Raphael A. Viscarra Rossel

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 Performance of Visible, Near-, and Mid-Infrared Reflectance Spectroscopy for Prediction of Soil Physical, Chemical, and Biological Properties

Abstract This review addresses the applicability of visible (Vis), near-infrared (NIR), and mid-infrared (MIR) reflectance spectroscopy for the prediction of soil properties. We address (1) the properties that can be predicted and the accuracy of the predictions, (2) the most suitable spectral regions for specific soil properties, (3) the number of predictions reported for each property, and (4) in-field versus laboratory spectral techniques. We found the following properties to be successfully predicted: soil water content, texture, soil carbon (C), cation exchange capacity, calcium and magnesium (exchangeable), total nitrogen (N), pH, concentration of metals/metalloids, microbial size, and activity. Generally, MIR produced better predictions than Vis-NIR, but Vis-NIR outperformed MIR for a number of properties (e.g., biological). An advantage of Vis-NIR is instrument portability although a new range of MIR portable devices is becoming available. In-field predictions for clay, water, total organic C, extractable phosphorus, total C and N appear similar to laboratory methods, but there are issues regarding, for example, sample heterogeneity, moisture content, and surface roughness. The nature of the variable being predicted, the quality and consistency of the reference laboratory methods, and the adequate representation of unknowns by the calibration set must be considered when predicting soil properties using reflectance spectroscopy.

Baseline map of organic carbon in Australian soil to support national carbon accounting and monitoring under climate change.

We can effectively monitor soil condition—and develop sound policies to offset the emissions of greenhouse gases—only with accurate data from which to define baselines. Currently, estimates of soil organic C for countries or continents are either unavailable or largely uncertain because they are derived from sparse data, with large gaps over many areas of the Earth. Here, we derive spatially explicit estimates, and their uncertainty, of the distribution and stock of organic C in the soil of Australia. We assembled and harmonized data from several sources to produce the most comprehensive set of data on the current stock of organic C in soil of the continent. Using them, we have produced a fine spatial resolution baseline map of organic C at the continental scale. We describe how we made it by combining the bootstrap, a decision tree with piecewise regression on environmental variables and geostatistical modelling of residuals. Values of stock were predicted at the nodes of a 3-arc-sec (approximately 90 m) grid and mapped together with their uncertainties. We then calculated baselines of soil organic C storage over the whole of Australia, its states and territories, and regions that define bioclimatic zones, vegetation classes and land use. The average amount of organic C in Australian topsoil is estimated to be 29.7 t ha−1 with 95% confidence limits of 22.6 and 37.9 t ha−1. The total stock of organic C in the 0–30 cm layer of soil for the continent is 24.97 Gt with 95% confidence limits of 19.04 and 31.83 Gt. This represents approximately 3.5% of the total stock in the upper 30 cm of soil worldwide. Australia occupies 5.2% of the global land area, so the total organic C stock of Australian soil makes an important contribution to the global carbon cycle, and it provides a significant potential for sequestration. As the most reliable approximation of the stock of organic C in Australian soil in 2010, our estimates have important applications. They could support Australias National Carbon Accounting System, help guide the formulation of policy around carbon offset schemes, improve Australias carbon balances, serve to direct future sampling for inventory, guide the design of monitoring networks and provide a benchmark against which to assess the impact of changes in land cover, land management and climate on the stock of C in Australia. In this way, these estimates would help us to develop strategies to adapt and mitigate the effects of climate change.

Development of a national VNIR soil-spectral library for soil classification and prediction of organic matter concentrations

Soil visible-near infrared diffuse reflectance spectroscopy (vis-NIR DRS) has become an important area of research in the fields of remote and proximal soil sensing. The technique is considered to be particularly useful for acquiring data for soil digital mapping, precision agriculture and soil survey. In this study, 1581 soil samples were collected from 14 provinces in China, including Tibet, Xinjiang, Heilongjiang, and Hainan. The samples represent 16 soil groups of the Genetic Soil Classification of China. After air-drying and sieving, the diffuse reflectance spectra of the samples were measured under laboratory conditions in the range between 350 and 2500 nm using a portable vis-NIR spectrometer. All the soil spectra were smoothed using the Savitzky-Golay method with first derivatives before performing multivariate data analyses. The spectra were compressed using principal components analysis and the fuzzy k-means method was used to calculate the optimal soil spectral classification. The scores of the principal component analyses were classified into five clusters that describe the mineral and organic composition of the soils. The results on the classification of the spectra are comparable to the results of other similar research. Spectroscopic predictions of soil organic matter concentrations used a combination of the soil spectral classification with multivariate calibration using partial least squares regression (PLSR). This combination significantly improved the predictions of soil organic matter (R2 = 0.899; RPD = 3.158) compared with using PLSR alone (R2 = 0.697; RPD = 1.817).

