic), and MAOC with the spectra, using convolutional neural networks (CNN) and cubist for benchmarking. We interpreted the models using the SHapley Additive exPlanations (SHAP) values and a land use classification of the data. Modelling the CLR-transformed SOC fractions with CNN maintained the composition of the fractions and improved the accuracy of the estimates (Lin’s concordance correlation coefficient (ρc) of 0.58, 0.86, and 0.94 for the POCmac, POCmic, and MAOC), compared to CLR with cubist (ρc of 0.49, 0.84, and 0.87 for the POCmac, POCmic, and MAOC) and cubist with no compositional transformation (ρc of 0.53, 0.85, and 0.88 for the POCmac, POCmic, and MAOC). The SHAP values reflected the compositional modelling and identified important organic and inorganic functional groups that differed by fraction and land use. Our approach can complement conventional physical SOC fractionations and improve the cost-effectiveness of the measurements, especially when there are many samples to measure, thus enhancing our understanding of SOC dynamics.http://www.sciencedirect.com/science/article/pii/S001670612500045XCarbon fractionsParticulate organic carbonMineral-associated organic carbonDeep learningLogratio transformationSoil spectroscopic modelling |
| spellingShingle | Mingxi Zhang Zefang Shen Lewis Walden Farid Sepanta Zhongkui Luo Lei Gao Oscar Serrano Raphael A. Viscarra Rossel Deep learning of the particulate and mineral-associated organic carbon fractions using a compositional transform and mid-infrared spectroscopy Geoderma Carbon fractions Particulate organic carbon Mineral-associated organic carbon Deep learning Logratio transformation Soil spectroscopic modelling |
| title | Deep learning of the particulate and mineral-associated organic carbon fractions using a compositional transform and mid-infrared spectroscopy |
| title_full | Deep learning of the particulate and mineral-associated organic carbon fractions using a compositional transform and mid-infrared spectroscopy |
| title_fullStr | Deep learning of the particulate and mineral-associated organic carbon fractions using a compositional transform and mid-infrared spectroscopy |
| title_full_unstemmed | Deep learning of the particulate and mineral-associated organic carbon fractions using a compositional transform and mid-infrared spectroscopy |
| title_short | Deep learning of the particulate and mineral-associated organic carbon fractions using a compositional transform and mid-infrared spectroscopy |
| title_sort | deep learning of the particulate and mineral associated organic carbon fractions using a compositional transform and mid infrared spectroscopy |
| topic | Carbon fractions Particulate organic carbon Mineral-associated organic carbon Deep learning Logratio transformation Soil spectroscopic modelling |
| url | http://www.sciencedirect.com/science/article/pii/S001670612500045X |
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