Developing a near-infrared spectroscopy calibration algorithm for soil organic carbon content in South Africa
Near-infrared (NIR) spectroscopy has emerged as an easy, rapid and cost-effective alternative for soil organic carbon (SOC) analysis and the accounting of carbon credits. South Africa currently lacks a calibration algorithm for predicting SOC content from NIR spectroscopy. This study aimed to develo...
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Elsevier
2025-06-01
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| Series: | Soil Advances |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2950289625000077 |
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| author | Willie Herman Cloete Gerhard du Preez George Munnik Van Zijl |
| author_facet | Willie Herman Cloete Gerhard du Preez George Munnik Van Zijl |
| author_sort | Willie Herman Cloete |
| collection | DOAJ |
| description | Near-infrared (NIR) spectroscopy has emerged as an easy, rapid and cost-effective alternative for soil organic carbon (SOC) analysis and the accounting of carbon credits. South Africa currently lacks a calibration algorithm for predicting SOC content from NIR spectroscopy. This study aimed to develop a NIR spectroscopy calibration algorithm for SOC content, specific to South Africa. Soil samples were collected from 2 fields and 3 catchments across South Africa. These samples were analysed using the total dry combustion (TDC) method and scanned with a NIR spectrometer. Sixty NIR calibration algorithms were developed on a regional scale. The impact of methodological parameters, such as sample state, sampling design, processing and machine learning models, on the root mean square error (RMSE) of the validation statistics was also assessed. Although 60 regional-scale calibration algorithms were developed, none were suitable (RMSE = 0.39 % and RPIQ > 2) for SOC content prediction, which was attributed to the small sample size (n = 238). However, local calibration models for the Tsitsa catchment and Ottosdal fields presented great accuracy (RMSE < 0.1 and RPIQ > 1.5) that can be used for future SOC content prediction. The study found that the open spectral library global prediction model poorly predicted SOC content using local data (RMSE = 1.23 % and R² = - 0.83). This was attributed to South African samples being underrepresented in the global dataset. Sample state and sampling design were the most influential parameters affecting RMSE. To develop a national calibration algorithm, effort should be placed on developing accurate calibration algorithms for smaller areas that could be added to the national spectral library. |
| format | Article |
| id | doaj-art-9805e9b0c753437a8bc4a07a85f1570f |
| institution | DOAJ |
| issn | 2950-2896 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
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| series | Soil Advances |
| spelling | doaj-art-9805e9b0c753437a8bc4a07a85f1570f2025-08-20T03:20:03ZengElsevierSoil Advances2950-28962025-06-01310003910.1016/j.soilad.2025.100039Developing a near-infrared spectroscopy calibration algorithm for soil organic carbon content in South AfricaWillie Herman Cloete0Gerhard du Preez1George Munnik Van Zijl2Corresponding author.; Unit for Environmental Sciences and Management, Potchefstroom Campus, North-West University, Potchefstroom, North West, South AfricaUnit for Environmental Sciences and Management, Potchefstroom Campus, North-West University, Potchefstroom, North West, South AfricaUnit for Environmental Sciences and Management, Potchefstroom Campus, North-West University, Potchefstroom, North West, South AfricaNear-infrared (NIR) spectroscopy has emerged as an easy, rapid and cost-effective alternative for soil organic carbon (SOC) analysis and the accounting of carbon credits. South Africa currently lacks a calibration algorithm for predicting SOC content from NIR spectroscopy. This study aimed to develop a NIR spectroscopy calibration algorithm for SOC content, specific to South Africa. Soil samples were collected from 2 fields and 3 catchments across South Africa. These samples were analysed using the total dry combustion (TDC) method and scanned with a NIR spectrometer. Sixty NIR calibration algorithms were developed on a regional scale. The impact of methodological parameters, such as sample state, sampling design, processing and machine learning models, on the root mean square error (RMSE) of the validation statistics was also assessed. Although 60 regional-scale calibration algorithms were developed, none were suitable (RMSE = 0.39 % and RPIQ > 2) for SOC content prediction, which was attributed to the small sample size (n = 238). However, local calibration models for the Tsitsa catchment and Ottosdal fields presented great accuracy (RMSE < 0.1 and RPIQ > 1.5) that can be used for future SOC content prediction. The study found that the open spectral library global prediction model poorly predicted SOC content using local data (RMSE = 1.23 % and R² = - 0.83). This was attributed to South African samples being underrepresented in the global dataset. Sample state and sampling design were the most influential parameters affecting RMSE. To develop a national calibration algorithm, effort should be placed on developing accurate calibration algorithms for smaller areas that could be added to the national spectral library.http://www.sciencedirect.com/science/article/pii/S2950289625000077Carbon creditsCarbon sequestrationMachine learningSoil spectroscopy |
| spellingShingle | Willie Herman Cloete Gerhard du Preez George Munnik Van Zijl Developing a near-infrared spectroscopy calibration algorithm for soil organic carbon content in South Africa Soil Advances Carbon credits Carbon sequestration Machine learning Soil spectroscopy |
| title | Developing a near-infrared spectroscopy calibration algorithm for soil organic carbon content in South Africa |
| title_full | Developing a near-infrared spectroscopy calibration algorithm for soil organic carbon content in South Africa |
| title_fullStr | Developing a near-infrared spectroscopy calibration algorithm for soil organic carbon content in South Africa |
| title_full_unstemmed | Developing a near-infrared spectroscopy calibration algorithm for soil organic carbon content in South Africa |
| title_short | Developing a near-infrared spectroscopy calibration algorithm for soil organic carbon content in South Africa |
| title_sort | developing a near infrared spectroscopy calibration algorithm for soil organic carbon content in south africa |
| topic | Carbon credits Carbon sequestration Machine learning Soil spectroscopy |
| url | http://www.sciencedirect.com/science/article/pii/S2950289625000077 |
| work_keys_str_mv | AT williehermancloete developinganearinfraredspectroscopycalibrationalgorithmforsoilorganiccarboncontentinsouthafrica AT gerharddupreez developinganearinfraredspectroscopycalibrationalgorithmforsoilorganiccarboncontentinsouthafrica AT georgemunnikvanzijl developinganearinfraredspectroscopycalibrationalgorithmforsoilorganiccarboncontentinsouthafrica |