Radim Vašát

Visible, Near-Infrared, and Mid-Infrared Spectroscopy Applications for Soil Assessment with Emphasis on Soil Organic Matter Content and Quality: State-of-the-Art and Key Issues

Visible near-infrared (Vis-NIR) reflection spectroscopy and mid-infrared (mid-IR) reflection spectroscopy are cost- and time-effective and environmentally friendly techniques that could be alternatives to conventional soil analysis methods. Successful determination of spectrally active soil components, including soil organic matter (SOM), depends on the selection of suitable pretreatment and multivariate calibration techniques. The objective of the present review is to critically examine the suitability of Vis-NIR (350–2500 nm) and mid-IR (4000–400 cm−1) spectroscopy as a tool for SOM quantity and quality determination. Particular attention is paid to different pretreatment and calibration procedures and methods, and their ability to predict SOM content from Vis-NIR and mid-IR data is discussed. We then review the most recent research using spectroscopy in different calibration scales (local, regional, or global). Finally, accuracy and robustness, as well as uncertainty in Vis-NIR and mid-IR spectroscopy, are considered. We conclude that spectroscopy, especially the mid-IR technique in association with Savitzky–Golay smoothing and derivatization and the least squares support vector machine (LS-SVM) algorithm, can be useful in determining SOM quantity and quality. Future research conducted for the standardization of protocols and soil conditions will allow more accurate and reliable results on a global and international scale.

Estimation of Potentially Toxic Elements Contamination in Anthropogenic Soils on a Brown Coal Mining Dumpsite by Reflectance Spectroscopy: A Case Study

In order to monitor Potentially Toxic Elements (PTEs) in anthropogenic soils on brown coal mining dumpsites, a large number of samples and cumbersome, time-consuming laboratory measurements are required. Due to its rapidity, convenience and accuracy, reflectance spectroscopy within the Visible-Near Infrared (Vis-NIR) region has been used to predict soil constituents. This study evaluated the suitability of Vis-NIR (350–2500 nm) reflectance spectroscopy for predicting PTEs concentration, using samples collected on large brown coal mining dumpsites in the Czech Republic. Partial Least Square Regression (PLSR) and Support Vector Machine Regression (SVMR) with cross-validation were used to relate PTEs data to the reflectance spectral data by applying different preprocessing strategies. According to the criteria of minimal Root Mean Square Error of Prediction of Cross Validation (RMSEPcv) and maximal coefficient of determination (R2 cv) and Residual Prediction Deviation (RPD), the SVMR models with the first derivative pretreatment provided the most accurate prediction for As (R2 cv) = 0.89, RMSEPcv = 1.89, RPD = 2.63). Less accurate, but acceptable prediction for screening purposes for Cd and Cu (0.66 ˂ R2 cv) ˂ 0.81, RMSEPcv = 0.0.8 and 4.08 respectively, 2.0 ˂ RPD ˂ 2.5) were obtained. The PLSR model for predicting Mn (R2 cv) = 0.44, RMSEPcv = 116.43, RPD = 1.45) presented an inadequate model. Overall, SVMR models for the Vis-NIR spectra could be used indirectly for an accurate assessment of PTEs’ concentrations.

Comparing Different Data Preprocessing Methods for Monitoring Soil Heavy Metals Based on Soil Spectral Features

