Fine grinding is needed to maintain the high accuracy of mid‐infrared diffuse reflectance spectroscopy for soil property estimation

Wijewardane, Nuwan K.; Ge, Yufeng; Sanderman, Jonathan; Ferguson, Richard

doi:10.1002/saj2.20194

Cited by 18 publications

(12 citation statements)

References 45 publications

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“…Reduce the costs of analyses and enable higher throughput Baldock et al, 2014;Wijewardane et al, 2018Wijewardane et al, , 2020Dangal et al, 2019;Yu et al, 2020 Improves baselining and reduces model uncertainty to generate high-value credits New metrics for soil health Earlier identification of practices that have an impact on soil carbon sequestration Conant et al, 2011;Cotrufo et al, 2015;Vos et al, 2018;Lavallee et al, 2020 inorganic carbon amendments such as biochar to enhance carbon sequestration, provided that the carbon remains in the project area. Properly quantifying and verifying the inorganic carbon stocks, however, is challenging as it is not currently estimated by biogeochemical models.…”

Section: Improved Lab Analytical Methodsmentioning

confidence: 99%

Implementing the Soil Enrichment Protocol at Scale: Opportunities for an Agricultural Carbon Market

et al. 2021

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High-quality agricultural carbon credits that incentivize regenerative practices can help address climate change through greenhouse gas (GHG) abatement and CO2 sequestration. Generating large volumes of such credits requires rigorous crediting methodologies. The Soil Enrichment Protocol (SEP) by the Climate Action Reserve (CAR) aims to unlock this type of crediting potential. The SEP includes new expert-driven standards for validating the use of soil biogeochemical modeling to generate credits. Technical experts at Indigo Ag participated in the SEP working group and are supporting implementation of the first project, CAR 1459_RP1, on hundreds of thousands of acres in the US. The authors share their thoughts on new approaches enabled by the SEP as both contributors to the theory behind and practitioners of these approaches. The SEP enables scalable, high-quality credits through four main advances: (1) allowing flexibility in the use of biogeochemical models that meet explicit performance requirements, (2) enabling a new approach to field-level, modeled baselines, (3) supporting a hybrid approach of credit generation using both soil measurement and modeling, and (4) requiring a new type of credit uncertainty quantification that accounts for multiple sources of uncertainty. Together these advances support agricultural credit quantification that enables payments to offset transitional costs for growers, at large enough scales to create a robust market, with a level of rigor that ensures any credited emission reductions have real climate impact. Innovations in soil analyses, advances in research, and improvements in data collection could further improve the potential for agricultural carbon credits to scale.

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Section: Improved Lab Analytical Methodsmentioning

confidence: 99%

Implementing the Soil Enrichment Protocol at Scale: Opportunities for an Agricultural Carbon Market

et al. 2021

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“… Nduwamungu et al (2009) did not find improvements in predictions from < 2 mm soil samples for the NIR range. Both LeGuillou et al (2015) and Wijewardane et al (2020) reported that predictions from fine ground samples always outperformed those from non-fine ground for the MIR region. Results in this study align with the literature.…”

Section: Resultsmentioning

confidence: 97%

“…A number of studies have researched these effects for (V)NIR/MIR soil spectroscopy. For example, studies analysed the effect of different particle sizes ( Nduwamungu et al, 2009 , LeGuillou et al, 2015 , Coutinho et al, 2019 , Wijewardane et al, 2020 ), soil water content ( Bogrekci and Lee, 2006 , Minasny et al, 2011 , Ji et al, 2015 ) and in-field (V)NIR measurements ( Stevens et al, 2008 ; Gras et al, 2014 ) on soil spectroscopy predictions.…”

Section: Introductionmentioning

confidence: 99%

Comparing the effect of different sample conditions and spectral libraries on the prediction accuracy of soil properties from near- and mid-infrared spectra at the field-scale

