Maria Knadel

Comparing Predictive Abilities of Three Visible-Near Infrared Spectrophotometers for Soil Organic Carbon and Clay Determination:

Due to advances in optical technology, a wide range of spectrometers is available. Recent interests in soil global libraries and sensor fusion presents a challenge with respect to combining data from different instrumentation. Little research, however, has been done on the comparison of visible-near infrared (vis-NIR) spectrometers for soil characterisation. There is a need for more work on the effects of scanning strategies and use of different soil instrumentation. We compared three vis-NIR spectrometers with varying resolution, signal-to-noise ratios and spectral range. Their performance was evaluated based on spectra collected from 194 Danish top soils and used to determine soil organic carbon (SOC) and clay content. Scanning procedures for the three spectrophotometers where done according to uniform laboratory protocols. Soil organic carbon and clay calibrations were performed using PLS regression. One third of the data set was used as an independent test set. A range of spectral preprocessing methods was applied in search of model improvement. Validation for SOC content using an independent data set derived from all three spectrophotometers provided values of RMSEP between 0.45% and 0.52%, r2=0.42–0.59 and RPD = 1.2–1.4. Clay content was predicted with a higher precision resulting in RMSEP values between 2.6% and 2.9%, r2 = 0.71–0.77 and RPD values in the range from 2.2 to 2.5. No substantial differences in the prediction accuracy were found for the three spectrometers, although there was a tendency that, in the tradeoff between noise and resolution, low noise was the more important for SOC and clay predictions. The application of different spectral preprocessing procedures did not generate important improvements of the calibration models either. Additionally, data simulation analysis, including resampling to a coarser resolution and addition of noise, was performed. No, or very little, effect of sampling resolution and additional noise on the performance of the spectrophotometers was reported. The results from this study showed that, as long as strict laboratory scanning protocols were followed, no significant differences in constituent determination were found, despite differences in spectral range, spectral resolution, spectral sampling intervals and sample presentation methods. The differences in predictive abilities between the spectrometers were mostly due to differences in spectral range.

Modeling Soil Organic Carbon at Regional Scale by Combining Multi-Spectral Images with Laboratory Spectra

There is a great challenge in combining soil proximal spectra and remote sensing spectra to improve the accuracy of soil organic carbon (SOC) models. This is primarily because mixing of spectral data from different sources and technologies to improve soil models is still in its infancy. The first objective of this study was to integrate information of SOC derived from visible near-infrared reflectance (Vis-NIR) spectra in the laboratory with remote sensing (RS) images to improve predictions of topsoil SOC in the Skjern river catchment, Denmark. The second objective was to improve SOC prediction results by separately modeling uplands and wetlands. A total of 328 topsoil samples were collected and analyzed for SOC. Satellite Pour l’Observation de la Terre (SPOT5), Landsat Data Continuity Mission (Landsat 8) images, laboratory Vis-NIR and other ancillary environmental data including terrain parameters and soil maps were compiled to predict topsoil SOC using Cubist regression and Bayesian kriging. The results showed that the model developed from RS data, ancillary environmental data and laboratory spectral data yielded a lower root mean square error (RMSE) (2.8%) and higher R2 (0.59) than the model developed from only RS data and ancillary environmental data (RMSE: 3.6%, R2: 0.46). Plant-available water (PAW) was the most important predictor for all the models because of its close relationship with soil organic matter content. Moreover, vegetation indices, such as the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI), were very important predictors in SOC spatial models. Furthermore, the ‘upland model’ was able to more accurately predict SOC compared with the ‘upland & wetland model’. However, the separately calibrated ‘upland and wetland model’ did not improve the prediction accuracy for wetland sites, since it was not possible to adequately discriminate the vegetation in the RS summer images. We conclude that laboratory Vis-NIR spectroscopy adds critical information that significantly improves the prediction accuracy of SOC compared to using RS data alone. We recommend the incorporation of laboratory spectra with RS data and other environmental data to improve soil spatial modeling and digital soil mapping (DSM).