Sensor Fusion for Precision Agriculture

With the rapid rise in demand for both agricultural crop quantity and quality and with the growing concern of non-point pollution caused by modern farming practices, the efficiency and environmental safety of agricultural production systems have been questioned (Gebbers and Adamchuk, 2010). While implementing best management practices around the world, it was observed that the most efficient quantities of agricultural inputs vary across the landscape due to various naturally occurring, as well as man-induced, differences in key productivity factors such as water and nutrient supply. Identifying and understanding these differences allow for varying crop management practices according to locally defined needs (Pierce and Nowak, 1999). Such spatially-variable management practices have become the central part of precision agriculture (PA) management strategies being adapted by many practitioners around the world (Sonka et al., 1997). PA is an excellent example of a system approach where the use of the sensor fusion concept is essential. Among the different parameters that describe landscape variability, topography and soils are key factors that control variability in crop growing environments (Robert, 1993). Variations in crop vegetation growth typically respond to differences in these microenvironments together with the effects of management practice. Our ability to accurately recognize and account for any such differences can make production systems more efficient. Traditionally differences in physical, chemical and biological soil attributes have been detected through soil sampling and laboratory analysis (Wollenhaupt et al., 1997; de Gruijter et al., 2006). The cost of sampling and analysis are such that it is difficult to obtain enough samples to accurately characterize the landscape variability. This economic consideration resulting in low sampling density has been recognized as a major limiting factor. Both proximal and remote sensing technologies have been implemented to provide highresolution data relevant to the soil attributes of interest. Remote sensing involves the deployment of sensor systems using airborne or satellite platforms. Proximal sensing requires the operation of the sensor at close range, or even in contact, with the soil being

Assimilating satellite imagery and visible-near infrared spectroscopy to model and map soil loss by water erosion in Australia

Soil loss causes environmental degradation and reduces agricultural productivity over large areas of the world. Here, we use the latest earth observation data and soil visible-near infrared (vis-NIR) spectroscopy to estimate the factors of the Revised Universal Soil Loss Equation (RUSLE) and to model soil loss by water erosion in Australia. We estimate rainfall erosivity (R) using the Tropical Rainfall Measuring Mission (TRMM); slope length and steepness (L and S) using a 3-arcsec Shuttle Radar Topography Mission (SRTM) digital elevation model; cover management (C) and control practice (P) using the national dynamic land cover dataset (DLCD) of Australia derived from the moderate-resolution imaging spectroradiometer (MODIS); and soil erodibility (K) using vis-NIR estimates of the contents of sand, silt, clay and organic carbon in Australian soil. We model K using a machine-learning algorithm with environmental predictors selected to best capture the factors that influence erodibility and produced a digital map of K. We use the derived RUSLE factors to estimate soil loss at 1-km resolution across the whole of Australia. We found that the potential gross average soil loss by water erosion in Australian is 1.86?t?ha-1?y-1 (95% confidence intervals of 1.78 and 1.93?t?ha-1?y-1), equivalent to a total of 1242?×?106 tonnes of soil lost annually (95% confidence intervals of 1195 and 1293?t?×?106?y-1). Our estimates of erosion are generally smaller than previous continental estimates using the RUSLE, but particularly in croplands, which might indicate that soil conservation practices effectively reduced erosion in Australia. However we also identify localized regions with large erosion in northern Australia and northeastern Queensland. Erosion in these areas carries sediments laden with nitrogen, phosphorus and pollutants from agricultural production into the sea, negatively affecting marine ecosystems. We used the best available data and our results provide better estimates compared to previous assessments. Our approach will be valuable for other large, sparsely sampled areas of the world where assessments of soil erosion are needed. Display Omitted Remote sensing, spectroscopy and digital soil mapping were combined to model and map erosion in Australia using the RUSLE.The potential average soil loss by water erosion in Australia is 1.86 (?0.6) t?ha-1?y-1.The estimates are the most current for Australia.Our approach is novel and will be valuable for other large and sparsely sampled areas of the world.