Breure

Prout

Haefele

et al. 2022

Soil and Tillage Research

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The prediction accuracy of soil properties by proximal soil sensing has made their application more practical. However, in order to gain sufficient accuracy, samples are typically air-dried and milled before spectral measurements are made. Calibration of the spectra is usually achieved by making wet chemistry measurements on a subset of the field samples and local regression models fitted to aid subsequent prediction. Both sample handling and wet chemistry can be labour and resource intensive. This study aims to quantify the uncertainty associated with soil property estimates from different methods to reduce effort of field-scale calibrations of soil spectra. We consider two approaches to reduce these expenses for predictions made from visible-near-infrared ((V)NIR), mid-infrared (MIR) spectra and their combination. First, we considered reducing the level of processing of the samples by comparing the effect of different sample conditions (in-situ, unprocessed, air-dried and milled). Second, we explored the use of existing spectral libraries to inform calibrations (based on milled samples from the UK National Soil Inventory) with and without ‘spiking’ the spectral libraries with a small subset of samples from the study fields. Prediction accuracy of soil organic carbon, pH, clay, available P and K for each of these approaches was evaluated on samples from agricultural fields in the UK. Available P and K could only be moderately predicted with the field-scale dataset where samples were milled. Therefore this study found no evidence to suggest that there is scope to reduce costs associated with sample processing or field-scale calibration for available P and K. However, the results showed that there is potential to reduce time and cost implications of using (V)NIR and MIR spectra to predict soil organic carbon, clay and pH. Compared to field-scale calibrations from milled samples, we found that reduced sample processing lowered the ratio of performance to inter-quartile range (RPIQ) between 0% and 76%. The use of spectral libraries reduced the RPIQ of predictions relative to field-scale calibrations from milled samples between 54% and 82% and the RPIQ was reduced between 29% and 70% for predictions when spectral libraries were spiked. The increase in uncertainty was specific to the combination of soil property and sensor analysed. We conclude that there is always a trade-off between prediction accuracy and the costs associated with soil sampling, sample processing and wet chemical analysis. Therefore the relative merits of each approach will depend on the specific case in question.

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“…The latter qualification is relevant given the importance of sample pre-treatment for the accuracy and reproducibility of VNIR and MIR measurements and derived SOC models [ 30 , 31 , 32 , 33 , 34 , 35 , 36 ]. Fine-grinding generally increases predictive accuracies and is especially relevant for MIR spectroscopy, where the amount of scanned soil material is lower and thus likely less representative without careful homogenization due to smaller beam apertures and shallower penetration depths for MIR instrumentation [ 2 ].…”

Section: Discussionmentioning

confidence: 99%

Accuracy and Reproducibility of Laboratory Diffuse Reflectance Measurements with Portable VNIR and MIR Spectrometers for Predictive Soil Organic Carbon Modeling

Semella

Hutengs

Seidel

et al. 2022

Sensors

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Soil spectroscopy in the visible-to-near infrared (VNIR) and mid-infrared (MIR) is a cost-effective method to determine the soil organic carbon content (SOC) based on predictive spectral models calibrated to analytical-determined SOC reference data. The degree to which uncertainty in reference data and spectral measurements contributes to the estimated accuracy of VNIR and MIR predictions, however, is rarely addressed and remains unclear, in particular for current handheld MIR spectrometers. We thus evaluated the reproducibility of both the spectral reflectance measurements with portable VNIR and MIR spectrometers and the analytical dry combustion SOC reference method, with the aim to assess how varying spectral inputs and reference values impact the calibration and validation of predictive VNIR and MIR models. Soil reflectance spectra and SOC were measured in triplicate, the latter by different laboratories, for a set of 75 finely ground soil samples covering a wide range of parent materials and SOC contents. Predictive partial least-squares regression (PLSR) models were evaluated in a repeated, nested cross-validation approach with systematically varied spectral inputs and reference data, respectively. We found that SOC predictions from both VNIR and MIR spectra were equally highly reproducible on average and similar to the dry combustion method, but MIR spectra were more robust to calibration sample variation. The contributions of spectral variation (ΔRMSE < 0.4 g·kg−1) and reference SOC uncertainty (ΔRMSE < 0.3 g·kg−1) to spectral modeling errors were small compared to the difference between the VNIR and MIR spectral ranges (ΔRMSE ~1.4 g·kg−1 in favor of MIR). For reference SOC, uncertainty was limited to the case of biased reference data appearing in either the calibration or validation. Given better predictive accuracy, comparable spectral reproducibility and greater robustness against calibration sample selection, the portable MIR spectrometer was considered overall superior to the VNIR instrument for SOC analysis. Our results further indicate that random errors in SOC reference values are effectively compensated for during model calibration, while biased SOC calibration data propagates errors into model predictions. Reference data uncertainty is thus more likely to negatively impact the estimated validation accuracy in soil spectroscopy studies where archived data, e.g., from soil spectral libraries, are used for model building, but it should be negligible otherwise.

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Fine grinding is needed to maintain the high accuracy of mid‐infrared diffuse reflectance spectroscopy for soil property estimation

Cited by 18 publications

References 45 publications

Implementing the Soil Enrichment Protocol at Scale: Opportunities for an Agricultural Carbon Market

Implementing the Soil Enrichment Protocol at Scale: Opportunities for an Agricultural Carbon Market

Comparing the effect of different sample conditions and spectral libraries on the prediction accuracy of soil properties from near- and mid-infrared spectra at the field-scale

Accuracy and Reproducibility of Laboratory Diffuse Reflectance Measurements with Portable VNIR and MIR Spectrometers for Predictive Soil Organic Carbon Modeling

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