Visible and near infrared spectroscopy in soil science

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. The development of the Global Soil Spectral Library might be a step in the right direction

Proximal sensing in soil profiles

Objective and quantitative soil information is crucial for pedological investigations and to inform diverse decision making processes. New techniques are required so that soil information can be ascertained in a timely manner to support sampling at finer spatial and temporal resolutions. Currently, no single technique can provide information on all of the properties of interest. This research investigated the conjoint use of visible near-infrared diffuse reflectance spectroscopy (VisNIR) and portable X-ray fluorescence spectroscopy (pXRF) for the in situ investigation of soil properties, profile variability and description. Fifteen soil pits across New South Wales, Australia, were selected for their diverse representation of soil properties. Sampling at these sites involved scanning three vertical with sensor readings taken at 2.5 cm intervals to a depth of 1 m within each transect. Soils were described by traditional pit description techniques and horizon based sampling was conducted to characterise the soil in terms of mineral composition, OC, TC, TN, CEC, EC, pH and PSA. A data fusion approach involving model averaging, and a mass balance was implemented to characterise the mineral composition of soils, including phyllosilicates sesquioxides, carbonate, gypsum, quartz and feldspars. Results were validated against X-ray diffraction analysis. To explore the predictive capability of scans taken in situ, existing spectral libraries were used to calibrate VisNIR and pXRF models and identify the best use of proximal sensor data to maximise soil information gain. As not all properties of interest have detectable spectral activity by either VisNIR or pXRF, a spectral soil inference system (SPEC-SINFERS) to augment the number of predicted properties. This system involved the propagation of sensor and model uncertainties through one hundred independent simulations for each calculation, and allowed the integration of both regression models and machine learning techniques

A selection of sensing techniques for mapping soil hydraulic properties

Data on soil hydraulic properties are needed as input for many models, such as models to predict unsaturated water movement and crop growth, and models to predict leaching of nutrients and pesticides to groundwater. The soil physics database of the Netherlands shows several lacunae, and a substantial part of the data were collected more than thirty years ago and thus might not represent actual soil hydraulic conditions

Combined use of Vis-NIR and XRF sensors for tropical soil fertility analysis : assessing different data fusion approaches

Visible and near infrared (vis-NIR) diffuse reflectance and X-ray fluorescence (XRF) sensors are promising proximal soil sensing (PSS) tools for predicting soil key fertility attributes. This work aimed at assessing the performance of the individual and combined use of vis-NIR and XRF sensors to predict clay, organic matter (OM), cation exchange capacity (CEC), pH, base saturation (V), and extractable (ex-) nutrients (ex-P, ex-K, ex-Ca, and ex-Mg) in Brazilian tropical soils. Individual models using the data of each sensor alone were calibrated using multiple linear regressions (MLR) for the XRF data, and partial least squares (PLS) regressions for the vis-NIR data. Six data fusion approaches were evaluated and compared against individual models using relative improvement (RI). The data fusion approaches included (i) two spectra fusion approaches, which simply combined the data of both sensors in a merged dataset, followed by support vector machine (SF-SVM) and PLS (SF-PLS) regression analysis; (ii) two model averaging approaches using the Granger and Ramanathan (GR) method; and (iii) two data fusion methods based on least squares (LS) modeling. For the GR and LS approaches, two different combinations of inputs were used for MLR. The GR2 and LS2 used the prediction of individual sensors, whereas the GR3 and LS3 used the individual sensors prediction plus the SF-PLS prediction. The individual vis-NIR models showed the best results for clay and OM prediction (RPD >= 2.61), while the individual XRF models exhibited the best predictive models for CEC, V, ex-K, ex-Ca, and ex-Mg (RPD >= 2.57). For eight out of nine soil attributes studied (clay, CEC, pH, V, ex-P, ex-K, ex-Ca, and ex-Mg), the combined use of vis-NIR and XRF sensors using at least one of the six data fusion approaches improved the accuracy of the predictions (with RI ranging from 1 to 21%). In general, the LS3 model averaging approach stood out as the data fusion method with the greatest number of attributes with positive RI (six attributes; namely, clay, CEC, pH, ex-P, ex-K, and ex-Mg). Meanwhile, no single approach was capable of exploiting the synergism between sensors for all attributes of interest, suggesting that the selection of the best data fusion approach should be attribute-specific. The results presented in this work evidenced the complementarity of XRF and vis-NIR sensors to predict fertility attributes in tropical soils, and encourage further research to find a generalized method of data fusion of both sensors data. Keywords Author Keywords

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Pedometrics: innovation in tropics; Legacy data: how turn it useful?; Advances in soil sensing; Pedometric guidelines to systematic soil surveys.Evento online. Coordenado por: Waldir de Carvalho Junior, Helena Saraiva Koenow Pinheiro, Ricardo Simão Diniz Dalmolin