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 pre-tratments, co-variations 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.

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The advantage of using near‐infrared spectroscopy to increase sample point density in soil mapping on farms relies on the number of conventional laboratory analyses for the calibrations being kept to a minimum. This study compared the performance of small farm‐scale calibrations (25 samples) with a larger national soil library (396 samples) and tested whether a site‐specific sample set selected from the national library, consisting of the 50 samples that were spectrally most similar to those of the local sites, could increase performance. In addition, the national library and selected subsets were augmented (‘spiked’) with up to 25 local calibration samples to test whether that had any additional effect on prediction errors and bias. Calibrations were made for predicting within‐field variation in clay, silt, sand, soil organic carbon (SOC), pH and phosphorus. Selecting a subset of samples from the national library did not improve the results compared with using the entire national library. However, spiking both libraries with local samples reduced the root mean squared error of prediction (RMSEP) considerably, mainly through a decrease in bias, and often resulted in comparable results to the local calibrations. There was a tendency for better clay and SOC predictions from spiking a reduced national library compared with spiking the entire national library, sometimes even resulting in better predictions than using the local calibrations. However, using local calibrations seems to be the best alternative for predicting soil properties at the farm or field scale, even with as few as 25 samples.

Summary Spiking is a useful approach to improve the accuracy of regional or national calibrations when they are used to predict at local scales. To do this, a small subset of local samples (spiking subset) is added to recalibrate the initial calibration. If the spiking subset is small in comparison with the size of the initial calibration set, then it could have little noticeable effect and a small improvement can be expected. For these reasons, we hypothesized that the accuracy of the spiked calibrations can be improved when the spiking subset is extra‐weighted. We also hypothesized that the spiking subset selection and the initial calibration size could affect the accuracy of the recalibrated models. To test these hypotheses, we evaluated different strategies to select the best spiking subset, with and without extra‐weighting, to spike three different‐sized initial calibrations. These calibrations were used to predict the soil organic carbon (SOC) content in samples from four target sites. Our results confirmed that spiking improved the prediction accuracy of the initial calibrations, with any differences depending on the spiking subset used. The best results were obtained when the spiking subset contained local samples evenly distributed in the spectral space, regardless of the initial calibration's characteristics. The accuracy was improved significantly when the spiking subset was extra‐weighted. For medium‐ and large‐sized initial calibrations, the improvement from extra‐weighting was larger than that caused by the increase in spiking subset size. Similar accuracies were obtained using small‐ and large‐sized calibrations, suggesting that incipient spectral libraries could be useful if the spiking subset is properly selected and extra‐weighted. When small‐sized spiking subsets were used, the predictions were more accurate than those obtained with ‘geographically‐local’ models. Overall, our results indicate that we can minimize the efforts needed to use near‐infrared (NIR) spectroscopy effectively for SOC assessment at local scales.

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 (MIR) reflection 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 costeffective strategy to map soil from NIR-calibrated data at the farm-scale is considerable.

Effective agricultural planning requires basic soil information. In recent decades visible near-infrared diffuse reflectance spectroscopy (vis-NIR) has been shown to be a viable alternative for rapidly analysing soil properties. We studied 7172 samples of seven different soil types collected from several regions of Brazil and varying in organic matter (OM) (0.2-10.3%) and clay content (0.2-99.0%). The aim was to explore the possibility of enhancing the performance of vis-NIR data in predicting organic matter and clay content in this library by dividing it into smaller sub-libraries on the basis of their vis-NIR spectra. We used partial least square regression (PLSR) models on the sub-libraries and compared the results with PLSR and two non-linear calibration techniques, boosted regression trees (BT) and support vector machines (SVM) applied to the whole library. The whole library calibrations for clay performed well (ME (modelling efficiency) > 0.82; RMSE (root mean squared error) < 10.9%), reflecting the influence of the direct spectral responses of this property in the vis-NIR range. Calibrations for OM were reasonably good, especially in view of the very small variation in this property (ME > 0.60; RMSE < 0.55%). The best results were, however, found when dividing the large library into smaller subsets by using variation in the mean-normalized or first derivative spectra. This divided the global data set into clusters that were more uniform in mineralogy, regardless of geographical origin, and improved predictive performance. The best clustering method improved the RMSE in the validation to 8.6% clay and 0.47% OM, which corresponds to a 21% and 15% reduction, respectively, as compared with whole library PLSR. For the whole library, SVM performed almost equally well, reducing RMSE to 8.9% clay and 0.48% OM.

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