Gholizadeh A., Borůvka L., Saberioon M.M., Kozak J., Vasat R., Němecek K. (2015): Comparing different data pre processing methods for monitoring soil heavy metals based on soil spectral features. Soil & Water Res., 10: 218–227. The lands near mining industries in the Czech Republic are subjected to soil pollution with heavy metals. Ex cessive heavy metal concentrations in soils not only dramatically impact the soil quality, but also due to their persistent nature and indefinite biological half-lives, potentially toxic metals can accumulate in the food chain and can eventually endanger human health. Monitoring and spatial information of these elements require a large number of samples and cumbersome and time-consuming laboratory measurements. A faster method has been developed based on a multivariate calibration procedure using support vector machine regression (SVMR) with cross-validation, to establish a relationship between reflectance spectra in the visible-near infrared (Vis-NIR) region and concentration of Mn, Cu, Cd, Zn, and Pb in soil . Spectral preprocessing methods, first and second derivatives (FD and SD), standard normal variate (SNV), multiplicative scatter correction (MSC), and continuum removal (CR) were employed after smoothing with Savitzky-Golay to improve the robustness and performance of the calibration models. According to the criteria of maximal coefficient of determination ( R 2 cv ) and minimal root mean square error of prediction in cross-validation ( RMSEP cv ), the SVMR algorithm with FD preprocessing was determined as the best method for predicting Cu, Mn, Pb, and Zn concentration, whereas the SVMR model with CR preprocessing was chosen as the final method for predicting Cd. Overall, this study indicated that the Vis-NIR reflectance spectroscopy technique combined with a continuously enriched soil spectral library as well as a suitable preprocessing method could be a nondestructive alternative for monitoring of the soil environment. The future possibilities of multivariate calibration and preprocessing with real-time remote sensing data have to be explored.

A Memory-Based Learning Approach as Compared to Other Data Mining Algorithms for the Prediction of Soil Texture Using Diffuse Reflectance Spectra

Successful determination of soil texture using reflectance spectroscopy across Visible and Near-Infrared (VNIR, 400–1200 nm) and Short-Wave-Infrared (SWIR, 1200–2500 nm) ranges depends largely on the selection of a suitable data mining algorithm. The objective of this research was to explore whether the new Memory-Based Learning (MBL) method performs better than the other methods, namely: Partial Least Squares Regression (PLSR), Support Vector Machine Regression (SVMR) and Boosted Regression Trees (BRT). For this purpose, we chose soil texture (contents of clay, silt and sand) as testing attributes. A selected set of soil samples, classified as Technosols, were collected from brown coal mining dumpsites in the Czech Republic (a total of 264 samples). Spectral readings were taken in the laboratory with a fiber optic ASD FieldSpec III Pro FR spectroradiometer. Leave-one-out cross-validation was used to optimize and validate the models. Comparisons were made in terms of the coefficient of determination (R2cv) and the Root Mean Square Error of Prediction of Cross-Validation (RMSEPcv). Predictions of the three soil properties by MBL outperformed the accuracy of the remaining algorithms. We found that the MBL performs better than the other three methods by about 10% (largest R2cv and smallest RMSEPcv), followed by the SVMR. It should be pointed out that the other methods (PLSR and BRT) still provided reliable results. The study concluded that in this examined dataset, reflectance spectroscopy combined with the MBL algorithm is rapid and accurate, offers major efficiency and cost-saving possibilities in other datasets and can lead to better targeting of management interventions.

Predicting oxidizable carbon content via visible- and near-infrared diffuse reflectance spectroscopy in soils heavily affected by water erosion.

Soil spectroscopy represents a low-cost alternative to routine time-consuming and expensive laboratory analyses. Its ability to measure a wide range of different chemical and physical soil properties was shown previously in many studies. Particularly, for organic carbon content, a reliable prediction accuracy is usually achieved. This is due to strong spectral signature of soil organic carbon and other distinct spectral implications of soil characteristics strongly tied to it, e.g. soil colour. All the known studies, however, deal with situation where the study area is fully covered (either in the manner of design- or model-based sampling approach) with calibration points. But in many cases the sampling strategy was initially designed for other purposes, falling outside requirements of spectroscopy for proper model calibration. Hence, here we attempt to test the ability of soil spectroscopy in the situation when only a minor isolated part (the steepest one) of the study area was sampled for calibration points, and predictions were made for its several time larger surroundings. For model training we used Partial Least Squares Regression (PLSR) technique and four different spectra pre-treatment methods (Savitzky-Golay smoothing, first and second derivative, and baseline normalization via continuum removal). Results show high potential (R 2 ≈ 0.70-0.80) of the method for rough terrain landscapes strongly affected by water erosion, even if the distance from calibration to prediction points is large.

Absorption Features in Soil Spectra Assessment.

