ORCID Profile
0000-0003-1540-4748
Current Organisation
Curtin University
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Research Topics
In Research Link Australia (RLA), "Research Topics" refer to ANZSRC FOR and SEO codes. These topics are either sourced from ANZSRC FOR and SEO codes listed in researchers' related grants or generated by a large language model (LLM) based on their publications.
Top 5 Research Topics
ANZSRC Field of Research (FoR)
Soil Sciences | Environment and Resource Economics | Simulation and Modelling | Environmental Science and Management | Aboriginal and Torres Strait Islander Environmental Knowledge | Analytical Spectrometry | Carbon Sequestration Science | Soil And Water Sciences Not Elsewhere Classified | Environmental Rehabilitation (excl. Bioremediation) | Land Capability And Soil Degradation | Natural Resource Management |
ANZSRC Socio-Economic Objective (SEO)
Sparseland, Permanent Grassland and Arid Zone Soils | Climate Change Mitigation Strategies | Ecosystem Assessment and Management of Sparseland, Permanent Grassland and Arid Zone Environments | Land and water management | Rehabilitation of Degraded Farmland, Arable Cropland and Permanent Cropland Environments | Aboriginal and Torres Strait Islander Development and Welfare | Native Forests | Land and water management | Environmental and resource evaluation not elsewhere classified
Related Links
Publications
Diffuse reflectance spectroscopy characterises the functional chemistry of soil organic carbon in agricultural soils
Publisher: Wiley
Date: 07-2022
DOI: 10.1111/EJSS.13263
Abstract: Soil organic carbon (SOC) originates from a complex mixture of organic materials, and to better understand its role in soil functions, one must characterise its chemical composition. However, current methods, such as solid‐state 13 C nuclear magnetic resonance (NMR) spectroscopy, are time‐consuming and expensive. Diffuse reflectance spectroscopy in the visible, near infrared and mid‐infrared regions (vis–NIR: 350–2500 nm mid‐IR: 4000–400 cm −1 ) can also be used to characterise SOC chemistry however, it is difficult to know the frequencies where the information occurs. Thus, we correlated the C functional groups from the 13 C NMR to the frequencies in the vis–NIR and mid‐IR spectra using two methods: (1) 2‐dimensional correlations of 13 C NMR spectra and the diffuse reflectance spectra, and (2) modelling the NMR functional C groups with the reflectance spectra using support vector machines (SVM) (validated using 5 times repeated 10‐fold cross‐validation). For the study, we used 99 mineral soils from the agricultural regions of Sweden. The results show clear correlations between organic functional C groups measured with NMR and specific frequencies in the vis–NIR and mid‐IR spectra. While the 2D correlations showed general relationships (mainly related to the total SOC content), analysing the importance of the wavelengths in the SVM models revealed more detail. Generally, models using mid‐IR spectra produced slightly better estimates than the vis–NIR. The best estimates were for the alkyl C group (R 2 = 0.83 and 0.85, vis–NIR and mid‐IR, respectively), and the O/N‐alkyl C group was the most difficult to estimate (R 2 = 0.34 and 0.38, vis–NIR and mid‐IR, respectively). Combining 13 C NMR with the cost‐effective diffuse reflectance methods could potentially increase the number of measured s les and improve the spatial and temporal characterisation of SOC. However, more studies with a wider range of soil types and land management systems are needed to further evaluate the conditions under which these methods could be used. Diffuse reflectance spectroscopy was used to characterise and model SOC functional chemistry. NMR derived C functional groups could be modelled with vis‐NIR and mid‐IR diffuse reflectance spectra. The methods allow for characterisation of SOC chemical composition on whole mineral soil s les. The approach can improve the spatial and temporal characterisation of SOC composition.
Spectral libraries for quantitative analyses of tropical Brazilian soils: Comparing vis–NIR and mid-IR reflectance data
Publisher: Elsevier BV
Date: 10-2015
A global spectral library to characterize the world's soil
Publisher: Elsevier BV
Date: 04-2016
In situ measurements of soil colour, mineral composition and clay content by vis-NIR spectroscopy
Publisher: Elsevier BV
Date: 05-2009
Using a legacy soil sample to develop a mid-IR spectral library
Publisher: CSIRO Publishing
Date: 2008
DOI: 10.1071/SR07099
Abstract: This paper describes the development of a diffuse reflectance spectral library from a legacy soil s le. When developing a soil spectral library, it is important to consider the number of s les that are needed to adequately describe the soil variability in the region in which the library is to be used the manner in which the soil is s led, handled, prepared, stored, and scanned and the reference analytical procedures used. As with any type of modelling, the dictum is ‘garbage in = garbage out’ and hopefully the converse ‘quality in = quality out’. The aims of this paper are to: (i) develop a soil mid infrared (mid-IR) diffuse reflectance spectral library for cotton-growing regions of eastern Australia from a legacy soil s le, (ii) derive soil spectral calibrations for the prediction of soil properties with uncertainty, and (iii) assess the accuracy of the predictions and populate the legacy soil database with good quality information. A scheme for the construction and use of this spectral library is presented. A total of 1878 soil s les from different layers were scanned. They originated from the Upper Namoi, Namoi, and Gwydir Valley catchments of north-western New South Wales (NSW) and the McIntyre region of southern Queensland (Qld). A conditioned Latin hypercube s ling (cLHS) scheme was used to s le the spectral data space and select 213 representative s les for laboratory soil analyses. Using these data, partial least-squares regression (PLSR) was used to construct the calibration models, which were validated internally using cross validation and externally using an independent test dataset. Models for organic C (OC), cation exchange capacity (CEC), clay content, exchangeable Ca, total N (TN), total C (TC), gravimetric moisture content θg, total sand and exchangeable Mg were robust and produced accurate results (R2adj. 0.75 for both cross and test set validations). The root mean squared error (RMSE) of mid-IR-PLSR predictions was compared to those from (blind) duplicate laboratory measurements. Mid-IR-PLSR produced lower RMSE values for soil OC, clay content, and θg. Finally, bootstrap aggregation-PLSR (bagging-PLSR) was used to predict soil properties with uncertainty for the entire library, thus repopulating the legacy soil database with good quality soil information.
Spatial uncertainty of the137Cs reference inventory for Australian soil
Publisher: American Geophysical Union (AGU)
Date: 11-2011
DOI: 10.1029/2010JF001942
Microbial dynamics and soil physicochemical properties explain large-scale variations in soil organic carbon.
Publisher: Wiley
Date: 12-02-2020
DOI: 10.1111/GCB.14994
Abstract: First-order organic matter decomposition models are used within most Earth System Models (ESMs) to project future global carbon cycling these models have been criticized for not accurately representing mechanisms of soil organic carbon (SOC) stabilization and SOC response to climate change. New soil biogeochemical models have been developed, but their evaluation is limited to observations from laboratory incubations or few field experiments. Given the global scope of ESMs, a comprehensive evaluation of such models is essential using in situ observations of a wide range of SOC stocks over large spatial scales before their introduction to ESMs. In this study, we collected a set of in situ observations of SOC, litterfall and soil properties from 206 sites covering different forest and soil types in Europe and China. These data were used to calibrate the model MIMICS (The MIcrobial-MIneral Carbon Stabilization model), which we compared to the widely used first-order model CENTURY. We show that, compared to CENTURY, MIMICS more accurately estimates forest SOC concentrations and the sensitivities of SOC to variation in soil temperature, clay content and litter input. The ratios of microbial biomass to total SOC predicted by MIMICS agree well with independent observations from globally distributed forest sites. By testing different hypotheses regarding (using alternative process representations) the physicochemical constraints on SOC deprotection and microbial turnover in MIMICS, the errors of simulated SOC concentrations across sites were further decreased. We show that MIMICS can resolve the dominant mechanisms of SOC decomposition and stabilization and that it can be a reliable tool for predictions of terrestrial SOC dynamics under future climate change. It also allows us to evaluate at large scale the rapidly evolving understanding of SOC formation and stabilization based on laboratory and limited filed observation.
Spatial uncertainty of 137Cs-derived net (1950s-1990) soil redistribution for Australia
Publisher: American Geophysical Union (AGU)
Date: 11-2011
DOI: 10.1029/2010JF001943
The likelihood of observing dust-stimulated phytoplankton growth in waters proximal to the Australian continent
Publisher: Elsevier BV
Date: 05-2013
Continental drivers of ammonium and nitrate in Australian soil under different land uses
Publisher: Copernicus GmbH
Date: 24-04-2018
DOI: 10.5194/SOIL-2018-5
Abstract: Abstract. Soil N is an essential element for plant growth, but its mineral forms are subject to loss to the environment by leaching and gaseous emissions. Despite its importance for the soil-plant system, factors controlling soil mineral N concentrations over large spatial scales are not well understood. We used NH4+ and NO3− concentrations (0–30 cm depth) from 469 sites across Australia, and determined soil controls on their regional variation. Soil mineral N varied regionally but depended on the different land uses. In the agricultural region of Australia, NH4+ tended to be depleted (4.9 ± 4.8 vs. 5.6 ± 9.0 mg N kg−1) and NO3− was significantly enriched (6.0 ± 9.2 vs. 3.8 ± 9.9 mg N kg−1), compared to the non-agricultural ecological region. The relative importance of soil controls on mineral N in the agricultural region, identified by the model trees algorithm Cubist, showed that NH4+ was affected by total N, cation exchange capacity (CEC) and pH. In the ecological region, NH4+ was affected by CEC and pH, but also organic C and total P. In each of the regions, NO3− was primarily affected by CEC, with more complex biophysical controls. In both regions, correlations between mineral N and soil C : N : P stoichiometry suggest that more NH4+ was found in P-depleted soil relative to total C and total N. However, our results showed that only in the other ecological region, NO3− was sensitive to the state of C and its interaction with N and P. The models helped to explain 36–68 % of regional variation in mineral N. Although soil controls on high N concentrations was highly uncertain, we found that region-specific interactions of soil properties control mineral N concentrations and therefore it is essential to understand how they alter soil mechanisms and N cycling at large scales.
Soil environment grouping system based on spectral, climate, and terrain data: a quantitative branch of soil series
Publisher: Copernicus GmbH
Date: 12-05-2020
Abstract: Abstract. Soil classification has traditionally been developed by combining the interpretation of taxonomic rules that are related to soil information with the pedologist's tacit knowledge. Hence, a more quantitative approach is necessary to characterize soils with less subjectivity. The objective of this study was to develop a soil grouping system based on spectral, climate, and terrain variables with the aim of establishing a quantitative way of classifying soils. Spectral data were utilized to obtain information about the soil, and this information was complemented by climate and terrain variables in order to simulate the pedologist knowledge of soil–environment interactions. We used a data set of 2287 soil profiles from five Brazilian regions. The soil classes of World Reference Base (WRB) system were predicted using the three above-mentioned variables, and the results showed that they were able to correctly classify the soils with an overall accuracy of 88 %. To derive the new system, we applied the spectral, climatic, and terrain variables, which – using cluster analysis – defined eight groups thus, these groups were not generated by the traditional taxonomic method but instead by grouping areas with similar characteristics expressed by the variables indicated. They were denominated as “soil environment groupings” (SEGs). The SEG system facilitated the identification of groups with equivalent characteristics using not only soil but also environmental variables for their distinction. Finally, the conceptual characteristics of the eight SEGs were described. The new system has been designed to incorporate applicable soil data for agricultural management, to require less interference from personal/subjective/empirical knowledge (which is an issue in traditional taxonomic systems), and to provide more reliable automated measurements using sensors.
