Special issue ‘Diffuse reflectance spectroscopy in soil science and land resource assessment’

Guerrero, C.; Rossel, Raphael A. Viscarra; Mouazen, Abdul Mounem

doi:10.1016/j.geoderma.2010.05.008

Cited by 28 publications

(11 citation statements)

References 14 publications

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“…21 Over the last 20 years, application of visible and near infrared (vis-NIR) diffuse reflectance spectroscopy in soil science has attracted great attention. 22,23 Diffuse reflectance spectroscopy (DRS) is non-destructive and inexpensive, and information can be easily collected from small quantities of sample. Furthermore, DRS can be performed in situ, providing information under field conditions and within a short time.…”

Section: Introductionmentioning

confidence: 99%

Using Visible and near Infrared Spectroscopy to Estimate Carbonates and Gypsum in Soils in Arid and Subhumid Regions of Isfahan, Iran

Khayamim

Wetterlind

Khademi

et al. 2015

Journal of Near Infrared Spectroscopy

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Soils in arid and semi-arid regions are strongly affected by the accumulation of carbonates, gypsum and other, more soluble, salts.Carbonates and gypsum both have a considerable influence on soil properties, especially the chemical properties of the soil solution. The development of reliable, fast and inexpensive methods to quantify the amounts of carbonates and gypsum in soil is therefore important. Visible and near infrared (vis-NIR) spectroscopy is a non-destructive, rapid and cheap method for measuring several soil properties simultaneously. However, research on vis-NIR spectroscopy in quantifying carbonates and gypsum is limited. Therefore, this study evaluated the efficiency of vis-NIR spectroscopy in quantifying carbonates and gypsum in surface soils using partial least-squares regression (PLSR) compared with standard laboratory methods and compared PLSR with a feature-specific method using continuum removal (CR). Carbonates and gypsum in a total of 251 sieved and air-dried topsoil samples from Isfahan Province in central Iran were measured by standard laboratory methods and vis-NIR spectroscopy (350-2500 nm wavelength range). In parallel, PLSR and the feature-specific method based on CR spectra were used to predict carbonates and gypsum. The PLSR model efficiency (E) for carbonates and gypsum in the validation set was 0.52 and 0.80, respectively. The PLSR model resulted in better predictions than the feature-specific method for both soil properties. Because of the unique absorption features of gypsum, which did not overlap with other soil properties, predictions of gypsum resulted in higher E values and lower errors than predictions of carbonates.

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Section: Introductionmentioning

confidence: 99%

Using Visible and near Infrared Spectroscopy to Estimate Carbonates and Gypsum in Soils in Arid and Subhumid Regions of Isfahan, Iran

Khayamim

Wetterlind

Khademi

et al. 2015

Journal of Near Infrared Spectroscopy

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“…Over time, the prediction accuracy of such models has improved significantly, making the relative contribution of measurement error in the calibration data larger. For example, visible (Vis), near-infrared (NIR) and midinfrared (MIR) diffuse reflectance spectroscopy models have improved rapidly due to advances in computation, instrument manufacturing and multivariate statistics (Dangal et al, 2019;Guerrero, Viscarra Rossel, & Mouazen, 2010). Furthermore, developments in computation and statistics helped to extract useful information from the measured spectra (Viscarra Rossel et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Statistical modelling of measurement error in wet chemistry soil data