Predicting soil organic carbon at field scale using a national soil spectral library

Visible and near infrared diffuse reflectance (vis-NIR) spectroscopy is a low-cost, efficient and accurate soil analysis technique and is thus becoming increasingly popular. Soil spectral libraries are commonly constructed as the basis for estimating soil texture and properties. In this study, partial least squares regression was used to develop models to predict the soil organic carbon (SOC) content of 35 soil samples from one field using (i) the Danish soil spectral library (2688 samples), (ii) a spiked spectral library (a combination of 30 samples selected from the local area and the spectral library, 2718 samples) and (iii) three sub-sets selected from the spectral library. In an attempt to improve prediction accuracy, sub-sets of the soil spectral library were made using three different sample selection methods: those geographically closest (84 samples), those with the same landscape and parent material (96 samples) and those with the most alike spectra to spectra from the field investigation (100 samples). These sub-sets were used to develop three calibration models and in predictions of SOC content. The results showed that the geographically closest model, which used the fewest number of samples, gave the lowest root mean square error of prediction (RMSEP) of 0.19% and the highest ratio of performance to deviation (RPD) of 3.7, followed by the spiked library, same parent material, the spectral library and the most alike spectra. The spiked library model also gave a low RMSEP value of 0.19% and high RPD value of 3.7% and performed markedly better than the model without spiking, despite using 30 samples for library spiking. The accuracy of the model developed using a sub-set from a spectral library was highly dependent on geographical location, soil parent material and landscape.

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

Abstract A total of 125 soil samples were collected from a Danish field varying in soil texture from sandy to loamy. Visible near-infrared reflectance (Vis-NIR) and mid-infrared reflectance (MIR) spectroscopy combined with chemometric methods were used to predict soil organic carbon (SOC) and clay contents. The main objective of this study was to find the best model for predicting SOC and clay content in the sampled field using Vis-NIR, MIR, and the combination of Vis-NIR and MIR and using different model development techniques. The secondary objectives were (i) to use iterations of calculation to find the optimal number of replicates for MIR measurements based on the root mean square error of cross validation (RMSECV) and (ii) to apply partial least squares regression in combination with jack-knifing (JK) to identify the most important part of spectral variables and the best model for predicting SOC and clay content. The study showed that with repeated MIR measurements it was possible to improve RMSECV by 20%. The optimal number of repeated MIR measurements was between 3 and 4 for SOC and clay content. Comparing all the prediction results, the combination of MIR and Vis-NIR with the partial least squares regression–JK technique resulted in the lowest prediction errors (RMSECVsoc of 0.35% and RMSECVclay of 1.05%). The average uncertainties of laboratory measurements were 0.39% and 1.86% for SOC and clay contents, respectively. All models had acceptable and—to a large extent—comparable margins of error. Partial least squares regression with JK simplified and enhanced the interpretation of the developed models because of a reduction in the number of variables used in the models.

Soil organic carbon and particle sizes mapping using vis-NIR, EC and temperature mobile sensor platform

Mobile sensor platform was used to predict and map SOC and particle sizes.Vis-NIRS, EC and temperature sensory data was used individually and fused.Successful calibration models were obtained for all soil properties.Optimal combination of sensor data was field and property dependent.Detailed maps of soil properties were generated using sensor data. Soil organic carbon (SOC) is an important parameter in the climate change mitigation strategies and it is crucial for the function of ecosystems and agriculture. Particle size fractions affect strongly the physical and chemical properties of soil and thus also SOC. Conventional analyses of SOC and particle sizes are costly limiting the detailed characterization of soil spatial variability and fine resolution mapping. Mobile sensors provide an alternative approach to soil analysis. They offer densely spaced georeferenced data in a cost-effective manner. In this study, two agricultural fields (Voulund1 and Voulund2) in Denmark were mapped with the Veris mobile sensor platform (MSP). MSP collected simultaneously visible near infrared spectra (vis-NIR; 350-2200nm), electrical conductivity (EC: shallow; 0-30cm, deep; 0-90cm), and temperature measurements. Fuzzy k-means clustering was applied to the obtained spectra to partition the fields and to select representative samples for calibration purposes. Calibration samples were analyzed for SOC and particle sizes (clay, silt and sand) using conventional wet chemistry analysis. The objectives of this study were to determine whether it is the single sensors or the fusion of sensor data that provides the best predictive ability of the soil properties in question. Using partial least square regression (PLS) excellent calibration results were generated for all soil properties with a ratio of performance to deviation (RPD) values above 2. The best predictive ability for SOC was obtained using a fusion of sensor data. The calibration models based on vis-NIR spectra and temperature resulted in RMSECV=0.14% and R2=0.94 in Voulund1. In Voulund2, the combination of EC, temperature and spectral data generated a SOC model with RMSECV=0.17% and R2=0.93. The highest predictive ability for clay was obtained using spectral data only in Voulund1 (RMSECV=0.34% and R2=0.76). Whereas in Voulund2, improved results were obtained after combining spectral and temperature data RMSECV=0.20% and R2=0.92. The best predictions of silt and sand were obtained when using spectral data only and resulted in RMSECV=0.35%, R2=0.82 and RMSECV=0.85%, R2=0.81, respectively, in Voulund1 and RMSECV=0.31%, R2=0.86 and RMSECV=0.74%, R2=0.92, respectively, in Voulund2.The best models were used to predict soil properties from the field spectra collected by the MSP. Maps of predicted soil properties were generated using ordinary kriging. Results from this study indicate that robust calibration models can be developed on the basis of the MSP and that high resolution field maps of soil properties can be compiled in a cost-effective manner.