Diagnostic screening of urban soil contaminants using diffuse reflectance spectroscopy
Publisher: CSIRO Publishing
Date: 2009
DOI: 10.1071/SR08068
Abstract: There is increasing demand for cheap and rapid screening tests for soil contaminants in environmental consultancies. Diffuse reflectance spectroscopy (DRS) in the visible-near infrared (vis-NIR) and mid infrared (MIR) has the potential to meet this demand. The aims of this paper were to develop diagnostic screening tests for heavy metals and polycyclic aromatic hydrocarbons (PAH) in soil using vis-NIR and MIR DRS. Cadmium, copper, lead, and zinc were analysed, as were total PAH and benzo[a]pyrene. An ordinal logistic regression technique was used for the screening and predictions of either contaminated or uncontaminated soil at different thresholds. We calculated the rates of false positive and false negative predictions and derived Receiver Operating Characteristic curves to explore how the choice of a threshold affects their proportion. Zinc and copper had the best prediction accuracies of the heavy metals, with 89% and 85%, respectively. Cadmium and lead had the lowest prediction accuracies, with 68% and 67%, respectively. PAH predictions averaged 78.9%. With an average prediction accuracy of 79.9%, MIR analysis was only slightly more accurate than vis-NIR analysis, which had an average prediction accuracy of 77.5%. However, vis-NIR may be used in situ, thereby reducing cost and time of analysis and providing diagnosis in ‘real-time’. DRS in the vis-NIR can substantially decrease both the time and cost associated with screening for soil contaminants.
Evaluation of two methods to eliminate the effect of water from soil vis–NIR spectra for predictions of organic carbon
Publisher: Elsevier BV
Date: 06-2017
Comparing spectral soil inference systems and mid-infrared spectroscopic predictions of soil moisture retention
Publisher: Wiley
Date: 09-2008
Precision Agriculture: Proximal Soil Sensing
Publisher: Springer Netherlands
Date: 2011
Using targeted sampling to process multivariate soil sensing data
Publisher: Elsevier BV
Date: 06-2011
The cost‐efficiency and reliability of two methods for soil organic C accounting
Publisher: Wiley
Date: 20-02-2018
DOI: 10.1002/LDR.2887
Using Wavelets to Analyse Proximally Sensed Vis-NIR Soil Spectra
Publisher: Springer Netherlands
Date: 2010
Soil Analysis Using Visible and Near Infrared Spectroscopy
Publisher: Humana Press
Date: 10-2012
DOI: 10.1007/978-1-62703-152-3_6
Abstract: 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.
Advances in Agronomy
Publisher: Elsevier
Date: 2011
Diagnostic Screening of Urban Soil Contaminants Using Diffuse Reflectance Spectroscopy
Publisher: Springer Netherlands
Date: 2010
The Performance of Visible, Near-, and Mid-Infrared Reflectance Spectroscopy for Prediction of Soil Physical, Chemical, and Biological Properties
Publisher: Informa UK Limited
Date: 12-08-2014
Characterizing scale‐ and location‐specific variation in non‐linear soil systems using the wavelet transform
Publisher: Wiley
Date: 17-07-2013
DOI: 10.1111/EJSS.12063
Distinct controls over the temporal dynamics of soil carbon fractions after land use change
Publisher: Wiley
Date: 31-05-2020
DOI: 10.1111/GCB.15157
Abstract: Soil organic carbon (SOC), the largest terrestrial carbon pool, plays a significant role in soil‐related ecosystem services such as climate regulation, soil fertility and agricultural production. However, its fate under land use change is difficult to predict. A major issue is that SOC comprised of numerous organic compounds with potentially distinct and poorly understood turnover properties. Here we use spatiotemporal measurements of the particulate (POC), mineral‐associated (MOC) and charred SOC (COC) fractions from 176 trials involving changes in land use to assess their underlying controls. We find that the initial pool sizes of each of the three fractions consistently and dominantly control their temporal dynamics after changes in land use (i.e. the baseline effects). The effects of climate, soil physicochemical properties and plant residues, however, are fraction‐ and time‐dependent. Climate and soil properties show similar importance for controlling the dynamics of MOC and COC, while plant residue inputs (in term of their quantity and quality) are much less important. For POC, plant residues and management practices (e.g. the frequency of pasture in crop‐pasture rotation systems) are substantially more important, overriding the influence of climate. These results demonstrate the pivotal role of measuring SOC composition and considering fraction‐specific stabilization and destabilization processes for effective SOC management and reliable SOC predictions.
Digitally mapping the information content of visible-near infrared spectra of surficial Australian soils
Publisher: Elsevier BV
Date: 06-2011
Diffuse reflectance spectroscopy for estimating soil properties: A technology for the 21st century
Publisher: Wiley
Date: 07-2022
DOI: 10.1111/EJSS.13271
Abstract: Spectroscopic measurements of soil s les are reliable because they are highly repeatable and reproducible. They characterise the s les' mineral–organic composition. Estimates of concentrations of soil constituents are inevitably less precise than estimates obtained conventionally by chemical analysis. But the cost of each spectroscopic estimate is at most one‐tenth of the cost of a chemical determination. Spectroscopy is cost‐effective when we need many data, despite the costs and errors of calibration. Soil spectroscopists understand the risks of over‐fitting models to highly dimensional multivariate spectra and have command of the mathematical and statistical methods to avoid them. Machine learning has fast become an algorithmic alternative to statistical analysis for estimating concentrations of soil constituents from reflectance spectra. As with any modelling, we need judicious implementation of machine learning as it also carries the risk of over‐fitting predictions to irrelevant elements of the spectra. To use the methods confidently, we need to validate the outcomes with appropriately s led, independent data sets. Not all machine learning should be considered ‘black boxes’. Their interpretability depends on the algorithm, and some are highly interpretable and explainable. Some are difficult to interpret because of complex transformations or their huge and complicated network of parameters. But there is rapidly advancing research on explainable machine learning, and these methods are finding applications in soil science and spectroscopy. In many parts of the world, soil and environmental scientists recognise the merits of soil spectroscopy. They are building spectral libraries on which they can draw to localise the modelling and derive soil information for new projects within their domains. We hope our article gives readers a more balanced and optimistic perspective of soil spectroscopy and its future. Spectroscopy is reliable because it is a highly repeatable and reproducible analytical technique. Spectra are calibrated to estimate concentrations of soil properties with known error. Spectroscopy is cost‐effective for estimating soil properties. Machine learning is becoming ever more powerful for extracting accurate information from spectra, and methods for interpreting the models exist. Large libraries of soil spectra provide information that can be used locally to aid estimates from new s les.
Modelling the kinetics of buffer reactions for rapid field predictions of lime requirements
Publisher: Elsevier BV
Date: 05-2003
Assessment of soil properties in situ using a prototype portable MIR spectrometer in two agricultural fields
Publisher: Elsevier BV
Date: 12-2016
Baseline map of organic carbon in Australian soil to support national carbon accounting and monitoring under climate change
Publisher: Wiley
Date: 28-04-2014
DOI: 10.1111/GCB.12569
Special issue on proximal soil sensing
Publisher: Elsevier BV
Date: 05-2013
Similar importance of edaphic and climatic factors for controlling soil organic carbon stocks of the world
Publisher: Copernicus GmbH
Date: 22-03-2021
Abstract: Abstract. Soil organic carbon (SOC) accounts for two-thirds of terrestrial carbon. Yet, the role of soil physicochemical properties in regulating SOC stocks is unclear, inhibiting reliable SOC predictions under land use and climatic changes. Using legacy observations from 141 584 soil profiles worldwide, we disentangle the effects of biotic, climatic and edaphic factors (a total of 31 variables) on the global spatial distribution of SOC stocks in four sequential soil layers down to 2 m. The results indicate that the 31 variables can explain 60 %–70 % of the global variance of SOC in the four layers, to which climatic variables and edaphic properties each contribute ∼35 % except in the top 20 cm soil. In the top 0–20 cm soil, climate contributes much more than soil properties (43 % vs. 31 %), while climate and soil properties show the similar importance in the 20–50, 50–100 and 100–200 cm soil layers. However, the most important in idual controls are consistently soil-related and include soil texture, hydraulic properties (e.g. field capacity) and pH. Overall, soil properties and climate are the two dominant controls. Apparent carbon inputs represented by net primary production, biome type and agricultural cultivation are secondary, and their relative contributions were ∼10 % in all soil depths. This dominant effect of in idual soil properties challenges the current climate-driven framework of SOC dynamics and needs to be considered to reliably project SOC changes for effective carbon management and climate change mitigation.
Magnetic Domain State Diagnosis in Soils, Loess, and Marine Sediments From Multiple First-Order Reversal Curve-Type DiagramsTaxonomyNumbers
Publisher: American Geophysical Union (AGU)
Date: 02-2018
DOI: 10.1002/2017JB015195
Distance and similarity-search metrics for use with soil vis–NIR spectra
Publisher: Elsevier BV
Date: 05-2013
Multiscale contextual spatial modelling with the Gaussian scale space
Publisher: Elsevier BV
Date: 2018
Visible-Near Infrared Spectra as a Proxy for Topsoil Texture and Glacial Boundaries
Publisher: Wiley
Date: 25-02-2013
Proximal sensing for soil carbon accounting
Publisher: Copernicus GmbH
Date: 22-01-2018
DOI: 10.5194/SOIL-2017-36
Abstract: Abstract. Maintaining or increasing soil organic carbon (C) is important for securing food production, and for mitigating greenhouse gas (GHG) emissions, climate change and land degradation. Some land management practices in cropping, grazing, horticultural and mixed farming systems can be used to increase organic C in soil, but to assess their effectiveness, we need accurate and cost-efficient methods for measuring and monitoring the change. To determine the stock of organic C in soil, one needs measurements of soil organic C concentration, bulk density and gravel content, but using conventional laboratory-based analytical methods is expensive. Our aim here is to review the current state of proximal sensing for the development of new soil C accounting methods for emissions reporting and in emissions reduction schemes. We evaluated sensing techniques in terms of their rapidity, cost, accuracy, safety, readiness and their state of development. The most suitable technique for measuring soil organic C concentrations appears to be vis–NIR spectroscopy and for bulk density active gamma-ray attenuation. Sensors for measuring gravel have not been developed, but an interim solution with rapid wet-sieving and automated measurement appears useful. Field-deployable, multi-sensor systems are needed for cost-efficient soil C accounting. Proximal sensing can be used for soil organic C accounting, but the methods need to be standardised and procedural guidelines need to be developed to ensure proficient measurement and accurate reporting and verification. This is particularly important if the schemes use financial incentives for landholders to adopt management practices to sequester soil organic C. We list and discuss the requirements for the development of new soil C accounting methods that are based on proximal sensing, including requirements for recording, verification and auditing.
Multi-scale mapping of Australia’s terrestrial and blue carbon stocks and their continental and bioregional drivers
Publisher: Springer Science and Business Media LLC
Date: 06-2023
DOI: 10.1038/S43247-023-00838-X
Abstract: The soil in terrestrial and coastal blue carbon ecosystems is an important carbon sink. National carbon inventories require accurate assessments of soil carbon in these ecosystems to aid conservation, preservation, and nature-based climate change mitigation strategies. Here we harmonise measurements from Australia’s terrestrial and blue carbon ecosystems and apply multi-scale machine learning to derive spatially explicit estimates of soil carbon stocks and the environmental drivers of variation. We find that climate and vegetation are the primary drivers of variation at the continental scale, while ecosystem type, terrain, clay content, mineralogy and nutrients drive subregional variations. We estimate that in the top 0–30 cm soil layer, terrestrial ecosystems hold 27.6 Gt (19.6–39.0 Gt), and blue carbon ecosystems 0.35 Gt (0.20–0.62 Gt). Tall open eucalypt and mangrove forests have the largest soil carbon content by area, while eucalypt woodlands and hummock grasslands have the largest total carbon stock due to the vast areas they occupy. Our findings suggest these are essential ecosystems for conservation, preservation, emissions avoidance, and climate change mitigation because of the additional co-benefits they provide.