From a wide range of techniques appropriate to relate spectra measurements with soil properties, partial least squares (PLS) regression and support vector machines (SVM) are most commonly used. This is due to their predictive power and the availability of software tools. Both represent exclusively statistically based approaches and, as such, benefit from multiple responses of soil material in the spectrum. However, physical-based approaches that focus only on a single spectral feature, such as simple linear regression using selected continuum-removed spectra values as a predictor variable, often provide accurate estimates. Furthermore, if this approach extends to multiple cases by taking into account three basic absorption feature parameters (area, width, and depth) of all occurring features as predictors and subjecting them to best subset selection, one can achieve even higher prediction accuracy compared with PLS regression. Here, we attempt to further extend this approach by adding two additional absorption feature parameters (left and right side area), as they can be important diagnostic markers, too. As a result, we achieved higher prediction accuracy compared with PLS regression and SVM for exchangeable soil pH, slightly higher or comparable for dithionite-citrate and ammonium oxalate extractable Fe and Mn forms, but slightly worse for oxidizable carbon content. Therefore, we suggest incorporating the multiple linear regression approach based on absorption feature parameters into existing working practices.

Modelling the Impact of Acid Deposition on Forest Soils in North Bohemian Mountains with Two Dynamic Models: the Very Simple Dynamic Model (VSD) and the Model of Acidification of Groundwater in Catchments (MAGIC)

Vasat R., Pavlů L., Borůvka L., Tejnecký V., Nikodem A. (2015): Modelling the impact of acid deposition on forest soils in North Bohemian Mountains with two dynamic models: the Very Simple Dynamic Model (VSD) and the Model of Acidification of Groundwater in Catchments (MAGIC). Soil & Water Res., 10: 10–18. Enormous acid deposition that culminated in the 1970s contributed largely to accelerate the process of acidifica tion of soils in northern Bohemia. As a consequence a wide forest decline occurred shortly afterwards. In this paper we present a long-term soil acidification modelling with two dynamic models (Model of Acidification of Groundwater in Catchments and Very Simple Dynamic Model) to describe history, make successive prediction, and assess possibility of recovery of the ecosystem. Focused on eight soil acidification indicators we found a strong rise of the soil acidification status in 1970s, when emission load culminated, and a large decrease after the year 2000 (after flue gas desulfurization). We further revealed slight differences, but general similarity, for both dynamic models. The results indicate that the impact of historic massive pollution will not probably be eliminated in the future by the year 2100.

Delineating Acidified Soils in the Jizera Mountains Region Using Fuzzy Classification

Soil acidification represents a serious problem in mountainous areas of the Czech Republic. It is mainly caused by acid parent materials, high precipitation, the type of vegetation, and acid deposition. These factors act in different combinations and result in different soil conditions. The aim of this chapter is to distinguish areas in the Jizera Mountains with different levels of soil acidification and sensitivity using fuzzy classification. A set of 98 sampling sites was analysed and sampling density was approximately one site per 2 km2. Samples were collected from surface organic horizons (O), depth ranged from 4 to 22 cm depending on site conditions. Soil analysis included active and exchangeable soil pH, total content of C, N, and S, pseudototal content of Ca and Mg (after aqua regia digestion), and the ratio of absorbances of soil sodium pyrophosphate extract at the wavelengths of 400 and 600 nm as indicator of humus quality A400/A600). Moreover, concentrations of exchangeable Al in KCl extract and organically bound Al in Na4P2O7 extract were determined. Soil classes were calculated using fuzzy k-means method with extragrades. Five classes were selected. The first class with high exchangeable Al content, high S and N, and low Ca, represents the area that was most affected by the acid deposition. The second class with the lowest pH represents strongly acid soils that have very high sensitivity to acidification, but with smaller acid deposition. The third class with high Ca content includes the areas that were limed in the past. The fourth class includes principally the sites with the highest S and N deposition that are populated by grass. The fifth class includes the areas with high Mg content; its distribution corresponds to beech forests that have more favourable effects on soils than spruce forests. Fuzzy classification distinguished soils with strongest sensitivity to acidification. Positive effect of beech forest, grass cover, and liming on surface organic soil horizons is shown.