Assessing Soil Water Repellency of a Sandy Field with Visible near Infrared Spectroscopy

Soil water repellency (WR) is a widespread phenomenon caused by aggregated organic matter (OM) and layers of hydrophobic organic substances coating the surface of soil particles. These substances have a very low surface free energy, reducing a soils water attraction. There is focus on WR due to its effects on germination, root growth, liquid–vapour dynamics, surface erosion and leaching of chemicals through fingered flow paths. However, common techniques for measuring WR are time-consuming and expensive. Meanwhile, it is well established that visible near infrared (vis-NIR) spectroscopy is a reliable method for determining soil OM. Potentially it could therefore provide fast measurements of WR through autocorrelation with OM. The aim of this study was to test the feasibility of vis-NIR spectroscopy for estimating the WR of soils with a small gradient in soil organic carbon (SOC) and texture, and to evaluate the effect of soil pretreatment on the predictive ability of WR models. A total of 87 soil samples from an agricultural coarse sandy field in Denmark were analysed for SOC, particle size fractions, water content and WR. Soil samples were scanned with a vis-NIR sensor (350–2500 nm) after air- and oven-drying at 60°C and 105°C. WR, expressed as liquid surface tension (mN m−1), was determined using the molarity of ethanol droplet test. Partial least squares regression models of SOC, texture and water content showed no predictive ability (r2 values between 0.10 and 0.51). However, successful models (r2 = 0.85) were generated for WR. The majority of bands important in the vis-NIR region of WR models were related to different components of OM indicating that, across the investigated field, WR was related to specific hydrophobic components of soil OM rather than to the total amount of carbon. A lower prediction error of the WR model for soils dried at 105°C (1.93 mN m−1) than at 60°C (2.52 mN m−1) can be explained by a lower range of WR values for the soils dried at 105°C. Moreover, a higher temperature reduced the number of absorption bands related to OM, indicating a degradation of hydrocarbon groups and a more hydrophobic character of the soil.

Field-Scale Predictions of Soil Contaminant Sorption Using Visible–Near Infrared Spectroscopy

Models used to evaluate leaching of contaminants to groundwater are very sensitive to sorption coefficients (Kd). These models need reliable Kd data at the field scale, but the number of samples required makes the classic batch sorption experiments inappropriate for this purpose. Since visible–near infrared (vis–NIR) spectroscopy is an inexpensive and fast method, it has been used for predicting soil properties related to soil sorption capacity. In this study, we aimed to predict the spatial variation of Kd from vis–NIR spectra for two contaminants: phenanthrene (sorbed on organic fractions) and glyphosate (sorbed on mineral fractions). Forty-five bulk soil samples were collected from an agricultural field in Estrup, Denmark, in a 15 m × 15 m grid. Samples were air-dried, sieved to 2 mm and analysed for selected soil properties. Sorption coefficients were obtained from a batch equilibration experiment. Soil samples were measured with a bench-top spectrometer covering the vis–NIR range between 400 nm and 2500 nm. Partial least squares regression with full cross-validation was used to correlate the soil spectra with Kd values and soil properties. The sorption coefficients ranged from 345 L kg−1 to 886 L kg−1 and from 162 L kg−1 to 536 L kg−1 for phenanthrene and glyphosate, respectively. The regression coefficients showed that phenanthrene sorption was correlated with total organic carbon, aluminium oxides and cation exchange capacity, and glyphosate sorption with clay minerals and iron oxides. By means of the vis–NIR spectra we were able to predict phenanthrene (R2 = 0.95, RMSECV = 31 L kg−1) and glyphosate (R2 = 0.79, RMSECV = 45 L kg−1) sorption capacities. A model using vis–NIR spectra plus pH values improved the prediction of glyphosate sorption capacity (R2 = 0.88, RMSECV = 34 L kg−1). The models obtained from vis–NIR spectra successfully predicted Kd within the investigated field, indicating the potential of vis–NIR spectroscopy as a fast method for determining Kd for input to leaching risk assessment models. However, further studies of different soil types and geographical scales are needed to confirm our findings.