Predicting soil properties from the Australian soil visible–near infrared spectroscopic database
Publisher: Wiley
Date: 05-11-2012
Soil spectroscopy with the Gaussian pyramid scale space
Publisher: Elsevier BV
Date: 11-2022
The importance of sampling support for explaining change in soil organic carbon
Publisher: Elsevier BV
Date: 02-2013
National digital soil map of organic matter in topsoil and its associated uncertainty in 1980's China
Publisher: Elsevier BV
Date: 02-2019
Assimilating satellite imagery and visible–near infrared spectroscopy to model and map soil loss by water erosion in Australia
Publisher: Elsevier BV
Date: 03-2016
Soil chemical analytical accuracy and costs: Implications from precision agriculture
Publisher: CSIRO Publishing
Date: 1998
DOI: 10.1071/EA97158
Abstract: Summary. This article reviews soil s ling and soil chemical analysis, discussing their implications from, and applications in, precision agriculture. The variability of a number of agriculturally important soil chemical properties was investigated and the ‘nugget’ variance or effect discussed in terms of its importance in determining the proportion of not only short-range spatial variation, but also s ling and measurement error. Comments were then made on the accuracy of laboratory methods. Analytical variances were compared with world-average and estimated nugget variances for a field in New South Wales, the comparison showing that analytical precision needs to be maintained or improved when developing or adapting analytical methods for precision agriculture. A simple cost-analysis showed that soil chemical analytical costs are much too large for economic use in precision agriculture, costs in Australia being higher than in the United States. The conclusion this paper draws is that, for large-scale implementation of precision agriculture, the development of field-deployed, ‘on-the-go’ proximal soil sensing systems and scanners is tremendously important. These sensing systems or scanners should aim to overcome current problems of high cost, labour, time and to some extent, imprecision of soil s ling and analysis to more efficiently and accurately represent the spatial variability of the measured properties.
Estimating soil fungal abundance and diversity at a macroecological scale with deep learning spectrotransfer functions
Publisher: Copernicus GmbH
Date: 13-09-2021
DOI: 10.5194/SOIL-2021-79
Abstract: Abstract. Soil fungi play important roles in the functioning of ecosystems, but they are challenging to measure. Using a continental scale dataset, we developed and evaluated a new method to estimate the relative abundance of the dominant phyla and ersity of fungi in Australian soil. The method relies on the development of spectro-transfer functions with state-of-the-art machine learning and using publicly available data on soil and environmental proxies for edaphic, climatic, biotic and topographic factors, and visible--near infrared (vis–NIR) wavelengths, to estimate the relative abundances of the Ascomycota, Basidiomycota, Glomeromycota, Mortierellomycota and Mucoromycota and community ersity measured with the abundance-based coverage estimator (ACE) index. The machine learning algorithms tested were partial least squares regression (PLSR), random forest (RF), Cubist, support vector machines (SVM), Gaussian process regression (GPR), XG-boost (XGB) and one-dimensional convolutional neural networks (1D-CNNs). The spectro-transfer functions were validated with a 10-fold cross-validation (n = 577). The 1D-CNNs outperformed the other algorithms and could explain between 45 and 73 % of fungal relative abundance and ersity. The models were interpretable, and showed that soil nutrients, pH, bulk density, an ecosystem water balance (a proxy for aridity) and net primary productivity were important predictors, as were specific vis–NIR wavelengths that correspond to organic functional groups, iron oxide and clay minerals. Estimates of the relative abundance for Mortierellomycota and Mucoromycota produced R2 ≥ 0.60, while estimates of the abundance of the Ascomycota and Basidiomycota produced R2 values of 0.5 and 0.58, respectively. The spectro-transfer functions for the Glomeromycota and ersity were the poorest with R2 values of 0.48 and 0.45, respectively. There is no doubt that the method provides estimates that are less accurate than more direct measurements with conventional molecular approaches. However, once the spectro-transfer functions are developed, they can be used with very little cost, and could serve to supplement the more expensive and laborious molecular approaches for a better understanding of soil fungal abundance and ersity under different agronomic and ecological settings.
Soil properties override climate controls on global soil organic carbon stocks
Publisher: Copernicus GmbH
Date: 11-08-2020
DOI: 10.5194/BG-2020-298
Abstract: Abstract. Soil organic carbon (SOC) accounts for two-thirds of terrestrial carbon. Yet, the role of soil physiochemical properties in regulating SOC stocks is unclear, inhibiting reliable SOC predictions under land use and climatic changes. Using legacy observations from 141,584 soil profiles worldwide, we disentangle the effects of biotic, climatic and edaphic factors (a total of 30 variables) on the global spatial distribution of SOC stocks in four sequential soil layers down to 2 m. The results indicate that the 30 variables can explain 70–80 % of the global variance of SOC in the four layers, to which edaphic properties contribute ~ 60 %. Soil lower limit is the most important in idual soil properties, positively associated with SOC in all layers, while climatic variables are secondary. This dominant effect of soil properties challenges current climate-driven framework of SOC dynamics, and need to be considered to reliably project SOC changes for effective carbon management and climate change mitigation.
Separating scale-specific soil spatial variability: A comparison of multi-resolution analysis and empirical mode decomposition
Publisher: Elsevier BV
Date: 11-2013
Developing the Swiss soil spectral library for local estimation and monitoring
Publisher: Copernicus GmbH
Date: 22-02-2021
Abstract: Abstract. Information on soils' composition and physical, chemical and biological properties is paramount to elucidate agroecosystem functioning in space and over time. For this purposes we developed a national Swiss soil spectral library (SSL n = 4374) in the mid-infrared (mid-IR), calibrating 17 properties from legacy measurements on soils from the Swiss bio ersity monitoring program (n = 3778 1094 sites) and the Swiss long-term monitoring network (n = 596 71 sites). General models were trained with the interpretable rule-based learner CUBIST, testing combinations of {5, 10, 20, 50, 100} committees of rules and {2, 5, 7, 9} neighbors to localize predictions with repeated by location grouped ten-fold cross-validation. To evaluate the information in spectra to facilitate long-term soil monitoring at a plot-level, we conducted 71 model transfers for the NABO sites to induce locally relevant information from the SSL, using the data-driven s le selection method rs-local. Eleven soil properties were estimated with discrimination capacity suitable for screening (R2 0.6), out of which total carbon (C), organic C (OC), total N, organic matter content, pH, and clay showed accuracy eligible for accurate diagnostics (R2 0.8). Cubist and the spectra estimated total C accurately with RMSE = 0.84 % while the measured range was 0.1–58.3 %, and OC with RMSE = 1.20 % (measured range 0.0–27.3 %). Compared to general estimates of properties from Cubist, local modeling on average reduced the root mean square error of total C per site fourfold. We found that the selected SSL subsets were highly dissimilar in terms of both their spectral input space and the measured values. This suggests that data-driven selection with RS-LOCAL leverages chemical ersity in composition rather than similarity. Our results suggest that mid-IR soil estimates were sufficiently accurate to support many soil applications that require a large volume of input data, such as precision agriculture, soil C accounting and monitoring, and digital soil mapping. This SSL can be updated continuously, for ex le with s les from deeper profiles and organic soils, so that the measurement of key soil properties becomes even more accurate and efficient in the near future.
Proximal sensing for soil carbon accounting
Publisher: Copernicus GmbH
Date: 15-05-2018
Abstract: Abstract. Maintaining or increasing soil organic carbon (C) is vital for securing food production and for mitigating greenhouse gas (GHG) emissions, climate change, and land degradation. Some land management practices in cropping, grazing, horticultural, and mixed farming systems can be used to increase organic C in soil, but to assess their effectiveness, we need accurate and cost-efficient methods for measuring and monitoring the change. To determine the stock of organic C in soil, one requires measurements of soil organic C concentration, bulk density, and gravel content, but using conventional laboratory-based analytical methods is expensive. Our aim here is to review the current state of proximal sensing for the development of new soil C accounting methods for emissions reporting and in emissions reduction schemes. We evaluated sensing techniques in terms of their rapidity, cost, accuracy, safety, readiness, and their state of development. The most suitable method for measuring soil organic C concentrations appears to be visible–near-infrared (vis–NIR) spectroscopy and, for bulk density, active gamma-ray attenuation. Sensors for measuring gravel have not been developed, but an interim solution with rapid wet sieving and automated measurement appears useful. Field-deployable, multi-sensor systems are needed for cost-efficient soil C accounting. Proximal sensing can be used for soil organic C accounting, but the methods need to be standardized and procedural guidelines need to be developed to ensure proficient measurement and accurate reporting and verification. These are particularly important if the schemes use financial incentives for landholders to adopt management practices to sequester soil organic C. We list and discuss requirements for developing new soil C accounting methods based on proximal sensing, including requirements for recording, verification, and auditing.
Sensing of soil bulk density for more accurate carbon accounting
Publisher: Wiley
Date: 07-2016
DOI: 10.1111/EJSS.12355
The cost‐effectiveness of reflectance spectroscopy for estimating soil organic carbon
Publisher: Wiley
Date: 30-12-2021
DOI: 10.1111/EJSS.13202
Abstract: Reflectance spectra of soil can be used to estimate the concentrations of organic carbon in soil (SOC). The estimates are more or less imprecise, but spectroscopy is quicker, less laborious and cheaper than conventional dry combustion analysis. Are the greater economy and efficiency sufficient to justify the loss of information arising from errors in estimation? We measured soil spectra with three instruments: a bench‐top mid‐infrared (mid‐IR) (mid‐IR b ) spectrometer, a portable mid‐IR (mid‐IR p ) spectrometer and a portable visible–near infrared (vis–NIR p ) spectrometer. We calculated a quantity E to express the cost‐effectiveness of spectroscopic estimates relative to the conventional analysis, by accounting for their inaccuracy, their cost and their capacity, namely the maximum number of s les that can be prepared and measured daily. In all, 562 s les of soil were collected from 150 locations at four depths on a farm. The s les were dried and ground to particle sizes of ≤2 and ≤0.5 mm before measurements were made by dry‐combustion analysis. The machine learning algorithm Cubist was used to derive spectroscopic models of SOC concentrations and their uncertainties. We found that the mid‐IR b on the ≤0.5 mm s les was the most accurate and expensive but nevertheless sufficiently cost‐effective (large value of E ) for determining the organic C. The mid‐IR p was somewhat more accurate, but its E was smaller than vis–NIR p on corresponding s les because it required more time to record the spectra. We also found that, with the portable spectrometers, the SOC predictions made on the ≤0.5 mm s les were somewhat more accurate than those made on the ≤2 mm s les, but their E was smaller because of the additional cost of s le preparation. The vis–NIR p on the ≤2 mm s les was the most cost‐effective for estimating SOC because it is cheap, accurate and has a large capacity for measurements. Concentrations of soil organic carbon (SOC) were determined by standard dry combustion and estimated from reflectance spectra recorded by three instruments. The labour required for each of the techniques and the cost, including that of the equipment, were recorded. A quantity E , expressing the cost‐effectiveness relative to dry combustion was calculated for each spectral technique, taking into account both accuracy and cost. Dry combustion was always more accurate than estimates from spectra for in idual s les, and the technique was also more cost‐effective for small numbers of s les. The cost‐effectiveness of the spectral techniques varied among themselves, but all were more cost‐effective than dry combustion for large numbers of s les.
Accounting for the effects of water and the environment on proximally sensed vis–NIR soil spectra and their calibrations
Publisher: Wiley
Date: 09-03-2015
DOI: 10.1111/EJSS.12239
Multiple observation types reduce uncertainty in Australia's terrestrial carbon and water cycles
Publisher: Copernicus GmbH
Date: 25-03-2013
Abstract: Abstract. Information about the carbon cycle potentially constrains the water cycle, and vice versa. This paper explores the utility of multiple observation sets to constrain a land surface model of Australian terrestrial carbon and water cycles, and the resulting mean carbon pools and fluxes, as well as their temporal and spatial variability. Observations include streamflow from 416 gauged catchments, measurements of evapotranspiration (ET) and net ecosystem production (NEP) from 12 eddy-flux sites, litterfall data, and data on carbon pools. By projecting residuals between observations and corresponding predictions onto uncertainty in model predictions at the continental scale, we find that eddy flux measurements provide a significantly tighter constraint on continental net primary production (NPP) than the other data types. Nonetheless, simultaneous constraint by multiple data types is important for mitigating bias from any single type. Four significant results emerging from the multiply-constrained model are that, for the 1990–2011 period: (i) on the Australian continent, a predominantly semi-arid region, over half the water loss through ET (0.64 ± 0.05) occurs through soil evaporation and bypasses plants entirely (ii) mean Australian NPP is quantified at 2.2 ± 0.4 (1σ) Pg C yr−1 (iii) annually cyclic ("grassy") vegetation and persistent ("woody") vegetation account for 0.67 ± 0.14 and 0.33 ± 0.14, respectively, of NPP across Australia (iv) the average interannual variability of Australia's NEP (±0.18 Pg C yr−1, 1σ) is larger than Australia's total anthropogenic greenhouse gas emissions in 2011 (0.149 Pg C equivalent yr–1), and is dominated by variability in desert and savanna regions.
