Bo Stenberg

Visible and Near Infrared Spectroscopy in Soil Science

Abstract This chapter provides a review on the state of soil visible–near infrared (vis–NIR) spectroscopy. Our intention is for the review to serve as a source of up-to-date information on the past and current role of vis–NIR spectroscopy in soil science. It should also provide critical discussion on issues surrounding the use of vis–NIR for soil analysis and on future directions. To this end, we describe the fundamentals of visible and infrared diffuse reflectance spectroscopy and spectroscopic multivariate calibrations. A review of the past and current role of vis–NIR spectroscopy in soil analysis is provided, focusing on important soil attributes such as soil organic matter (SOM), minerals, texture, nutrients, water, pH, and heavy metals. We then discuss the performance and generalization capacity of vis–NIR calibrations, with particular attention on sample pretratments, covariations in data sets, and mathematical data preprocessing. Field analyses and strategies for the practical use of vis–NIR are considered. We conclude that the technique is useful to measure soil water and mineral composition and to derive robust calibrations for SOM and clay content. Many studies show that we also can predict properties such as pH and nutrients, although their robustness may be questioned. For future work we recommend that research should focus on: (i) moving forward with more theoretical calibrations, (ii) better understanding of the complexity of soil and the physical basis for soil reflection, and (iii) applications and the use of spectra for soil mapping and monitoring, and for making inferences about soils quality, fertility and function. To do this, research in soil spectroscopy needs to be more collaborative and strategic. The development of the Global Soil Spectral Library might be a step in the right direction.

The use of near infrared (NIR) spectroscopy to improve soil mapping at the farm scale

The creation of fine resolution soil maps is hampered by the increasing costs associated with conventional laboratory analyses of soil. In this study, near infrared (NIR) reflectance spectroscopy was used to reduce the number of conventional soil analyses required by the use of calibration models at the farm scale. Soil electrical conductivity and mid infrared reflection (MIR) from a satellite image were used and compared as ancillary data to guide the targeting of soil sampling. About 150 targeted samples were taken over a 97 hectare farm (approximately 1.5 samples per hectare) for each type of ancillary data. A sub-set of 25 samples was selected from each of the targeted data sets (150 points) to measure clay and soil organic matter (SOM) contents for calibration with NIR. For the remaining 125 samples only their NIR-spectra needed to be determined. The NIR calibration models for both SOM and clay contents resulted in predictions with small errors. Maps derived from the calibrated data were compared with a map based on 0.5 samples per hectare representing a conventional farm-scale soil map. The maps derived from the NIR-calibrated data are promising, and the potential for developing a cost-effective strategy to map soil from NIR-calibrated data at the farm-scale is considerable.

Diffuse Reflectance Spectroscopy for High-Resolution Soil Sensing

Diffuse reflectance spectroscopy in the visible–near-infrared (vis–NIR) and mid-infrared (mid-IR) is a practical analytical technique that can be used for both laboratory and in situ soil analysis. The techniques are sensitive to both organic and mineral soil composition. They are particularly well suited to situations where the primary (conventional) analytical method is laborious and costly or where a large number of analyses and samples are required, e.g. for high-resolution digital soil mapping or precision agriculture. This chapter will describe diffuse reflectance spectroscopy of soil in the vis–NIR (400–700–2,500 nm) and mid-IR (2,500–25,000 nm) portions of the electromagnetic spectrum. The theory of the mechanisms of absorbance in soil will be explained briefly, followed by aspects of data pretreatments, chemometrics, and multivariate calibrations. Finally, both laboratory and in situ applications are discussed and the focus of future research suggested.

Near infrared reflectance spectroscopy for quantification of crop residue, green manure and catch crop C and N fractions governing decomposition dynamics in soil

For environmental, as well as agronomic reasons, the turnover of carbon (C) and nitrogen (N) from crop residues, catch crops and green manures incorporated into agricultural soils has attracted much attention. It has previously been found that the C and N content in fractions from stepwise chemical digestion of plant materials constitutes an adequate basis for describing a priori the degradability of both C and N in soil. However, the analyses involved are costly and, therefore, unlikely to be used routinely. The aim of the present work was to develop near infrared (NIR) calibrations for C and N fractions governing decomposition dynamics. Within the five Nordic countries, we sampled a uniquely broad-ranged collection representing most of the fresh and mature plant materials that may be incorporated into agricultural soils from temperate regions. The specific objectives of the current study were (1) to produce NIR calibrations with data on C and N in fractions obtained by stepwise chemical digestion (SCD); (2) to validate these calibrations on independent plant samples and (3) to compare the precision and robustness of these broad-based calibrations with calibrations derived from materials within a narrower quality range. According to an internal validation set, plant N, soluble N, cellulose C, holocellulose (hemicellulose + cellulose) C, soluble C and neutral detergent fibre (NDF) dry matter were the parameters best predicted (r2 = 0.97, 0.95, 0.94, 0.91, 0.90 and 0.94, respectively). However, the calibrations for soluble C and NDF were regarded as unstable, as their validation statistics were substantially poorer than the calibration statistics. The calibrations for all structural N fractions and lignin C were considered poor (r2 = 0.47–0.70). By comparing our broad-based calibrations for plant N and NDF with similar calibrations for a sample set representing a commercial forage database, it was evident that the broad-based calibrations predicted a narrow-based sample set better than vice versa. For plant N, the residual mean squared error of prediction (RMSEP), when testing the broad-based calibration with the narrow-based validation set, was substantially smaller than the RMSEP obtained when validating the broad-based calibration internally (1.8 vs 2.7 mg Ng−1 dry matter). Overall, the calibrations that performed best were those concerning the parameters most strongly influencing C and N mineralisation from plant materials.

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.

Soil analysis using visible and near infrared spectroscopy.

Visible-near infrared diffuse reflectance (vis-NIR) spectroscopy is a fast, nondestructive technique well suited for analyses of some of the essential constituents of the soil. These constituents, mainly clay minerals, organic matter and soil water strongly affect conditions for plant growth and influence plant nutrition. Here we describe the process by which vis-NIR spectroscopy can be used to collect soil spectra in the laboratory. Because it is an indirect technique, the succeeding model calibrations and validations that are necessary to obtain reliable predictions about the soil properties of interest are also described in the chapter.

Using visible and near infrared spectroscopy to estimate carbonates and gypsum in soils in arid and subhumid regions of Isfahan, Iran

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.