Using Vis-NIR Spectroscopy for Monitoring Temporal Changes in Soil Organic Carbon

Abstract Monitoring the spatial and temporal changes in soil organic carbon (SOC) brought about by climate change and agricultural practices is challenging because existing SOC monitoring methods are very time and resource consuming. This study examined the use of visible near-infrared spectroscopy (Vis-NIR) as a speedy method to predict SOC and to monitor spatial and temporal changes in SOC compared with labor-intensive traditional laboratory (TL) measurements. For SOC prediction, topsoil (0–25 cm) and subsoil (25–50 cm) samples in the Danish soil spectral library for the years 1986 and 2009 were used. Empirical Bayesian Kriging was used to map SOC. The Vis-NIR predictions indicated that average topsoil and subsoil SOC had decreased slightly in Denmark from 1986 to 2009, and this was confirmed by TL measurements of SOC. In East Denmark, Vis-NIR predictions differed significantly from the measured SOC values. For subsoil samples, the ability of Vis-NIR to predict SOC levels varied. In West Jutland, Central Jutland, North Jutland, and Thy, Vis-NIR–predicted SOC levels did not differ from TL-measured levels, showing good predictive ability. For topsoil samples, the spatial pattern of change in TL-measured and predicted SOC was consistent during the 23-year study period, but there were significant discrepancies in the corresponding SOC change patterns for subsoil samples. To conclude, Vis-NIR is a promising method for monitoring spatial and temporal changes in SOC at the national scale, especially in the topsoil. Some difficulties can arise in low SOC subsoils, so more systematic work is needed to improve the method for practical applications.

Digital Mapping of Toxic Metals in Qatari Soils Using Remote Sensing and Ancillary Data

After decades of mining and industrialization in Qatar, it is important to estimate their impact on soil pollution with toxic metals. The study utilized 300 topsoil (0–30 cm) samples, multi-spectral images (Landsat 8), spectral indices and environmental variables to model and map the spatial distribution of arsenic (As), chromium (Cr), nickel (Ni), copper (Cu), lead (Pb) and zinc (Zn) in Qatari soils. The prediction model used condition-based rules generated in the Cubist tool. In terms of R2 and the ratio of performance to interquartile distance (RPIQ), the models showed good predictive capabilities for all elements. Of all of the prediction results, Cu had the highest R2 = 0.74, followed by As > Pb > Cr > Zn > Ni. This study found that all of the models only chose images from January and February as predictors, which indicates that images from these two months are important for soil toxic metals’ monitoring in arid soils, due to the climate and the vegetation cover during this season. Topsoil maps of the six toxic metals were generated. The maps can be used to prioritize the choice of remediation measures and can be applied to other arid areas of similar environmental/socio-economic conditions and pollution causes.

Visible–Near-Infrared Spectroscopy can predict Mass Transport of Dissolved Chemicals through Intact Soil

The intensification of agricultural production to meet the growing demand for agricultural commodities is increasing the use of chemicals. The ability of soils to transport dissolved chemicals depends on both the soil’s texture and structure. Assessment of the transport of dissolved chemicals (solutes) through soils is performed using breakthrough curves (BTCs) where the application of a solute at one site and its appearance over time at another are recorded. Obtaining BTCs from laboratory studies is extremely expensive and time- and labour-consuming. Visible–near-infrared (vis–NIR) spectroscopy is well recognized for its measurement speed and for its low data acquisition cost and can be used for quantitative estimation of basic soil properties such as clay and organic matter. In this study, for the first time ever, vis–NIR spectroscopy was used to predict dissolved chemical breakthrough curves obtained from tritium transport experiments on a large variety of intact soil columns. Averaged across the field, BTCs were estimated with a high degree of accuracy. So, with vis-NIR spectroscopy, the mass transport of dissolved chemicals can be measured, paving the way for next-generation measurements and monitoring of dissolved chemical transport by spectroscopy.