Soil organic carbon and its fractions estimated by visible–near infrared transfer functions
Publisher: Wiley
Date: 06-03-2015
DOI: 10.1111/EJSS.12237
National-scale spectroscopic assessment of soil organic carbon in forests of the Czech Republic
Publisher: Elsevier BV
Date: 03-2021
Guest Editorial: Near Infrared Spectroscopy for a Better Understanding of Soil
Publisher: SAGE Publications
Date: 2016
DOI: 10.1255/JNIRS.1234
Miniaturised visible and near-infrared spectrometers for assessing soil health indicators in mine site rehabilitation
Publisher: Copernicus GmbH
Date: 18-07-2022
Abstract: Abstract. Mining can cause severe disturbances to the soil, which underpins the viability of terrestrial ecosystems. Post-mining rehabilitation relies on measuring soil properties that are critical indicators of soil health. Soil visible–near-infrared (vis–NIR) spectroscopy is rapid, accurate, and cost-effective for estimating a range of soil properties. Recent advances in infrared detectors and microelectromechanical systems (MEMSs) have produced miniaturised, relatively inexpensive spectrometers. Here, we evaluate the spectra from four miniaturised visible and NIR spectrometers, some combinations, and a full-range vis–NIR spectrometer for modelling 29 soil physical, chemical, and biological properties used to assess soil health at mine sites. We collected topsoil s les from reference, undisturbed native vegetation, and stockpiles from seven mines in Western Australia. We evaluated the spectrometers' repeatability and the accuracy of spectroscopic models built with seven statistical and machine learning algorithms. The spectra from the visible spectrometer could estimate sand, silt, and clay with similar or better accuracy than the NIR spectrometers. However, the spectra from the NIR spectrometers produced better estimates of soil chemical and biological properties. By combining the miniaturised visible and NIR spectrometers, we improved the accuracy of their soil property estimates, which were similar to those from the full-range spectrometer. The miniaturised spectrometers and combinations predicted 24 of the 29 soil properties with moderate or greater accuracy (Lin's concordance correlation, ρc≥0.65). The repeatability of the NIR spectrometers was similar to that of the full-range, portable spectrometer. The miniaturised NIR spectrometers produced comparably accurate soil property estimates to the full-range portable system which is an order of magnitude more expensive, particularly when combined with the visible range sensor. Thus, the miniaturised spectrometers could form the basis for a rapid, cost-effective soil diagnostic capacity to support mine site rehabilitation and deliver significant positive economic and environmental outcomes.
Developing the Swiss mid-infrared soil spectral library for local estimation and monitoring
Publisher: Copernicus GmbH
Date: 18-08-2021
Abstract: Abstract. Information on soils' composition and physical, chemical and biological properties is paramount to elucidate agroecosystem functioning in space and over time. For this purpose, we developed a national Swiss soil spectral library (SSL n=4374) in the mid-infrared (mid-IR), calibrating 16 properties from legacy measurements on soils from the Swiss Bio ersity Monitoring program (BDM n=3778 1094 sites) and the Swiss long-term Soil Monitoring Network (NABO n=596 71 sites). General models were trained with the interpretable rule-based learner CUBIST, testing combinations of {5,10,20,50, and 100} ensembles of rules (committees) and {2, 5, 7, and 9} nearest neighbors used for local averaging with repeated 10-fold cross-validation grouped by location. To evaluate the information in spectra to facilitate long-term soil monitoring at a plot level, we conducted 71 model transfers for the NABO sites to induce locally relevant information from the SSL, using the data-driven s le selection method RS-LOCAL. In total, 10 soil properties were estimated with discrimination capacity suitable for screening (R2≥0.72 ratio of performance to interquartile distance (RPIQ) ≥ 2.0), out of which total carbon (C), organic C (OC), total nitrogen (N), pH and clay showed accuracy eligible for accurate diagnostics (R2 .8 RPIQ ≥ 3.0). CUBIST and the spectra estimated total C accurately with the root mean square error (RMSE) = 8.4 g kg−1 and the RPIQ = 4.3, while the measured range was 1–583 g kg−1 and OC with RMSE = 9.3 g kg−1 and RPIQ = 3.4 (measured range 0–583 g kg−1). Compared to the general statistical learning approach, the local transfer approach – using two respective training s les – on average reduced the RMSE of total C per site fourfold. We found that the selected SSL subsets were highly dissimilar compared to validation s les, in terms of both their spectral input space and the measured values. This suggests that data-driven selection with RS-LOCAL leverages chemical ersity in composition rather than similarity. Our results suggest that mid-IR soil estimates were sufficiently accurate to support many soil applications that require a large volume of input data, such as precision agriculture, soil C accounting and monitoring and digital soil mapping. This SSL can be updated continuously, for ex le, with s les from deeper profiles and organic soils, so that the measurement of key soil properties becomes even more accurate and efficient in the near future.
Assessment of the production and economic risks of site-specific liming using geostatistical uncertainty modelling
Publisher: Wiley
Date: 2001
DOI: 10.1002/ENV.471
Developing the Australian mid-infrared spectroscopic database using data from the Australian Soil Resource Information System
Publisher: CSIRO Publishing
Date: 2015
DOI: 10.1071/SR15171
Abstract: We developed and tested spectroscopic models derived by partial least squares regression (PLSR) using data from the Commonwealth Scientific and Industrial Research Organisation’s (CSIRO) national soil database (NatSoil) and soil s les from the Australian National Soil Archive. Models were constructed for 21 soil attributes, and their predictability assessed using the R2, ranged from 0.57 for bicarbonate extractable available phosphorus to 0.92 for the sum of the exchangeable bases. Investigating the spectral library coverage with a suite of 1484 unknown s les from a geochemical survey of Australian catchments, we found that the models could be used to predict many soil attributes in a geographically erse set of s les.
Toxicity and bioaccumulation of Cu in an accumulator crop (Lactuca sativa L.) in different Australian agricultural soils
Publisher: Elsevier BV
Date: 09-2015
The Australian three-dimensional soil grid: Australia's contribution to the GlobalSoilMap project
Publisher: CSIRO Publishing
Date: 2015
DOI: 10.1071/SR14366
Abstract: Information on the geographic variation in soil has traditionally been presented in polygon (choropleth) maps at coarse scales. Now scientists, planners, managers and politicians want quantitative information on the variation and functioning of soil at finer resolutions they want it to plan better land use for agriculture, water supply and the mitigation of climate change land degradation and desertification. The GlobalSoilMap project aims to produce a grid of soil attributes at a fine spatial resolution (approximately 100 m), and at six depths, for the purpose. This paper describes the three-dimensional spatial modelling used to produce the Australian soil grid, which consists of Australia-wide soil attribute maps. The modelling combines historical soil data plus estimates derived from visible and infrared soil spectra. Together they provide a good coverage of data across Australia. The soil attributes so far include sand, silt and clay contents, bulk density, available water capacity, organic carbon, pH, effective cation exchange capacity, total phosphorus and total nitrogen. The data on these attributes were harmonised to six depth layers, namely 0–0.05 m, 0.05–0.15 m, 0.15–0.30 m, 0.30–0.60 m, 0.60–1.00 m and 1.00–2.00 m, and the resulting values were incorporated simultaneously in the models. The modelling itself combined the bootstrap, a decision tree with piecewise regression on environmental variables and geostatistical modelling of residuals. At each layer, values of the soil attributes were predicted at the nodes of a 3 arcsecond (approximately 90 m) grid and mapped together with their uncertainties. The assessment statistics for each attribute mapped show that the models explained between 30% and 70% of their total variation. The outcomes are illustrated with maps of sand, silt and clay contents and their uncertainties. The Australian three-dimensional soil maps fill a significant gap in the availability of quantitative soil information in Australia.
Spectral soil analysis and inference systems: A powerful combination for solving the soil data crisis
Publisher: Elsevier BV
Date: 12-2006
Particulate and mineral-associated organic carbon turnover revealed by modelling their long-term dynamics
Publisher: Elsevier BV
Date: 10-2022
Supplementary material to "Estimating soil fungal abundance and diversity at a macroecological scale with deep learning spectrotransfer functions"
Publisher: Copernicus GmbH
Date: 13-09-2021
Soil bacterial abundance and diversity better explained and predicted with spectro-transfer functions
Publisher: Elsevier BV
Date: 02-2019
Improved analysis and modelling of soil diffuse reflectance spectra using wavelets
Publisher: Wiley
Date: 14-05-2009
Soil carbon simulation confounded by different pool initialisation
Publisher: Springer Science and Business Media LLC
Date: 14-12-2019
Integrating multi-source data to improve water erosion mapping in Tibet, China
Publisher: Elsevier BV
Date: 10-2018
Colour space models for soil science
Publisher: Elsevier BV
Date: 08-2006
Soil and landscape grid of Australia
Publisher: CSIRO Publishing
Date: 2015
DOI: 10.1071/SR15191
Abstract: The Soil and Landscape Grid of Australia (SLGA) is the first continental version of the GlobalSoilMap concept and the first nationally consistent, fine spatial resolution set of continuous soil attributes with Australia-wide coverage. The SLGA relies on digital soil mapping methods and integrates historical soil data, new measurement with spectroscopic sensors, novel spatial modelling and a web-service delivery architecture. The SLGA provides soil, regolith and landscape estimates at the centre point of 3 arcsecond grid cells (~90 × 90 m) across Australia. At each point, there are estimates of 11 soil attributes and confidence intervals for each estimate to a depth of 2 m or less, depth of regolith and a set of terrain descriptors. The information system also includes a library of mid-infrared spectra, an inference engine that allows estimation of additional soil parameters and an information model that enables users to access the system via web services. The explicit mapping of depth, bulk density and coarse fragments allows estimation of material stores and fluxes on a volumetric basis. The SLGA therefore has immediate applications in carbon, nitrogen and water process modelling. The map of regolith depth will find immediate application to studies of vadose zone processes, including solute transport, groundwater and nutrient fluxes beyond the root zone. Landscape attributes at 1 and 3 arcseconds are useful for a wide spectrum of ecological, hydrological and broader environmental applications. The SLGA can be accessed at no cost from www.csiro.au/soil-and-landscape-grid. It is managed and delivered as part of the Australian Soil Resource Information System (ASRIS).
