Effective spectral pre-processing methods enhance accuracy of soil property prediction by NIR spectroscopy
Abstract Near-infrared (NIR) spectroscopy is a fast, non-invasive, and effective technique that has gained widespread use in soil analysis. Pre-processing plays an essential role in enhancing the precision of calibrating NIR spectra with laboratory-measured soil properties. This research assessed th...
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Springer
2025-08-01
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| Series: | Discover Applied Sciences |
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| Online Access: | https://doi.org/10.1007/s42452-025-07580-3 |
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| author | Mehdi Eslamifar Hamed Tavakoli Eiko Thiessen Rainer Kock José Correa Eberhard Hartung |
| author_facet | Mehdi Eslamifar Hamed Tavakoli Eiko Thiessen Rainer Kock José Correa Eberhard Hartung |
| author_sort | Mehdi Eslamifar |
| collection | DOAJ |
| description | Abstract Near-infrared (NIR) spectroscopy is a fast, non-invasive, and effective technique that has gained widespread use in soil analysis. Pre-processing plays an essential role in enhancing the precision of calibrating NIR spectra with laboratory-measured soil properties. This research assessed the efficacy of two two-band index transformations—simple ratio indices (SRI) and normalized difference indices (NDI)—in addition to four distinct three-band index transformations (TBI) for predicting various soil characteristics using NIR spectroscopy in a laboratory setting. A total of 333 soil samples were sourced from different farms across Northern Germany, analyzed using two NIR spectrometers, and their properties were measured in a certified lab. Several feature selection approaches, including recursive feature elimination (RFE) and the least absolute shrinkage and selection operator (LASSO), were employed to identify the most significant wavebands. Calibration models were developed using partial least squares regression (PLSR) and LASSO regression. The results indicated that index transformations considerably enhanced the predictive performance of the models. Model performance was assessed through several metrics, including the coefficient of determination (R2), root mean square error (RMSE), and the ratio of performance to deviation (RPD). Compared to unprocessed data, R2 values improved by up to 0.13, 0.30, and 0.23 for organic matter (OM), pH, and phosphorus (P2O5), respectively. The optimal models for estimating OM (R2=0.59, RMSE = 1.61%, RPD = 1.79), pH (R2=0.63, RMSE = 0.28, RPD = 1.73), and P2O5 (R2=0.46, RMSE = 16.1 mg/100 g, RPD = 1.46) were attributed to TBI transformations on selected wavebands, calibrated using PLSR. These findings highlight that NIR spectroscopy, even with a limited spectral range (950–1650 nm), can provide reliable estimates of soil properties when combined with suitable pre-processing methods. |
| format | Article |
| id | doaj-art-b28fce33cc244567af0585319d71a2e2 |
| institution | DOAJ |
| issn | 3004-9261 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Applied Sciences |
| spelling | doaj-art-b28fce33cc244567af0585319d71a2e22025-08-20T03:05:55ZengSpringerDiscover Applied Sciences3004-92612025-08-017812710.1007/s42452-025-07580-3Effective spectral pre-processing methods enhance accuracy of soil property prediction by NIR spectroscopyMehdi Eslamifar0Hamed Tavakoli1Eiko Thiessen2Rainer Kock3José Correa4Eberhard Hartung5Institute of Agricultural Engineering, Kiel UniversityDepartment of Agromechatronics, Leibniz Institute for Agricultural Engineering and Bioeconomy e.V. (ATB)Institute of Agricultural Engineering, Kiel UniversityInstitute of Agricultural Engineering, Kiel UniversityDepartment of Agromechatronics, Leibniz Institute for Agricultural Engineering and Bioeconomy e.V. (ATB)Institute of Agricultural Engineering, Kiel UniversityAbstract Near-infrared (NIR) spectroscopy is a fast, non-invasive, and effective technique that has gained widespread use in soil analysis. Pre-processing plays an essential role in enhancing the precision of calibrating NIR spectra with laboratory-measured soil properties. This research assessed the efficacy of two two-band index transformations—simple ratio indices (SRI) and normalized difference indices (NDI)—in addition to four distinct three-band index transformations (TBI) for predicting various soil characteristics using NIR spectroscopy in a laboratory setting. A total of 333 soil samples were sourced from different farms across Northern Germany, analyzed using two NIR spectrometers, and their properties were measured in a certified lab. Several feature selection approaches, including recursive feature elimination (RFE) and the least absolute shrinkage and selection operator (LASSO), were employed to identify the most significant wavebands. Calibration models were developed using partial least squares regression (PLSR) and LASSO regression. The results indicated that index transformations considerably enhanced the predictive performance of the models. Model performance was assessed through several metrics, including the coefficient of determination (R2), root mean square error (RMSE), and the ratio of performance to deviation (RPD). Compared to unprocessed data, R2 values improved by up to 0.13, 0.30, and 0.23 for organic matter (OM), pH, and phosphorus (P2O5), respectively. The optimal models for estimating OM (R2=0.59, RMSE = 1.61%, RPD = 1.79), pH (R2=0.63, RMSE = 0.28, RPD = 1.73), and P2O5 (R2=0.46, RMSE = 16.1 mg/100 g, RPD = 1.46) were attributed to TBI transformations on selected wavebands, calibrated using PLSR. These findings highlight that NIR spectroscopy, even with a limited spectral range (950–1650 nm), can provide reliable estimates of soil properties when combined with suitable pre-processing methods.https://doi.org/10.1007/s42452-025-07580-3Feature selectionProximal soil sensingPartial least square regressionSpectral pre-processingThree-band indices transformationTwo-band indices transformation |
| spellingShingle | Mehdi Eslamifar Hamed Tavakoli Eiko Thiessen Rainer Kock José Correa Eberhard Hartung Effective spectral pre-processing methods enhance accuracy of soil property prediction by NIR spectroscopy Discover Applied Sciences Feature selection Proximal soil sensing Partial least square regression Spectral pre-processing Three-band indices transformation Two-band indices transformation |
| title | Effective spectral pre-processing methods enhance accuracy of soil property prediction by NIR spectroscopy |
| title_full | Effective spectral pre-processing methods enhance accuracy of soil property prediction by NIR spectroscopy |
| title_fullStr | Effective spectral pre-processing methods enhance accuracy of soil property prediction by NIR spectroscopy |
| title_full_unstemmed | Effective spectral pre-processing methods enhance accuracy of soil property prediction by NIR spectroscopy |
| title_short | Effective spectral pre-processing methods enhance accuracy of soil property prediction by NIR spectroscopy |
| title_sort | effective spectral pre processing methods enhance accuracy of soil property prediction by nir spectroscopy |
| topic | Feature selection Proximal soil sensing Partial least square regression Spectral pre-processing Three-band indices transformation Two-band indices transformation |
| url | https://doi.org/10.1007/s42452-025-07580-3 |
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