Leeuwen¹,

Mulder²,

Batjes³

et al. 2021

Preprint

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There is a growing demand for high quality soil data to model soil processes and map soil properties. However, wet chemistry measurements on soil properties are subjected to many error sources, such as the observer, the instrument and lack of standardised methodologies. Consequently, soil data are imperfect and uncertain because of these error sources. Uncertainties in measurements of fundamental soil properties can propagate through, e.g., pedotransfer functions, spectroscopic models and digital soil mapping algorithms. Therefore, it is important to provide detailed uncertainty information about soil measurements to potential data users. In practice, uncertainty estimates are rarely specified by providers of analytical soil data.In this research, we aimed to quantify uncertainties in synthetic and real-world pH (1:1 soil-water suspension) and Total Organic Carbon (TOC) measurements. We assumed that uncertainty can be represented by a normal distribution. A linear mixed-effects model was applied to estimate the parameters of the normal distribution, i.e., mean and standard deviation, of both synthetic and real-world datasets. The model included &#8216;sample ID&#8217; as a fixed effect, and &#8216;batch&#8217; and &#8216;laboratory&#8217; as random effects. The use of synthetic datasets allowed us to investigate how well the model parameters could be estimated given a specific experimental measurement design, whereas the real-world case served to explore if the parameter estimates were still accurate for such unbalanced datasets.For a balanced dataset (n=20, n=100, n=200 and n=500), using synthetic pH data for three hypothetical laboratories (two batches per laboratory), the mean estimated standard deviations (&#963;) of the random effects were &#963;batch=0.10, &#963;laboratory=0.24 and &#963;residual=0.2. These estimates were in agreement with the &#963; for the respective random effects used to generate the synthetic dataset, meaning that the model could accurately estimate the model parameters. Subsequently, changes were made to the experimental measurement design by randomly removing 20%, 50% and 80% of the data, resulting in unbalanced datasets. In general, the interquartile range (IQR) of &#963; for each random effect increased with a larger percentage of removed data. However, the increase in IQR was larger for n=20 compared to, e.g., n=200. When comparing 0% and 80% randomly removed data, the IQR for the batch effect increased with 60.3%. Conversely, for n=200 an increase of only 23.5% was observed.Subsequently, the same model was fitted on real-world pH and TOC data, provided by the Wageningen Evaluating Programs for Analytical Laboratories (WEPAL). The unbalanced dataset structure was first reconstructed and filled with synthetically generated data, based on sample means and standard deviations derived from the measured data. The model was fitted on both datasets. For measured pH, the model yielded &#963;batch=0.27, &#963;laboratory=0.17 and &#963;residual=0.10. The IQRs of the estimated &#963; from synthetic WEPAL data were 0.04 (batch), 0.06 (laboratory) and 0.02 (residual). The model fitted on the measured TOC data estimated &#963;batch=5.3%, &#963;laboratory=2.8% and &#963;residual=2.1%. For the synthetic WEPAL data, IQRs of 1.3% (batch), 1.4% (laboratory) and 0.4% (residual) were determined for the estimated &#963;. These findings suggest that despite having a highly unbalanced dataset, realistic model parameter estimates can still be obtained.

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“…there are authors who have studied analysis using nIr on a local scale. [21][22][23][24][25][26] nevertheless, currently, few studies exist on the application of nIr spectroscopy technology in the vertisols of Mediterranean areas to monitor the c content. these soils are characterised by their low content of organic matter.…”

mentioning

confidence: 99%

Soil Carbon Determination in a Mediterranean Vertisol by Visible and near Infrared Reflectance Spectroscopy

Fontán

López‐Bellido

García-Olmo

et al. 2011

Journal of Near Infrared Spectroscopy

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all rights reserved agricultural soils, having been depleted of much of their native c stocks, have a significant co 2 sink capacity. 1 for this reason, the quantification of c content in various types of soil, according to climate and soil management, is of great interest. Moreover, c sequestration in agricultural soils will also benefit productivity and sustainability, and trading of c credits or tax benefits could be a source of income for agricultural producers. 2 awarding these benefits would require the monitoring of soil organic c content. It has been estimated that a soil c monitoring system, including all uS farms, using existing analytical techniques, could cost more than $1 billion for the first four-year commitment period. 3 clearly, a reduction in per-sample cost of analysis

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Special issue ‘Diffuse reflectance spectroscopy in soil science and land resource assessment’

Cited by 28 publications

References 14 publications

Using Visible and near Infrared Spectroscopy to Estimate Carbonates and Gypsum in Soils in Arid and Subhumid Regions of Isfahan, Iran

Using Visible and near Infrared Spectroscopy to Estimate Carbonates and Gypsum in Soils in Arid and Subhumid Regions of Isfahan, Iran

Statistical modelling of measurement error in wet chemistry soil data

Soil Carbon Determination in a Mediterranean Vertisol by Visible and near Infrared Reflectance Spectroscopy

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