Fine-resolution multiscale mapping of clay minerals in Australian soils measured with near infrared spectra
Publisher: American Geophysical Union (AGU)
Date: 23-11-2011
DOI: 10.1029/2011JF001977
Soil classification using visible/near-infrared diffuse reflectance spectra from multiple depths
Publisher: Elsevier BV
Date: 07-2014
Assessment of soil organic carbon at local scale with spiked NIR calibrations: effects of selection and extra‐weighting on the spiking subset
Publisher: Wiley
Date: 05-02-2014
DOI: 10.1111/EJSS.12129
Predicting soil carbon by efficiently using variation in a mid-IR soil spectral library
Publisher: Copernicus GmbH
Date: 08-01-2021
DOI: 10.5194/SOIL-2020-93
Abstract: Abstract. Traditional laboratory methods of acquiring soil information remain important for assessing key soil properties, soil functions and ecosystem services over space and time. Infrared spectroscopic modelling can link and massively scale up these methods for many soil characteristics in a cost-effective and timely manner. In Switzerland, only 10 % to 15 % of agricultural soils have been mapped sufficiently to serve spatial decision support systems, presenting an urgent need for rapid quantitative soil characterization. The current Swiss soil spectral library (SSL n = 4374) in the mid-infrared range includes soil s les from the Bio ersity Monitoring Program (BDM), arranged in a regularly spaced grid across Switzerland, and temporally-resolved data from the Swiss Soil Monitoring Network (NABO). Given the relatively low representation of organic soils and their organo-mineral ersity in the SSL, we aimed to develop both an efficient calibration s ling scheme and accurate modelling strategy to estimate soil carbon (SC) contents of heterogeneous s les between 0 m to 2 m depth from 26 locations within two drained peatland regions (HAFL dataset n = 116). The focus was on minimizing the need for new reference analyses by efficiently mining the spectral information of SSL instances and their target-feature representations. We used partial least square regressions (PLSR) together with a 5 times repeated, grouped by location, 10-fold cross validation (CV) to predict SC ranging from 1 % to 52 % in the local HAFL dataset. We compared the validation performance of different calibration schemes involving local models (1), models using the entire SSL spiked with local s les (2) and 15 subsets of local and SSL s les using the RS-LOCAL algorithm (3). Using local and RS-LOCAL calibrations with at least 5 local s les, we achieved similar validation results for predictions of SC up to 52 % (R2 = 0.94–0.96, bias = −0.6–1.5, RMSE = 2.6 % to 3.5 % total carbon). However, calibrations of representative SSL and local s les using RS-LOCAL only required 5 local s les for very accurate models (RMSE = 2.9 % total carbon), while local calibrations required 50 s les for similarly accurate results (RMSE
Assessment of the Effect of Soil Sample Preparation, Water Content and Excitation Time on Proximal X-ray Fluorescence Sensing
Publisher: MDPI AG
Date: 17-06-2022
DOI: 10.3390/S22124572
Abstract: X-ray fluorescence (XRF) spectroscopy offers a fast and efficient method for analysing soil elemental composition, both in the laboratory and the field. However, the technique is sensitive to spectral interference as well as physical and chemical matrix effects, which can reduce the precision of the measurements. We systematically assessed the XRF technique under different s le preparations, water contents, and excitation times. Four different soil s les were used as blocks in a three-way factorial experiment, with three s le preparations (natural aggregates, ground to ≤2 mm and ≤1 mm), three gravimetric water contents (air-dry, 10% and 20%), and three excitation times (15, 30 and 60 s). The XRF spectra were recorded and gave 540 spectra in all. Elemental peaks for Si, K, Ca, Ti, Fe and Cu were identified for analysis. We used analysis of variance (anova) with post hoc tests to identify significant differences between our factors and used the intensity and area of the elemental peaks as the response. Our results indicate that all of these factors significantly affect the XRF spectrum, but longer excitation times appear to be more defined. In most cases, no significant difference was found between air-dry and 10% water content. Moisture has no apparent effect on coarse s les unless ground to 1 mm. We suggested that the XRF measurements that take 60 s from dry s les or only slightly moist ones might be an optimum option under field conditions.
A simple approach to estimate coastal soil salinity using digital camera images
Publisher: CSIRO Publishing
Date: 2020
DOI: 10.1071/SR20009
Abstract: Soil salinisation is a global problem that hinders the sustainable development of ecosystems and agricultural production. Remote and proximal sensing technologies have been used to effectively evaluate soil salinity over large scales, but research on digital camera images is still lacking. In this study, we propose to relate the pixel brightness of soil surface digital images to the soil salinity information. We photographed the surface of 93 soils in the field at different times and weather conditions, and s led the corresponding soils for laboratory analyses of soil salinity information. Results showed that the pixel digital numbers were related to soil salinity, especially at the intermediate and higher brightness levels. Based on this relationship, we employed random forest (RF) and partial least-squares regression (PLSR) to model soil salt content and ion concentrations, and applied root mean squared error, coefficient of determination and Lin’s concordance correlation coefficient to evaluate the accuracy of models. We found that ions with high concentration were estimated more accurately than ions with low concentrations, and RF models performed overall better than PLSR models. However, the method is only suitable for bare land of coastal soil, and verification is needed for other conditions. In conclusion, a new approach of using digital camera images has good potential to predict and manage soil salinity in the context of precision agriculture with the rapid development of unmanned aerial vehicles.
On the soil information content of visible-near infrared reflectance spectra
Publisher: Wiley
Date: 25-05-2011
Multivariate calibration of hyperspectral γ‐ray energy spectra for proximal soil sensing
Publisher: Wiley
Date: 31-01-2007
Proximal sensing of Cu in soil and lettuce using portable X-ray fluorescence spectrometry
Publisher: Elsevier BV
Date: 03-2016
ParLeS: Software for chemometric analysis of spectroscopic data
Publisher: Elsevier BV
Date: 2008
Continental-scale soil carbon composition and vulnerability modulated by regional environmental controls
Publisher: Springer Science and Business Media LLC
Date: 03-06-2019
Do we really need large spectral libraries for local scale SOC assessment with NIR spectroscopy?
Publisher: Elsevier BV
Date: 2016
SOC fractions and vis--NIR
Publisher: Copernicus GmbH
Date: 16-02-2018
Deep transfer learning of global spectra for local soil carbon monitoring
Publisher: Elsevier BV
Date: 06-2022
Modelling soil water retention and water‐holding capacity with visible–near‐infrared spectra and machine learning
Publisher: Wiley
Date: 03-2022
DOI: 10.1111/EJSS.13220
Abstract: We need measurements of soil water retention (SWR) and available water capacity (AWC) to assess and model soil functions, but methods are time‐consuming and expensive. Our aim here was to investigate the modelling of AWC and SWR with visible–near‐infrared spectra (vis–NIR) and the machine‐learning method cubist . We used soils from 54 locations across Australian agricultural regions, from three depths: 0–15 cm, 15–30 cm and 30–60 cm. The volumetric water content of the s les and their vis–NIR spectra were measured at seven matric potentials from −1 kPa to −1500 kPa. We modelled the following: (i) AWC directly with the average spectra of the s les measured at different water contents, (ii) water contents at field capacity and permanent wilting point and calculated AWC from those estimates, (iii) AWC with spectra of air‐dried soils, and (iv) parameters of the Kosugi and van Genuchten SWR models, then reconstructed the SWR curves to calculate AWC. We compared the estimates with those from a local pedotransfer function (PTF) and an established Australian PTF. The accuracy of the spectroscopic approaches varied but was generally better than the PTFs. The spectroscopic methods are also more practical because they do not require additional soil properties for the modelling. The root‐mean squared‐error (RMSE) of the spectroscopic methods ranged from 0.033 cm 3 cm −3 to 0.059 cm 3 cm −3 . The RMSEs of the PTFs were 0.050 cm 3 cm −3 for the local and 0.077 cm 3 cm −3 for the general PTF. Spectroscopy with machine learning provides a rapid and versatile method for estimating the AWC and SWR characteristics of erse agricultural soils. Soil available water capacity can be estimated with vis‐NIR specta. Parameters of water retention models can be estimated with vis‐NIR spectra. vis‐NIR spectroscopy performed better than pedotransfer functions. The results apply to a erse range of soils.
Laboratory evaluation of a proximal sensing technique for simultaneous measurement of soil clay and water content
Publisher: Elsevier BV
Date: 07-1998
rs‐local data‐mines information from spectral libraries to improve local calibrations
Publisher: Wiley
Date: 11-2017
DOI: 10.1111/EJSS.12490
Simulation of soil carbon dynamics in Australia under a framework that better connects spatially explicit data with Rᴏᴛʜ C
Publisher: Copernicus GmbH
Date: 21-07-2020
DOI: 10.5194/BG-2020-150
Abstract: Abstract. We simulated soil organic carbon (C) dynamics across Australia with the Rothamsted carbon model (Rᴏᴛʜ C) under a framework that connects new spatially-explicit soil measurements and data with the model. Doing so helped to bridge the disconnection that exists between datasets used to inform the model and the processes that it depicts. Under this framework, we compiled continental-scale datasets and pre-processed, standardised and configured them to the required spatial and temporal resolutions. We then calibrated Rᴏᴛʜ C and run simulations to predict the baseline soil organic C stocks and composition in the 0–0.3 m layer at 4,043 sites in cropping, modified grazing, native grazing, and natural environments across Australia. The Rᴏᴛʜ C model uses measured C fractions, the particulate, humus, and resistant organic C (POC, HOC and ROC, respectively) to represent the three main C pools in its structure. The model explained 97–98 % of the variation in measured total organic C in soils under cropping and grazing, and 65 % in soils under natural environments. We optimised the model at each site and experimented with different amounts of C inputs to predict the potential for C accumulation in a 100-year simulation. With an annual increase of 1 Mg C ha−1 in C inputs, the model predicted a potential soil C increase of 13.58 (interquartile range 12.19–15.80), 14.21 (12.38–16.03), and 15.57 (12.07–17.82) Mg C ha−1 under cropping, modified grazing and native grazing, and 3.52 (3.15–4.09) Mg C ha−1 under natural environments. Soils under native grazing were the most potentially vulnerable to C decomposition and loss, while soils under natural environments were the least vulnerable. An empirical assessment of the controls on the C change showed that climate, pH, total N, the C:N ratio, and cropping were the most important controls on POC change. Clay content and climate were dominant controls on HOC change. Consistent and explicit soil organic C simulations improve confidence in the model's predictions, contributing to the development of sustainable soil management under global change.
Curvelet transform to study scale-dependent anisotropic soil spatial variation
Publisher: Elsevier BV
Date: 2014
Soil sensing: A new paradigm for agriculture
Publisher: Elsevier BV
Date: 10-2016
Assessing the response of soil carbon in Australia to changing inputs and climate using a consistent modelling framework
Publisher: Copernicus GmbH
Date: 22-09-2021
Abstract: Abstract. Land use and management practices affect the response of soil organic carbon (C) to global change. Process-based models of soil C are useful tools to simulate C dynamics, but it is important to bridge any disconnect that exists between the data used to inform the models and the processes that they depict. To minimise that disconnect, we developed a consistent modelling framework that integrates new spatially explicit soil measurements and data with the Rothamsted carbon model (Roth C) and simulates the response of soil organic C to future climate change across Australia. We compiled publicly available continental-scale datasets and pre-processed, standardised and configured them to the required spatial and temporal resolutions. We then calibrated Roth C and ran simulations to estimate the baseline soil organic C stocks and composition in the 0–0.3 m layer at 4043 sites in cropping, modified grazing, native grazing and natural environments across Australia. We used data on the C fractions, the particulate, mineral-associated and resistant organic C (POC, MAOC and ROC, respectively) to represent the three main C pools in the Roth C model's structure. The model explained 97 %–98 % of the variation in measured total organic C in soils under cropping and grazing and 65 % in soils under natural environments. We optimised the model at each site and experimented with different amounts of C inputs to simulate the potential for C accumulation under constant climate in a 100-year simulation. With an annual increase of 1 Mg C ha−1 in C inputs, the model simulated a potential soil C increase of 13.58 (interquartile range 12.19–15.80), 14.21 (12.38–16.03) and 15.57 (12.07–17.82) Mg C ha−1 under cropping, modified grazing and native grazing and 3.52 (3.15–4.09) Mg C ha−1 under natural environments. With projected future changes in climate (+1.5, 2 and 5.0 ∘C) over 100 years, the simulations showed that soils under natural environments lost the most C, between 3.1 and 4.5 Mg C ha−1, while soils under native grazing lost the least, between 0.4 and 0.7 Mg C ha−1. Soil under cropping lost between 1 and 2.7 Mg C ha−1, while those under modified grazing showed a slight increase with temperature increases of 1.5 ∘C, but with further increases of 2 and 5 ∘C the median loss of TOC was 0.28 and 3.4 Mg C ha−1, respectively. For the different land uses, the changes in the C fractions varied with changes in climate. An empirical assessment of the controls on the C change showed that climate, pH, total N, the C : N ratio and cropping were the most important controls on POC change. Clay content and climate were dominant controls on MAOC change. Consistent and explicit soil organic C simulations improve confidence in the model's estimations, facilitating the development of sustainable soil management under global change.
