Quantification of SOC and Clay Content Using Visible Near-Infrared Reflectance–Mid-Infrared Reflectance Spectroscopy With Jack-Knifing Partial Least Squares Regression

Yi, Peng; Knadel, Maria; Gislum, René; Schelde, Kirsten; Thomsen, Anne Belinda; Greve, Mogens Humlekrog

doi:10.1097/ss.0000000000000074

Cited by 33 publications

(11 citation statements)

References 30 publications

(46 reference statements)

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“…Our results are also better than those reported (R 2 = 0.88), with 125 samples (S 2 = 1.82) collected from a Danish field (4.6 ha) situated on an old moraine with a podzol soil type (Y. Peng et al, 2014), using a single ML model. The improved results obtained in the current study may be attributed to the ability of the SG-ML to overcome the nonlinear relationship between the response variable and predictor variables, which can decrease the accuracy of the OC model (Nawar & Mouazen, 2019).…”

Section: The Performance Of Oc Modelscontrasting

confidence: 86%

Combining mid infrared spectroscopy with stacked generalisation machine learning for prediction of key soil properties

Nawar

Mouazen

2022

European J Soil Science

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Accurate assessment of key soil attributes such as soil organic carbon (OC), available phosphorus (P), and available potassium (K) using mid‐infrared spectroscopy (MIRS) is essential for better soil management in precision agriculture. However, the calibration of the portable version of MIRS is more challenging than the benchmark technologies, hence, demanding more efficient modelling methods to provide accurate outcomes. This research aims to use the stacked generalisation machine learning (SG–ML) framework, combining support vector machine (SVM), gradient boosted regression (GBR), and random forest (RF), using linear ridge regression as a meta learner, for predicting OC, P, and K using MIR spectra of 375 soil samples collected from four farms (Flanders, Belgium). The performance of the SG–ML models was compared with the multilayer perceptron (MLP) deep learning (DL) method. Results showed the superiority of the SG–ML method over the corresponding single ML and DL models. The predictive performance of SG–ML using the validation set was excellent for the three soil attributes, with coefficient of determination (R2) and root mean square error (RMSE) values of 0.88% and 0.10%, 0.85 and 4.53 mg 100 g−1, and 0.84 and 3.87 mg 100 g−1 for OC, K, and P, respectively. The performance of DL models were good for OC (R2 = 0.65, and RMSE = 0.17%), poor for K (R2 = 0.58 and RMSE = 7.59 mg 100 g−1), and very poor for P (R2 = 0.46, and RMSE = 6.57 mg 100 g−1). The SG–ML reduced the prediction RMSE by 10% to 31%, compared with the single ML (SVM, RF, and GBR) models. In summary, the proposed stacking method is a powerful modelling tool for the accurate prediction of key soil attributes using portable MIRS. Highlights Machine learning (ML) and deep learning (DL) models were developed based on mid‐infrared soil spectra. The stacked generalisation ML (SG–ML) was compared with ML and DL for predicting OC, P, and K. SG–ML models outperformed ML (GBR, RF, and SVM) and deep learning (DL) models. The SG–ML models significantly decreased RMSE by 10% to 31% compared with the single ML models.

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Section: The Performance Of Oc Modelscontrasting

confidence: 86%

Combining mid infrared spectroscopy with stacked generalisation machine learning for prediction of key soil properties

Nawar

Mouazen

2022

European J Soil Science

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“…It would be a considerable benefit to be able to use existing archival calibration models for use with handheld MIR spectrometers (Peng et al 2014). Calibration transfer software may be required, for example, so that a particular calibration developed on a 'master' instrument can be used on a 'slave' instrument, or when calibrations need to be transferred from an older obsolete instrument.…”

Section: Introductionmentioning

confidence: 99%

Feasibility of handheld mid-infrared spectroscopy to predict particle size distribution: influence of soil field condition and utilisation of existing spectral libraries

