In-situ prediction of soil organic carbon contents in wheat-rice rotation fields via visible near-infrared spectroscopy
Visible near-infrared (VNIR) spectroscopy is a reliable method for estimating soil properties. However, its effectiveness in accurately predicting soil organic carbon (SOC) contents, particularly in wheat-rice rotation fields, remains uncertain. In this study, we collected 202 samples from wheat-ric...
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| Language: | English |
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Elsevier
2024-11-01
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| Series: | Soil & Environmental Health |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2949919424000566 |
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| author | Lingju Dai Jie Xue Rui Lu Zheng Wang Zhongxing Chen Qiangyi Yu Zhou Shi Songchao Chen |
| author_facet | Lingju Dai Jie Xue Rui Lu Zheng Wang Zhongxing Chen Qiangyi Yu Zhou Shi Songchao Chen |
| author_sort | Lingju Dai |
| collection | DOAJ |
| description | Visible near-infrared (VNIR) spectroscopy is a reliable method for estimating soil properties. However, its effectiveness in accurately predicting soil organic carbon (SOC) contents, particularly in wheat-rice rotation fields, remains uncertain. In this study, we collected 202 samples from wheat-rice fields (0–20 cm) in southeastern China and measured in-situ spectra of the vertical surface of the soil cores and the laboratory spectra of the dried and sieved soil samples. Our study focused on evaluating three algorithms - external parameter orthogonalization (EPO), direct standardization (DS), and piecewise direct standardization (PDS) - to address the influence of external factors, particularly soil moisture. To carry out our analysis, the dataset was divided into calibration (141 samples) and validation (61 samples) sets via the Kennard-Stone algorithm. A subset of the corresponding in-situ and laboratory spectra in the calibration set (transfer set) was used to derive the transfer matrix for EPO, DS, and PDS, enabling the conversion of in-situ spectra to laboratory spectra by characterizing their differences. Four machine learning models, including cubist, partial least squares regression (PLSR), random forest (RF), and memory-based learning (MBL), were used to predict the SOC, particulate organic carbon (POC), and mineral-associated organic carbon (MAOC) contents based on the laboratory, in-situ, and corrected in-situ spectra. The results revealed that the laboratory spectra outperformed the non-corrected in-situ spectra, with coefficients of determination (R2) of 0.91, 0.75, and 0.80 for SOC, POC, and MAOC, respectively. Among the models, MBL and PLSR exhibited the highest average R2 at 0.85–0.86. EPO marginally improved the prediction accuracy (R2 increased from 0.85 to 0.87 for SOC, 0.64 to 0.69 for POC, and 0.75 to 0.82 for MAOC). These promising prediction accuracies underscore the potential of VNIR spectra for in-situ predictions in wheat-rice fields in Southeast China, offering insights for predicting SOC contents via in-situ spectroscopy. |
| format | Article |
| id | doaj-art-42da9344e21d47d9ac56bc6ffa5075b6 |
| institution | Kabale University |
| issn | 2949-9194 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Soil & Environmental Health |
| spelling | doaj-art-42da9344e21d47d9ac56bc6ffa5075b62024-11-17T04:53:02ZengElsevierSoil & Environmental Health2949-91942024-11-0124100113In-situ prediction of soil organic carbon contents in wheat-rice rotation fields via visible near-infrared spectroscopyLingju Dai0Jie Xue1Rui Lu2Zheng Wang3Zhongxing Chen4Qiangyi Yu5Zhou Shi6Songchao Chen7College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China; ZJU-Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University, Hangzhou 311215, ChinaDepartment of Land Management, Zhejiang University, Hangzhou 310058, ChinaCollege of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, ChinaCollege of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, ChinaCollege of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China; ZJU-Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University, Hangzhou 311215, ChinaInstitute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, ChinaCollege of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, ChinaCollege of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China; ZJU-Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University, Hangzhou 311215, China; Corresponding author. College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China.Visible near-infrared (VNIR) spectroscopy is a reliable method for estimating soil properties. However, its effectiveness in accurately predicting soil organic carbon (SOC) contents, particularly in wheat-rice rotation fields, remains uncertain. In this study, we collected 202 samples from wheat-rice fields (0–20 cm) in southeastern China and measured in-situ spectra of the vertical surface of the soil cores and the laboratory spectra of the dried and sieved soil samples. Our study focused on evaluating three algorithms - external parameter orthogonalization (EPO), direct standardization (DS), and piecewise direct standardization (PDS) - to address the influence of external factors, particularly soil moisture. To carry out our analysis, the dataset was divided into calibration (141 samples) and validation (61 samples) sets via the Kennard-Stone algorithm. A subset of the corresponding in-situ and laboratory spectra in the calibration set (transfer set) was used to derive the transfer matrix for EPO, DS, and PDS, enabling the conversion of in-situ spectra to laboratory spectra by characterizing their differences. Four machine learning models, including cubist, partial least squares regression (PLSR), random forest (RF), and memory-based learning (MBL), were used to predict the SOC, particulate organic carbon (POC), and mineral-associated organic carbon (MAOC) contents based on the laboratory, in-situ, and corrected in-situ spectra. The results revealed that the laboratory spectra outperformed the non-corrected in-situ spectra, with coefficients of determination (R2) of 0.91, 0.75, and 0.80 for SOC, POC, and MAOC, respectively. Among the models, MBL and PLSR exhibited the highest average R2 at 0.85–0.86. EPO marginally improved the prediction accuracy (R2 increased from 0.85 to 0.87 for SOC, 0.64 to 0.69 for POC, and 0.75 to 0.82 for MAOC). These promising prediction accuracies underscore the potential of VNIR spectra for in-situ predictions in wheat-rice fields in Southeast China, offering insights for predicting SOC contents via in-situ spectroscopy.http://www.sciencedirect.com/science/article/pii/S2949919424000566External parameter orthogonalizationMineral-associated organic carbonPiecewise direct standardizationPartial least squares regressionCalibration transfer methodsParticulate organic carbon |
| spellingShingle | Lingju Dai Jie Xue Rui Lu Zheng Wang Zhongxing Chen Qiangyi Yu Zhou Shi Songchao Chen In-situ prediction of soil organic carbon contents in wheat-rice rotation fields via visible near-infrared spectroscopy Soil & Environmental Health External parameter orthogonalization Mineral-associated organic carbon Piecewise direct standardization Partial least squares regression Calibration transfer methods Particulate organic carbon |
| title | In-situ prediction of soil organic carbon contents in wheat-rice rotation fields via visible near-infrared spectroscopy |
| title_full | In-situ prediction of soil organic carbon contents in wheat-rice rotation fields via visible near-infrared spectroscopy |
| title_fullStr | In-situ prediction of soil organic carbon contents in wheat-rice rotation fields via visible near-infrared spectroscopy |
| title_full_unstemmed | In-situ prediction of soil organic carbon contents in wheat-rice rotation fields via visible near-infrared spectroscopy |
| title_short | In-situ prediction of soil organic carbon contents in wheat-rice rotation fields via visible near-infrared spectroscopy |
| title_sort | in situ prediction of soil organic carbon contents in wheat rice rotation fields via visible near infrared spectroscopy |
| topic | External parameter orthogonalization Mineral-associated organic carbon Piecewise direct standardization Partial least squares regression Calibration transfer methods Particulate organic carbon |
| url | http://www.sciencedirect.com/science/article/pii/S2949919424000566 |
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