Integrating geospatial and multi‐depth laboratory spectral data for mapping soil classes in a geologically complex area in southeastern B razil
Publisher: Wiley
Date: 29-04-2015
DOI: 10.1111/EJSS.12255
Prediction of soil attributes using the Chinese soil spectral library and standardized spectra recorded at field conditions
Publisher: Elsevier BV
Date: 2016
Development of a national VNIR soil-spectral library for soil classification and prediction of organic matter concentrations
Publisher: Springer Science and Business Media LLC
Date: 22-02-2014
Baseline map of soil organic carbon in Tibet and its uncertainty in the 1980s
Publisher: Elsevier BV
Date: 2019
Examining assumptions of soil microbial ecology in the monitoring of ecological restoration
Publisher: Wiley
Date: 23-11-2020
Abstract: 1. Global interest in building healthy soils combined with new DNA sequencing technologies has led to the generation of a vast amount of soil microbial community (SMC) data. 2. SMC analysis is being adopted widely for monitoring ecological restoration trajectories. However, despite the large and growing quantity of soil microbial data, it remains unclear how these data inform and best guide restoration practice. 3. Here, we examine assumptions around SMC as a tool for guiding ecosystem restoration and evaluate the effectiveness of using species inventories of SMC as a benchmark for restoration success. 4. We investigate other approaches of assessing soil health, and conclude that we can significantly enhance the utility of species inventory data for ecological restoration by complementing it with the use of non‐molecular approaches.
Development of On-the-Go Proximal Soil Sensor Systems
Publisher: Springer Netherlands
Date: 2010
Baseline estimates of soil organic carbon by proximal sensing: Comparing design-based, model-assisted and model-based inference
Publisher: Elsevier BV
Date: 03-2016
Diffuse Reflectance Spectroscopy for High-Resolution Soil Sensing
Publisher: Springer Netherlands
Date: 2010
Proximal Soil Nutrient Sensing Using Electrochemical Sensors
Publisher: Springer Netherlands
Date: 2010
Supplementary material to "Soil properties override climate controls on global soil organic carbon stocks"
Publisher: Copernicus GmbH
Date: 11-08-2020
Robust modelling of soil diffuse reflectance spectra by bagging-partial least squares regression
Publisher: SAGE Publications
Date: 02-2007
DOI: 10.1255/JNIRS.694
Abstract: Visible (vis), near infrared (NIR) and mid infrared (mid-IR) diffuse reflectance spectroscopy (DRS) coupled with partial least squares regression (PLSR) are increasingly being used in the agricultural and environmental sciences as an efficient complement to conventional laboratory analysis. The DRS techniques are rapid, relatively cheap and more efficient for obtaining data than conventional analysis, especially when a large number of s les and analyses are required. A single spectrum may be used to predict various physical, chemical and biological soil properties. The robustness of PLSR models and their predictions may be improved by combining the implementation of PLSR with bootstrap aggregation or “bagging”. Bagging aims to reduce the variance of predictions by aggregating a number of models obtained in the course of re-s ling. The aim of this work was to test the implementation of bagging with PLSR (bagging-PLSR) using vis-NIR and mid-IR soil diffuse reflectance spectra to predict soil organic carbon (OC). Bagging-PLSR was shown to: (i) be more robust than PLSR alone, (ii) be less prone to over fitting and improve prediction accuracy and (iii) provide a measure of the uncertainty of the models and their predictions.
Foreword to ‘Soil & Landscape Grid of Australia’
Publisher: CSIRO Publishing
Date: 2015
DOI: 10.1071/SRV53N8_FO
Australian net (1950s–1990) soil organic carbon erosion: implications for CO<sub>2</sub> emission and land–atmosphere modelling
Publisher: Copernicus GmbH
Date: 29-09-2014
Abstract: Abstract. The debate remains unresolved about soil erosion substantially offsetting fossil fuel emissions and acting as an important source or sink of CO2. There is little historical land use and management context to this debate, which is central to Australia's recent past of European settlement, agricultural expansion and agriculturally-induced soil erosion. We use "catchment" scale (∼25 km2) estimates of 137Cs-derived net (1950s–1990) soil redistribution of all processes (wind, water and tillage) to calculate the net soil organic carbon (SOC) redistribution across Australia. We approximate the selective removal of SOC at net eroding locations and SOC enrichment of transported sediment and net depositional locations. We map net (1950s–1990) SOC redistribution across Australia and estimate erosion by all processes to be ∼4 Tg SOC yr−1, which represents a loss of ∼2% of the total carbon stock (0–10 cm) of Australia. Assuming this net SOC loss is mineralised, the flux (∼15 Tg CO2-equivalents yr−1) represents an omitted 12% of CO2-equivalent emissions from all carbon pools in Australia. Although a small source of uncertainty in the Australian carbon budget, the mass flux interacts with energy and water fluxes, and its omission from land surface models likely creates more uncertainty than has been previously recognised.
Special issue ‘Diffuse reflectance spectroscopy in soil science and land resource assessment’
Publisher: Elsevier BV
Date: 08-2010
Proximal soil sensing of soil texture and organic matter with a prototype portable mid‐infrared spectrometer
Publisher: Wiley
Date: 27-05-2015
DOI: 10.1111/EJSS.12265
Spectral fusion by Outer Product Analysis (OPA) to improve predictions of soil organic C
Publisher: Elsevier BV
Date: 02-2019
A two-factor empirical deterministic response surface calibration model for site-specific predictions of lime requirement
Publisher: Springer Science and Business Media LLC
Date: 2000
Mapping iron oxides and the color of Australian soil using visible–near-infrared reflectance spectra
Publisher: American Geophysical Union (AGU)
Date: 15-12-2010
DOI: 10.1029/2009JF001645
Using a digital camera to measure soil organic carbon and iron contents
Publisher: Elsevier BV
Date: 06-2008
Fluctuations in method‐of‐moments variograms caused by clustered sampling and their elimination by declustering and residual maximum likelihood estimation
Publisher: Wiley
Date: 14-03-2013
DOI: 10.1111/EJSS.12029
Simulation of soil carbon dynamics in Australia with {\sc Roth C}
Publisher: Copernicus GmbH
Date: 28-04-2021
DOI: 10.5194/ISMC2021-91
Abstract: & & We simulated soil organic carbon (C) dynamics across Australia with the Rothamsted carbon model ({\\sc Roth C}) by connecting new spatially-explicit soil measurements and data with the model. This helped us to bridge the disconnection that exists between datasets used to inform the model and the processes that it depicts. We compiled publicly available continental-scale datasets and pre-processed, standardised and configured them to the required spatial and temporal resolutions. We then calibrated {\\sc Roth C} and run simulations to estimate the baseline soil organic C stocks and composition in the 0--0.3~m layer at 4,043 sites in cropping, modified grazing, native grazing, and natural environments across Australia. We used data on the C fractions, the particulate, mineral associated, and resistant organic C (POC, MAOC and ROC, respectively) to represent the three main C pools in the {\\sc Roth C} model's structure.& span class=& quot Apple-converted-space& quot & & & /span& The model explained 97--98\\% of the variation in measured total organic C in soils under cropping and grazing, and 65\\% in soils under natural environments. We optimised the model at each site and experimented with different amounts of C inputs to simulate the potential for C accumulation under constant and chainging climate in a 100-year simulation. Soils under native grazing were the most potentially vulnerable to C decomposition and loss, while soils under natural environments were the least vulnerable. An empirical assessment of the controls on the C change showed that climate, pH, total N, the C:N ratio, and cropping were the most important controls on POC change. Clay content and climate were dominant controls on MAOC change. Consistent and explicit soil organic C simulations improve confidence in the model's estimations, contributing to the development of sustainable soil management under global change.& span class=& quot Apple-converted-space& quot & & & /span& & &
Response to anonymous referee #2 comments on “Proximal sensing for soil carbon accounting”
Publisher: Copernicus GmbH
Date: 27-03-2018
Continental soil drivers of ammonium and nitrate in Australia
Publisher: Copernicus GmbH
Date: 13-09-2018
Abstract: Abstract. Soil N is an essential element for plant growth, but its mineral forms are subject to loss from the environment by leaching and gaseous emissions. Despite its importance for the soil-plant system, factors controlling soil mineral N contents over large spatial scales are not well understood. We used NH4+ and NO3- contents (0–30 cm depth) from 469 sites across Australia and determined soil controls on their regional variation. Soil mineral N varied regionally but depended on the different land uses. In the agricultural region of Australia, NH4+ tended to be similar (median 4.0 vs. 3.5 mg N kg−1) and NO3- was significantly enriched (3.0 vs. 1.0 mg N kg−1), compared to the non-agricultural region. The importance of soil controls on mineral N in the agricultural region, identified by the model trees algorithm Cubist, showed that NH4+ was affected by total N, cation exchange capacity (CEC) and pH. In the non-agricultural region, NH4+ was affected not only by CEC and pH, but also by organic C and total P. In each of the regions, NO3- was primarily affected by CEC, with more complex biophysical controls. In both regions, correlations between mineral N and soil C : N : P stoichiometry suggest that more NH4+ was found in P-depleted soil relative to total C and total N. However, our results showed that only in the non-agricultural region was NO3- sensitive to the state of C and its interaction with N and P. The models helped to explain 36 %–68 % of regional variation in mineral N. Although soil controls on high N contents were highly uncertain, we found that region-specific interactions of soil properties control mineral N contents. It is therefore essential to understand how they alter soil mechanisms and N cycling at large scales.
Response to anonymous referee #1 comments on “Proximal sensing for soil carbon accounting”
Publisher: Copernicus GmbH
Date: 27-03-2018
Regional predictions of soil organic carbon content from spectral reflectance measurements
Publisher: Elsevier BV
Date: 11-2009
A multivariate method for matching soil classification systems, with an Australian example
Publisher: CSIRO Publishing
Date: 2020
DOI: 10.1071/SR19320
Abstract: Differences between local systems of soil classification hinder the communication between pedologists from different countries. The FAO–UNESCO Soil Map of the World, as a fruit of world-wide collaboration between innumerable soil scientists, is recognised internationally. Ideally, pedologists should be able to match whole classes in their local systems to those in an international soil classification system. The Australian Soil Classification (ASC) system, created specifically for Australian soil, is widely used in Australia, and Australian pedologists wish to translate the orders they recognise into the FAO soil units when writing for readers elsewhere. We explored the feasibility of matching soil orders in the ASC to units in the FAO legend using a multivariate analysis. Twenty soil properties, variates, of 4927 profiles were estimated from their visible–near infrared reflectance (vis–NIR) spectra. We arranged the profiles in a Euclidean 20-dimensional orthogonal vector space defined by standardised variates. Class centroids were computed in that space, and the Euclidean distances between the centroids of the ASC orders and units in the FAO scheme were also computed. The shortest distance between a centroid of any ASC order and one of units in the FAO classification was treated as a best match. With only one exception the best matches were those that an experienced pedologist might expect. Second and third nearest neighbours in the vector space provided additional insight. We conclude that vis–NIR spectra represent sufficiently well the essential characters of the soil and so spectra could form the basis for the development of a universal soil classification system. In our case, we could assign with confidence the orders of the ASC to the units of the FAO scheme. A similar approach could be applied to link other national classification systems to one or other international systems of soil classification.
