Asa Gholizadeh

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.

Agricultural Soil Spectral Response and Properties Assessment: Effects of Measurement Protocol and Data Mining Technique

Soil spectroscopy has shown to be a fast, cost-effective, environmentally friendly, non-destructive, reproducible and repeatable analytical technique. Soil components, as well as types of instruments, protocols, sampling methods, sample preparation, spectral acquisition techniques and analytical algorithms have a combined influence on the final performance. Therefore, it is important to characterize these differences and to introduce an effective approach in order to minimize the technical factors that alter reflectance spectra and consequent prediction. To quantify this alteration, a joint project between Czech University of Life Sciences Prague (CULS) and Tel-Aviv University (TAU) was conducted to estimate Cox, pH-H2O, pH-KCl and selected forms of Fe and Mn. Two different soil spectral measurement protocols and two data mining techniques were used to examine seventy-eight soil samples from five agricultural areas in different parts of the Czech Republic. Spectral measurements at both laboratories were made using different ASD spectroradiometers. The CULS protocol was based on employing a contact probe (CP) spectral measurement scheme, while the TAU protocol was carried out using a CP measurement method, accompanied with the internal soil standard (ISS) procedure. Two spectral datasets, acquired from different protocols, were both analyzed using partial least square regression (PLSR) technique as well as the PARACUDA II®, a new data mining engine for optimizing PLSR models. The results showed that spectra based on the CULS setup (non-ISS) demonstrated significantly higher albedo intensity and reflectance values relative to the TAU setup with ISS. However, the majority of statistics using the TAU protocol was not noticeably better than the CULS spectra. The paper also highlighted that under both measurement protocols, the PARACUDA II® engine proved to be a powerful tool for providing better results than PLSR. Such initiative is not only a way to unlock current limitations of soil spectroscopy, but also offers considerable efficiency and cost- and time-saving possibilities, which lead to further improvements in prediction performance of spectral models.

Paddy soil nutrient assessment using visible and near infrared reflectance spectroscopy

The ability of obtaining soil properties estimations from time and cost efficient remotely sensed techniques has been identified as a valuable technique as there is a great demand for larger amounts of good quality and inexpensive soil data to be used in environmental monitoring, modelling and precision agriculture. Visible (Vis) and Near Infrared (NIR) spectroscopy provides a good alternative that may be used to enhance or replace conventional methods of soil analysis. The aim of this paper is to evaluate the abilities of Vis (350-700 nm) and near infrared (700-2500 nm) for prediction of soil nutrients. In this instance we implemented Savitzky-Golay algorithm and Stepwise Multiple Linear Regression (SMLR) to construct calibration models. The soil nutrients examined were soil Total Nitrogen (N), Available Phosphorus (P) and Exchangeable Potassium (K). Our results revealed the accuracy of SMLR prediction in each of the Vis and NIR spectral regions. The NIR produced more accurate predictions for N and K; however, higher significant correlation was obtained using the Vis for available P. This work demonstrated Vis and NIR spectroscopy could be considered as a good tool to assess soil nutrients in Malaysian paddy fields.

Relationship between apparent electrical conductivity and soil physical properties in a Malaysian paddy field

Site-specific crop management, well-established in some developed countries, is now being considered in developing countries such as Malaysia. The apparent electrical conductivity (ECa) of the soil can be used as an indirect indicator of a number of soil physical properties and even crop yield. Commercially available ECa sensors can efficiently develop the spatially dense data sets desirable in describing within-field spatial soil variability for precision farming. The main purpose of this study was to generate a variability map of soil ECa within a Malaysian paddy field using a VerisEC sensor. The ECa values were then compared with some soil variables within classes after delineation. Measured parameters were mapped using the kriging technique and their correlation with soil ECa was determined. The study showed that the VerisEC can determine soil spatial variability, and can acquire soil ECa information quickly. Spatial variability of shallow and deep ECa showed the same patterns. Estimation of soil properties based on ECa varied from one soil parameter to another and all could be estimated better by deep ECa. Cross-validation results showed that shallow and deep ECa, and also bulk density, gave more accurate estimates compared with other variables.

Apparent Electrical Conductivity in Correspondence to Soil Chemical Properties and Plant Nutrients in Soil

Spatial variability and relationship between soil apparent electrical conductivity (ECa), soil chemical properties, and plant nutrients in soil have not been well documented in Malaysian paddy fields. For this reason precision farming has been used for assessing field conditions. ECa technique for describing soil spatial variability is used for soil data acquisition. Soil sampling provides the data used to make maps of the spatial patterns in soil properties. Maps are then used to make recommendations on the variation of application rates. The main purpose of the authors in this study was to generate variability map of soil ECa within a Malaysian rice cultivation area using VerisEC sensor. The ECa values were compared to some soil properties after delineation. Measured parameters were mapped using kriging technique and their correlation with soil ECa was determined. Through this study the authors showed that the EC sensor can determine soil spatial variability, where it can acquire the soil information quickly.

Monitoring of selected soil contaminants using proximal and remote sensing techniques: Background, state-of-the-art and future perspectives

ABSTRACT Soil degradation includes a number of processes, ranging from soil erosion to soil contamination, which reduce the capability of soil to work as a base for vegetation roots. Methods to quantify soil degradation due to contamination on a large area with a proper domain are needed and must be studied and developed. Proximal and remote sensing techniques are essential tools, well-suited for surveying large areas, and monitoring soil contamination at a high temporal and spatial interval. Recently developed and forthcoming satellites also dedicated to land monitoring and provide inimitable data streams, which have potential of soil contamination detection. This study peruses the potential of spectroscopy methods in various domains to assess selected soil contaminants including potentially toxic elements and petroleum hydrocarbons from reflectance information, plus a preliminary review of the new-generation orbital Earth observation sensors. An aim is to review the means to do so from spaceborne sensors, which are considered to be state-of-the-art Earth orbit observation technologies. This review will help to answer the question: how can spectral information from proximal and remote sensing techniques in different domains be used for soil contamination modelling? This direction will pave the way for soil contamination monitoring using these techniques.

Soil organic carbon content monitoring and mapping using airborne and Sentinel-2 spectral imaging

In this study, the performances of hyperspectral airborne and superspectral spaceborne spectral imaging to derive selected Soil Organic Carbon (SOC) were analyzed and compared in agricultural sites of the Czech Republic. The main aim was to assess the potential of superspectral Sentinel-2 satellite for the prediction and mapping of the attribute. The prediction accuracy based on airborne and spaceborne techniques in majority of the sites was adequate for SOC. Comparing the spatial distribution maps of SOC derived from the airborne and spaceborne data showed a similar trend at both platforms. The SOC maps also confirmed that in areas with a high level of SOC, Sentinel-2 was able to detect SOC even more precisely than the airborne sensors. Although a decrease in the model and map performances was obvious in the case of parameters with low contents. The findings of the current research showed that superspectral Sentinel-2 allows for the estimation and mapping of SOC. The study also emphasized the importance of the superspectral Sentinel-2 data in soil characteristics assessment with a frequent revisit-time over larger areas than it currently is with laboratory and airborne instruments. Certainly, the repeatability of the Sentinel-2 products is still a work in progress and with the Sentinel-2B, a revisit-time of five-day and the temporal frequency of cloud-free acquisitions will be further increased. Accordingly, much more data will be freely available in the near future, which will have a significant influence on the obtaining of high-quality soil data.