Johanna Wetterlind

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.

Estimation of Soil Carbon Input in France: An Inverse Modelling Approach

Development of a quantitative understanding of soil organic carbon (SOC) dynamics is vital for management of soil to sequester carbon (C) and maintain fertility, thereby contributing to food security and climate change mitigation. There are well-established process-based models that can be used to simulate SOC stock evolution; however, there are few plant residue C input values and those that exist represent a limited range of environments. This limitation in a fundamental model component (i.e., C input) constrains the reliability of current SOC stock simulations. This study aimed to estimate crop-specific and environment-specific plant-derived soil C input values for agricultural sites in France based on data from 700 sites selected from a recently established French soil monitoring network (the RMQS database). Measured SOC stock values from this large scale soil database were used to constrain an inverse RothC modelling approach to derive estimated C input values consistent with the stocks. This approach allowed us to estimate significant crop-specific C input values (P -1 year -1 (silage corn) to 5.15 ± 0.12 t C ha -1 year -1 (grassland/pasture). Furthermore, the incorporation of climate variables improved the predictions. C input of 4 crop types could be predicted as a function of temperature and 8 as a function of precipitation. This study offered an approach to meet the urgent need for crop-specific and environment-specific C input values in order to improve the reliability of SOC stock prediction.

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.

Performance Evaluation of Proximal Sensors for Soil Assessment in Smallholder Farms in Embu County, Kenya.

Four proximal soil sensors were tested at four smallholder farms in Embu County, Kenya: a portable X-ray fluorescence sensor (PXRF), a mobile phone application for soil color determination by photography, a dual-depth electromagnetic induction (EMI) sensor, and a LED-based soil optical reflectance sensor. Measurements were made at 32–43 locations at each site. Topsoil samples were analyzed for plant-available nutrients (N, P, K, Mg, Ca, S, B, Mn, Zn, Cu, and Fe), pH, total nitrogen (TN) and total carbon (TC), soil texture, cation exchange capacity (CEC), and exchangeable aluminum (Al). Multivariate prediction models of each of the lab-analyzed soil properties were parameterized for 576 sensor-variable combinations. Prediction models for K, N, Ca and S, B, Zn, Mn, Fe, TC, Al, and CEC met the setup criteria for functional, robust, and accurate models. The PXRF sensor was the sensor most often included in successful models. We concluded that the combination of a PXRF and a portable soil reflectance sensor is a promising combination of handheld soil sensors for the development of in situ soil assessments as a field-based alternative or complement to laboratory measurements.

Can within field yield variation be explained using horizontal penetrometer resistance and electrical conductivity measurements? Results from three Swedish fields

ABSTRACT Spatial variations in yield occur in all agricultural fields to some degree. Knowledge of the reasons for these variations, especially temporally stable yield patterns, is needed in order to improve productivity by changing production methods or inputs. This information must be provided in a simple, cost-effective way. This study investigated whether field-scale measurements of penetration resistance (PR) could identify temporally stable yield patterns without complementary measurements of other properties. The PR measurements were also compared with field-scale apparent electrical conductivity (ECa) measurements and correlations between yield and PR and yield and ECa were investigated. Measurements with a newly constructed horizontal penetrometer and a non-invasive proximal electromagnetic induction sensor were carried out in three fields in east-central Sweden. The measurements were made once in each field, apart from one field where PR was measured twice in one season. The sensor measurements were compared with crop yield measured over the course of several years. The horizontal penetrometer was able to identify low-yielding areas in one field. However, single measurements of PR or ECa were insufficient to consistently identify yield variations. Parts of the fields showed more consistent patterns, with stronger correlations between PR, ECa and yield. PR and ECa data were better at explaining yield variations in drier years, but showed diverging patterns in different fields and neither showed a much stronger correlation to yield than the other. In future studies, simultaneous measurement of soil water content is recommended and also PR measurements at a water content drier than field capacity.