Miniaturised visible and near-infrared spectrometers for assessing soil health indicators in mine site rehabilitation
Publisher: Copernicus GmbH
Date: 15-02-2022
Abstract: Abstract. Mining can cause severe disturbances to the soil, which underpins the viability of terrestrial ecosystems. Post-mining rehabilitation relies on measuring soil properties that are critical soil health indicators. Soil visible–near-infrared (vis–NIR) spectroscopy is rapid, relatively accurate and cost-effective for estimating a range of soil properties. Recent advances in infrared detectors and microelectromechanical systems (MEMS) have produced miniaturised, relatively inexpensive spectrometers. Here, we evaluate the spectra from four miniaturised visible and NIR spectrometers, some combinations and a full-range vis–NIR spectrometer to model 29 soil physical, chemical and biological properties used to assess soil health at mine sites. We collected soils from reference undisturbed native vegetation and topsoil stockpiles from seven mines in Western Australia. We evaluated the repeatability of the spectrometers and the accuracy of the spectroscopic models built with seven statistical and machine learning algorithms. The spectra from the visible spectrometer could estimate soil texture (sand, silt, and clay) more accurately than the NIR spectrometers. However, the spectra from the NIR spectrometers produced better estimates of soil chemical and biological properties. By combining the miniaturised visible and NIR spectrometers, we improved the accuracy of their soil property estimates, which were similar to those from the full-range spectrometer. The miniaturised spectrometers and combinations predicted 24 of the 29 soil properties with moderate or greater accuracy (Lin’s concordance correlation, pc ≥ 0.65). The repeatability of the NIR spectrometers was similar to that of the full-range, portable spectrometer. Our results show that the miniaturised NIR spectrometers can produce accurate predictions of soil properties comparable to the (orders of magnitude) more expensive full-range portable system, particularly when combined with a visible range spectrometer. Thus, there is potential to develop rapid, accurate, cost-effective diagnostic capacity to support mine site rehabilitation based on miniaturised spectrometers and deliver significant positive economic and environmental outcomes.
Novel Proximal Sensing for Monitoring Soil Organic C Stocks and Condition
Publisher: American Chemical Society (ACS)
Date: 05-2017
Abstract: Soil information is needed for environmental monitoring to address current concerns over food, water and energy securities, land degradation, and climate change. We developed the Soil Condition ANalysis System (SCANS) to help address these needs. It integrates an automated soil core sensing system (CSS) with statistical analytics and modeling to characterize soil at fine depth resolutions and across landscapes. The CSS's sensors include a γ-ray attenuation densitometer to measure bulk density, digital cameras to image the measured soil, and a visible-near-infrared (vis-NIR) spectrometer to measure iron oxides and clay mineralogy. The spectra are also modeled to estimate total soil organic carbon (C), particulate, humus, and resistant organic C (POC, HOC, and ROC, respectively), clay content, cation exchange capacity (CEC), pH, volumetric water content, available water capacity (AWC), and their uncertainties. Measurements of bulk density and organic C are combined to estimate C stocks. Kalman smoothing is used to derive complete soil property profiles with propagated uncertainties. The SCANS provides rapid, precise, quantitative, and spatially explicit information about the properties of soil profiles with a level of detail that is difficult to obtain with other approaches. The information gained effectively deepens our understanding of soil and calls attention to the central role soil plays in our environment.
Response to referee #1
Publisher: Copernicus GmbH
Date: 25-09-2020
Prediction of soil organic matter using a spatially constrained local partial least squares regression and the C hinese vis–NIR spectral library
Publisher: Wiley
Date: 27-05-2015
DOI: 10.1111/EJSS.12272
Response to referee#2
Publisher: Copernicus GmbH
Date: 25-09-2020
Site-level simulations of measurable soil fractions with Millennial Version 2
Publisher: Copernicus GmbH
Date: 28-04-2021
DOI: 10.5194/ISMC2021-19
Abstract: & & Soil carbon (C) models are used to predict C sequestration responses to climate and land use change. Yet, the soil models embedded in Earth system models typically do not represent processes that reflect our current understanding of soil C cycling, such as microbial decomposition, mineral association, and aggregation. Rather, they rely on conceptual pools with turnover times that are fit to bulk C stocks and/or fluxes. As measurements of soil fractions become increasingly available, soil C models that represent these measurable quantities can be evaluated more accurately. Here we present Version 2 (V2) of the Millennial model, a soil model developed to simulate C pools that can be measured by extraction or fractionation, including particulate organic C, mineral-associated organic C, aggregate C, microbial biomass, and dissolved organic C. Model processes have been updated to reflect the current understanding of mineral-association, temperature sensitivity and reaction kinetics, and different model structures were tested within an open-source framework. We evaluated the ability of Millennial V2 to simulate total soil organic C (SOC), as well as the mineral-associated and particulate fractions, using three soil fractionation data sets spanning a range of climate and geochemistry in Australia (N=495), Europe (N=176), and across the globe (N=730). Millennial V2 (RMSE = 1.98 & #8211 4.76 kg, AIC = 597 & #8211 1755) generally predicts SOC content better than the widely-used Century model (RMSE = 2.23 & #8211 4.8 kg, AIC = 584 & #8211 2271), despite an increase in process complexity and number of parameters. Millennial V2 reproduces between-site variation in SOC across a gradient of plant productivity, and predicts SOC turnover times similar to those of a global meta-analysis. Millennial V2 updates the conceptual Century model pools and processes and represents our current understanding of the roles that microbial activity, mineral association and aggregation play in soil C sequestration.& &
Improved estimates of organic carbon using proximally sensed vis–NIR spectra corrected by piecewise direct standardization
Publisher: Wiley
Date: 27-05-2015
DOI: 10.1111/EJSS.12271
A response-surface calibration model for rapid and versatile site-specific lime-requirement predictions in south-eastern Australia
Publisher: CSIRO Publishing
Date: 2001
DOI: 10.1071/SR99131
Abstract: The development of response-surface calibration models that may be used in conjunction with the lime requirement buffer methods is described. The buffer methods tested were the Woodruff, New Wooruff, Mehlich, and Shoemaker, McLean and Pratt lime-requirement buffers. Model predictions were compared with those obtained from multivariate models and buffer methods calibrated using conventional linear regressions. The multivariate models described lime requirement as a function of a number of soil variables. All of the models were validated against soil : CaCO 3 incubations using a statistical jackknifing procedure for error and bias estimations. The advantages of the derived response-surface models were their improved prediction accuracy and flexibility, with a choice of any target pH CaCl 2 value between 5.5 and 7 without need for in idual calibrations. The response-surface model for the Woodruff buffer method produced the most accurate predictions of lime requirement. The uncertainty of its lime requirement predictions for acid soil in an agricultural field at Kelso, New South Wales, Australia, measured by 95% confidence intervals, was 0.22 Mg/ha. A spatial analysis of lime requirement for the field showed a range of 4–11 Mg/ha. This range provides a reason for site-specific lime applications. Under- and over-applications resulting from a ‘blanket’ 7.13 Mg/ha single-rate application of lime over the field were estimated to range from –4 to 2.9 Mg/ha.
Supplementary material to "Simulation of soil carbon dynamics in Australia under a framework that better connects spatially explicit data with Rᴏᴛʜ C"
Publisher: Copernicus GmbH
Date: 21-07-2020
Selected papers from the Third Global Workshop on Proximal Soil Sensing 2013
Publisher: Wiley
Date: 02-06-2015
DOI: 10.1111/EJSS.12276
Estimating soil fungal abundance and diversity at a macroecological scale with deep learning spectrotransfer functions
Publisher: Copernicus GmbH
Date: 25-03-2022
Abstract: Abstract. Soil fungi play important roles in the functioning of ecosystems, but they are challenging to measure. Using a continental-scale dataset, we developed and evaluated a new method to estimate the relative abundance of the dominant phyla and ersity of fungi in Australian soil. The method relies on the development of spectrotransfer functions with state-of-the-art machine learning and uses publicly available data on soil and environmental proxies for edaphic, climatic, biotic and topographic factors, and visible–near infrared (vis–NIR) wavelengths, to estimate the relative abundances of Ascomycota, Basidiomycota, Glomeromycota, Mortierellomycota and Mucoromycota and community ersity measured with the abundance-based coverage estimator (ACE) index. The algorithms tested were partial least squares regression (PLSR), random forest (RF), Cubist, support vector machines (SVM), Gaussian process regression (GPR), extreme gradient boosting (XGBoost) and one-dimensional convolutional neural networks (1D-CNNs). The spectrotransfer functions were validated with a 10-fold cross-validation (n=577). The 1D-CNNs outperformed the other algorithms and could explain between 45 % and 73 % of fungal relative abundance and ersity. The models were interpretable, and showed that soil nutrients, pH, bulk density, ecosystem water balance (a proxy for aridity) and net primary productivity were important predictors, as were specific vis–NIR wavelengths that correspond to organic functional groups, iron oxide and clay minerals. Estimates of the relative abundance for Mortierellomycota and Mucoromycota produced R2≥0.60, while estimates of the abundance of the Ascomycota and Basidiomycota produced R2 values of 0.5 and 0.58 respectively. The spectrotransfer functions for the Glomeromycota and ersity were the poorest with R2 values of 0.48 and 0.45 respectively. There is no doubt that the method provides estimates that are less accurate than more direct measurements with conventional molecular approaches. However, once the spectrotransfer functions are developed, they can be used with very little cost, and could serve to supplement the more expensive and laborious molecular approaches for a better understanding of soil fungal abundance and ersity under different agronomic and ecological settings.
Updating a national soil classification with spectroscopic predictions and digital soil mapping
Publisher: Elsevier BV
Date: 05-2018
Projecting long-term soil organic dynamics in Australia's rangelands
Publisher: Copernicus GmbH
Date: 28-04-2021
DOI: 10.5194/ISMC2021-92
Abstract: & & Rangelands in Australia are vast and occupy more than 80% of the continental land area. They extend across arid, semi-arid, and the tropical regions with seasonal, variable rainfall in the north. They include erse, relatively undisturbed grasslands, shrublands, woodlands and tropical savanna ecosystems. They represent Australia& #8217 s largest terrestrial carbon sink as they account for almost 70% of Australia's total soil organic carbon stock (Viscarra Rossel et al., 2014), more than all above-ground sources of carbon (native grasses, trees and shrubs) in these regions (Gifford et al., 1992). Here we have developed a novel space-time approach for projecting the long-term C dynamics of rangelands soils using Long Short-Term Memory (LSTM) deep learning neural networks. We further demonstrate how the networks might be interpreted and quantified the influence of explanatory variables on the spatiotemporal dynamics of soil C in these regions. Our results provide an improved ability to accurately model long-term carbon dynamics, which is needed to confidently predict changes in soil C from change in climate or anthropogenic disturbance. The information is critical for improving our understanding of soil C in these regions and for understanding the potential for sequestering C in the rangelands.& &
Spatio-temporal simulation of the field-scale evolution of organic carbon over the landscape
Publisher: Wiley
Date: 09-2003
A new detailed map of total phosphorus stocks in Australian soil
Publisher: Elsevier BV
Date: 2016
DOI: 10.1016/J.SCITOTENV.2015.09.119
Abstract: 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, bio ersity and conservation, inform future s ling 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.
Rapid, quantitative and spatial field measurements of soil pH using an Ion Sensitive Field Effect Transistor
Publisher: Elsevier BV
Date: 03-2004
How does grinding affect the mid-infrared spectra of soil and their multivariate calibrations to texture and organic carbon?
Publisher: CSIRO Publishing
Date: 2015
DOI: 10.1071/SR15019
Abstract: Mid-infrared (mid-IR) diffuse reflectance spectroscopy can be used to effectively analyse soil, but the preparation of soil s les by grinding is time consuming. Soil s les are usually finely ground to a particle size of less than 0.250 mm because the spectrometer’s beam aperture is approximately 1–2 mm in diameter. Larger particles can generate specular reflections and spectra that do not adequately represent the soil s le. Grinding soil to small particle sizes enables the diffuse reflectance of light and more representative s le measurements. Here, we report on research that investigates the effect that grinding to different particle sizes have on soil mid-IR spectra. Our aims were to compare the effect of grinding soil to different particle sizes (2.000 mm, 1.000 mm, 0.500 mm, 0.250 mm and 0.106 mm) on the frequencies of mid-IR spectra, and compare the effect of these particle sizes on the accuracy of spectroscopic calibrations to predict organic carbon, sand, silt and clay contents. Using the Commonwealth Scientific and Industrial Research Organisation’s (CSIRO) National visible–near infrared database, we selected 227 soil s les from the National Soil Archive for our experiments, and designed an experiment whereby each soil s le was ground in triplicate to the different particle sizes. These ground s les were measured using an FT-IR spectrometer with a spectral range of 4000–600 cm–1. Grinding to particle sizes that are ≤2.000 mm reduces subs le variability. Smaller particle sizes produce finer and sharper absorption features, which are related to organic carbon, and clay and sand mineralogies. Generally, better predictions for clay, sand and soil organic carbon contents were achieved using soil that is more finely ground, but there were no statistically significant differences between predictions that use soil ground to 1 mm, 0.5 mm, 0.25 mm. Grinding did not affect predictions of silt content. Recommendations on how much grinding is required for mid-IR analysis should also consider the time, cost and effort needed to prepare the soil s les as well as the purpose of the analysis.