A new detailed map of total phosphorus stocks in Australian soil

Accurate data are needed to effectively monitor environmental condition, and develop sound policies to plan for the future. Globally, current estimates of soil total phosphorus (P) stocks are very uncertain because they are derived from sparse data, with large gaps over many areas of the Earth. Here, we derive spatially explicit estimates, and their uncertainty, of the distribution and stock of total P in Australian soil. Data from several sources were harmonized to produce the most comprehensive inventory of total P in soil of the continent. They were used to produce fine spatial resolution continental maps of total P in six depth layers by combining the bootstrap, a decision tree with piecewise regression on environmental variables and geostatistical modelling of residuals. Values of percent total P were predicted at the nodes of a 3-arcsecond (approximately 90 m) grid and mapped together with their uncertainties. We combined these predictions with those for bulk density and mapped the total soil P stock in the 0-30 cm layer over the whole of Australia. The average amount of P in Australian topsoil is estimated to be 0.98 t ha(-1) with 90% confidence limits of 0.2 and 4.2 t ha(-1). The total stock of P in the 0-30 cm layer of soil for the continent is 0.91 Gt with 90% confidence limits of 0.19 and 3.9 Gt. The estimates are the most reliable approximation of the stock of total P in Australian soil to date. They could help improve ecological models, guide the formulation of policy around food and water security, biodiversity and conservation, inform future sampling for inventory, guide the design of monitoring networks, and provide a benchmark against which to assess the impact of changes in land cover, land use and management and climate on soil P stocks and water quality in Australia.

Quantitative Soil Spectroscopy

1 Section of Remote Sensing, Helmholtz Centre Potsdam, GFZ German Research Centre for Geosciences, Telegrafenberg, 14473 Potsdam, Germany 2The Remote Sensing Laboratory, Department of Geography and Human Environment, Tel-Aviv University, P.O. Box 39040, Ramat Aviv, 69978 Tel-Aviv, Israel 3 Soil and Landscape Program, CSIRO Land and Water, Bruce E. Butler Laboratory, Clunies-Ross Street Black Mountain, P.O. Box 1666, Canberra, ACT 2601, Australia 4 Soil Science Department, Luiz de Queiroz College of Agriculture, Sao Paulo, University of Piracicaba, SP 13418-900, Brazil

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.

Current and future assessments of soil erosion by water on the Tibetan Plateau based on RUSLE and CMIP5 climate models

Soil erosion by water is accelerated by a warming climate and negatively impacts water security and ecological conservation. The Tibetan Plateau (TP) has experienced warming at a rate approximately twice that observed globally, and heavy precipitation events lead to an increased risk of erosion. In this study, we assessed current erosion on the TP and predicted potential soil erosion by water in 2050. The study was conducted in three steps. During the first step, we used the Revised Universal Soil Equation (RUSLE), publicly available data, and the most recent earth observations to derive estimates of annual erosion from 2002 to 2016 on the TP at 1-km resolution. During the second step, we used a multiple linear regression (MLR) model and a set of climatic covariates to predict rainfall erosivity on the TP in 2050. The MLR was used to establish the relationship between current rainfall erosivity data and a set of current climatic and other covariates. The coefficients of the MLR were generalised with climate covariates for 2050 derived from the fifth phase of the Coupled Model Intercomparison Project (CMIP5) models to estimate rainfall erosivity in 2050. During the third step, soil erosion by water in 2050 was predicted using rainfall erosivity in 2050 and other erosion factors. The results show that the mean annual soil erosion rate on the TP under current conditions is 2.76tha-1y-1, which is equivalent to an annual soil loss of 559.59×106t. Our 2050 projections suggested that erosion on the TP will increase to 3.17tha-1y-1 and 3.91tha-1y-1 under conditions represented by RCP2.6 and RCP8.5, respectively. The current assessment and future prediction of soil erosion by water on the TP should be valuable for environment protection and soil conservation in this unique region and elsewhere.