2020

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Partial least-squares regression (PLSR), using spectra from a handheld mid-infrared instrument (the ExoScan), was tested for the prediction of particle size distribution. Soils were sampled from agricultural sites in the Eyre Peninsula under field conditions and with varying degrees of soil preparation. Issues relevant to field sampling were identified, such as sample heterogeneity, micro-aggregate size and moisture content. The PLSR models for particle size distribution were derived with the varying degrees of preparation. Cross-validation of clay content in the as-received in situ soils resulted in low accuracy: coefficient of determination (R2) = 0.55 and root mean square error (RMSE) = 7%. This was improved by manual mixing, drying, sieving to < 2 mm and fine grinding, resulting in R2 values of 0.64, 0.75 and 0.81, and RMSE of 6%, 5% and 4% respectively; less improvement resulted for sand, with corresponding R2 values of 0.82, 0.88, 0.91 and 0.89, and RMSE of 10%, 8%, 6% and 7%. Predictions for silt remained poor. Where only archival benchtop calibration models were available, predictions of clay contents for spectra scanned with the handheld ExoScan spectrometer resulted in high error because of spectral intensity mismatch between benchtop and handheld spectra (R2 = 0.72, RMSE = 24.2% and bias = 21%). Pre-processing the benchtop spectra by piecewise direct standardisation resulted in more successful predictions (R2 = 0.73, RMSE = 6.7% and bias = –1.5%), confirming the advantage of piecewise direct standardisation for prediction from archival spectral libraries.

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“…Computed tomography (CT) was introduced in soil science several decades ago (Petrovic et al, 1982). Since then, it has been used to assess porosity and pore size distribution (inversely related to bulk density), tortuosity, soil structure, and compaction (Lopes et al, 1999;Pires et al, 2010).…”

Section: Computed Tomographymentioning

confidence: 99%

Proximal sensing for soil carbon accounting

England

Rossel

2018

SOIL

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Abstract. Maintaining or increasing soil organic carbon (C) is vital for securing food production and for mitigating greenhouse gas (GHG) emissions, climate change, and land degradation. Some land management practices in cropping, grazing, horticultural, and mixed farming systems can be used to increase organic C in soil, but to assess their effectiveness, we need accurate and cost-efficient methods for measuring and monitoring the change. To determine the stock of organic C in soil, one requires measurements of soil organic C concentration, bulk density, and gravel content, but using conventional laboratory-based analytical methods is expensive. Our aim here is to review the current state of proximal sensing for the development of new soil C accounting methods for emissions reporting and in emissions reduction schemes. We evaluated sensing techniques in terms of their rapidity, cost, accuracy, safety, readiness, and their state of development. The most suitable method for measuring soil organic C concentrations appears to be visible–near-infrared (vis–NIR) spectroscopy and, for bulk density, active gamma-ray attenuation. Sensors for measuring gravel have not been developed, but an interim solution with rapid wet sieving and automated measurement appears useful. Field-deployable, multi-sensor systems are needed for cost-efficient soil C accounting. Proximal sensing can be used for soil organic C accounting, but the methods need to be standardized and procedural guidelines need to be developed to ensure proficient measurement and accurate reporting and verification. These are particularly important if the schemes use financial incentives for landholders to adopt management practices to sequester soil organic C. We list and discuss requirements for developing new soil C accounting methods based on proximal sensing, including requirements for recording, verification, and auditing.

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Quantification of SOC and Clay Content Using Visible Near-Infrared Reflectance–Mid-Infrared Reflectance Spectroscopy With Jack-Knifing Partial Least Squares Regression

Cited by 33 publications

References 30 publications

Combining mid infrared spectroscopy with stacked generalisation machine learning for prediction of key soil properties

Combining mid infrared spectroscopy with stacked generalisation machine learning for prediction of key soil properties

Feasibility of handheld mid-infrared spectroscopy to predict particle size distribution: influence of soil field condition and utilisation of existing spectral libraries

Proximal sensing for soil carbon accounting

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