Spatial Modeling of a Soil Fertility Index using Visible–Near‐Infrared Spectra and Terrain Attributes
Publisher: Wiley
Date: 07-2010
Reflectance spectroscopy: a tool for predicting soil properties related to the incidence of Fe chlorosis
Publisher: Instituto Nacional de Investigacion y Tecnologia Agraria y Alimentaria (INIA)
Date: 28-11-2012
Quantitative Soil Spectroscopy
Publisher: Hindawi Limited
Date: 2013
DOI: 10.1155/2013/616578
Site-level simulations of measurable soil fractions with the Millennial V2 soil model
Publisher: Copernicus GmbH
Date: 04-03-2021
DOI: 10.5194/EGUSPHERE-EGU21-9795
Abstract: & & Soil carbon (C) models are used to predict C sequestration responses to climate and land use change. Yet, the soil models embedded in Earth system models typically do not represent processes that reflect our current understanding of soil C cycling, such as microbial decomposition, mineral association, and aggregation. Rather, they rely on conceptual pools with turnover times that are fit to bulk C stocks and/or fluxes. As measurements of soil fractions become increasingly available, it is necessary for soil C models to represent these measurable quantities so that model processes can be evaluated more accurately. Here we present Version 2 (V2) of the Millennial model, a soil model developed in 2018 to simulate C pools that can be measured by extraction or fractionation, including particulate organic C, mineral-associated organic C, aggregate C, microbial biomass, and dissolved organic C. Model processes have been updated to reflect the current understanding of mineral-association, temperature sensitivity and reaction kinetics, and different model structures were tested within an open-source framework. We evaluated the ability of Millennial V2 to simulate total soil organic C (SOC), as well as the mineral-associated and particulate fractions, using three independent data sets of soil fractionation measurements spanning a range of climate and geochemistry in Australia (N=495), Europe (N=176), and across the globe (N=716). Considering RMSE and AIC as indices of model performance, site-level evaluations show that Millennial V2 predicts soil organic carbon content better than the widely-used Century model, despite an increase in process complexity and number of parameters. Millennial V2 also reproduces between-site variation in SOC across gradients of climate, plant productivity, and soil type. By including the additional constraints of measured soil fractions, we can predict site-level mean residence times similar to a global distribution of mean residence times measured using SOC/respiration rate under an assumption of steady state. The Millennial V2 model updates the conceptual Century model pools and processes and represents our current understanding of the roles that microbial activity, mineral association and aggregation play in soil C sequestration.& &
The potential of NIR spectroscopy to predict stability parameters in sewage sludge and derived compost
Publisher: Elsevier BV
Date: 08-2010
Spatial modelling with Euclidean distance fields and machine learning
Publisher: Wiley
Date: 12-07-2018
DOI: 10.1111/EJSS.12687
Automated spectroscopic modelling with optimised convolutional neural networks
Publisher: Springer Science and Business Media LLC
Date: 08-01-2021
DOI: 10.1038/S41598-020-80486-9
Abstract: Convolutional neural networks (CNN) for spectroscopic modelling are currently tuned manually, and the effects of their hyperparameters are not analysed. These can result in sub-optimal models. Here, we propose an approach to tune one-dimensional CNN (1D-CNNs) automatically. It consists of a parametric representation of 1D-CNNs and an optimisation of hyperparameters to maximise a model’s performance. We used a large European soil spectroscopic database to demonstrate our approach for estimating soil organic carbon (SOC) contents. To assess the optimisation, we compared it to random search, and to understand the effects of the hyperparameters, we calculated their importance using functional Analysis of Variance. Compared to random search, the optimisation produced better final results and showed faster convergence. The optimal model produced the most accurate estimates of SOC with $$\\hbox {RMSE} = 9.67 \\pm 0.51$$ RMSE = 9.67 ± 0.51 (s.d.) and $${R}^2 = 0.89 \\pm 0.013$$ R 2 = 0.89 ± 0.013 (s.d.). The hyperparameters associated with model training and architecture critically affected the model’s performance, while those related to the spectral preprocessing had little effect. The optimisation searched through a complex hyperparameter space and returned an optimal 1D-CNN. Our approach simplified the development of 1D-CNNs for spectroscopic modelling by automatically selecting hyperparameters and preprocessing methods. Hyperparameter importance analysis shed light on the tuning process and increased the model’s reliability.
Spatial point-process statistics: concepts and application to the analysis of lead contamination in urban soil
Publisher: Wiley
Date: 2005
DOI: 10.1002/ENV.705
Using data mining to model and interpret soil diffuse reflectance spectra
Publisher: Elsevier BV
Date: 08-2010
Soil organic carbon prediction by hyperspectral remote sensing and field vis-NIR spectroscopy: An Australian case study
Publisher: Elsevier BV
Date: 08-2008
Quantifying soil carbon in temperate peatlands using a mid-IR soil spectral library
Publisher: Copernicus GmbH
Date: 14-06-2021
Abstract: Abstract. Traditional laboratory methods for acquiring soil information remain important for assessing key soil properties, soil functions and ecosystem services over space and time. Infrared spectroscopic modeling can link and massively scale up these methods for many soil characteristics in a cost-effective and timely manner. In Switzerland, only 10 % to 15 % of agricultural soils have been mapped sufficiently to serve spatial decision support systems, presenting an urgent need for rapid quantitative soil characterization. The current Swiss soil spectral library (SSL n = 4374) in the mid-infrared range includes soil s les from the Bio ersity Monitoring Program (BDM), arranged in a regularly spaced grid across Switzerland, and temporally resolved data from the Swiss Soil Monitoring Network (NABO). Given that less than 2 % of the s les in the SSL originate from organic soils, we aimed to develop both an efficient calibration s ling scheme and accurate modeling strategy to estimate the soil carbon (SC) contents of heterogeneous s les between 0 and 2 m depth from 26 locations within two drained peatland regions (School of Agricultural, Forest and Food Sciences (HAFL) data set n = 116). The focus was on minimizing the need for new reference analyses by efficiently mining the spectral information of the SSL. We used partial least square regressions (PLSRs), together with five repetitions of a location-grouped, 10-fold cross-validation, to predict SC ranging from 1 % to 52 % in the local HAFL data set. We compared the validation performance of different calibration schemes involving local models (1), models using the entire SSL combined with local s les (2), commonly referred to as spiking, and subsets of local and SSL s les optimized for the peatland target sites using the res ling local (RS-LOCAL) algorithm (3). Using local and RS-LOCAL calibrations with at least five local s les, we achieved similar validation results for predictions of SC up to 52 % (R2 = 0.93 to 0.97 bias = -0.07 to 1.65 root mean square error (RMSE) = 2.71 % to 3.89 % total carbon ratio of performance to deviation (RPD) = 3.38 to 4.86 and ratio of performance to interquartile range (RPIQ) = 4.93 to 7.09). However, calibrations using RS-LOCAL only required five or 10 local s les for very accurate models (RMSE = 3.16 % and 2.71 % total carbon, respectively), while purely local calibrations required 50 s les for similarly accurate results (RMSE 3 % total carbon). Of the three approaches, the entire SSL spiked with local s les for model calibration led to validations with the lowest performance in terms of R2, bias, RMSE, RPD and RPIQ. Hence, we show that a simple and comprehensible modeling approach, using RS-LOCAL together with a SSL, is an efficient and accurate strategy when using infrared spectroscopy. It decreases field and laboratory work, the bias of SSL spiking approaches and the uncertainty of local models. If adequately mined, the information in the SSL is sufficient to predict SC in new and independent study regions, even if the local soil characteristics are very different from the ones in the SSL. This will help to efficiently scale up the acquisition of quantitative soil information over space and time.
Soil organic carbon dust emission: an omitted global source of atmospheric CO2
Publisher: Wiley
Date: 29-07-2013
DOI: 10.1111/GCB.12305
Abstract: Soil erosion redistributes soil organic carbon (SOC) within terrestrial ecosystems, to the atmosphere and oceans. Dust export is an essential component of the carbon (C) and carbon dioxide (CO(2)) budget because wind erosion contributes to the C cycle by removing selectively SOC from vast areas and transporting C dust quickly offshore augmenting the net loss of C from terrestrial systems. However, the contribution of wind erosion to rates of C release and sequestration is poorly understood. Here, we describe how SOC dust emission is omitted from national C accounting, is an underestimated source of CO(2) and may accelerate SOC decomposition. Similarly, long dust residence times in the unshielded atmospheric environment may considerably increase CO(2) emission. We developed a first approximation to SOC enrichment for a well-established dust emission model and quantified SOC dust emission for Australia (5.83 Tg CO(2)-e yr(-1)) and Australian agricultural soils (0.4 Tg CO(2)-e yr(-1)). These amount to underestimates for CO(2) emissions of ≈10% from combined C pools in Australia (year = 2000), ≈5% from Australian Rangelands and ≈3% of Australian Agricultural Soils by Kyoto Accounting. Northern hemisphere countries with greater dust emission than Australia are also likely to have much larger SOC dust emission. Therefore, omission of SOC dust emission likely represents a considerable underestimate from those nations' C accounts. We suggest that the omission of SOC dust emission from C cycling and C accounting is a significant global source of uncertainty. Tracing the fate of wind-eroded SOC in the dust cycle is therefore essential to quantify the release of CO(2) from SOC dust to the atmosphere and the contribution of SOC deposition to downwind C sinks.
Can the sequestered carbon in agricultural soil be maintained with changes in management, temperature and rainfall? A sensitivity assessment
Publisher: Elsevier BV
Date: 04-2016
Visible and Near Infrared Spectroscopy in Soil Science
Publisher: Elsevier
Date: 2010
Applications of proximal sensing to soil science
Publisher: Wiley
Date: 11-2014
DOI: 10.1111/EJSS.12204
Supplementary material to "Continental drivers of ammonium and nitrate in Australian soil under different land uses"
Publisher: Copernicus GmbH
Date: 24-04-2018
Diffuse Reflectance Spectroscopy as a Tool for Digital Soil Mapping
Publisher: Springer Netherlands
Date: 2008
Visible, near infrared, mid infrared or combined diffuse reflectance spectroscopy for simultaneous assessment of various soil properties
Publisher: Elsevier BV
Date: 03-2006
Proximal spectral sensing in pedological assessments: vis–NIR spectra for soil classification based on weathering and pedogenesis
Publisher: Elsevier BV
Date: 05-2018
The relevant range of scales for multi-scale contextual spatial modelling
Publisher: Springer Science and Business Media LLC
Date: 15-10-2019
DOI: 10.1038/S41598-019-51395-3
Abstract: Spatial autocorrelation in the residuals of spatial environmental models can be due to missing covariate information. In many cases, this spatial autocorrelation can be accounted for by using covariates from multiple scales. Here, we propose a data-driven, objective and systematic method for deriving the relevant range of scales, with distinct upper and lower scale limits, for spatial modelling with machine learning and evaluated its effect on modelling accuracy. We also tested an approach that uses the variogram to see whether such an effective scale space can be approximated a priori and at smaller computational cost. Results showed that modelling with an effective scale space can improve spatial modelling with machine learning and that there is a strong correlation between properties of the variogram and the relevant range of scales. Hence, the variogram of a soil property can be used for a priori approximations of the effective scale space for contextual spatial modelling and is therefore an important analytical tool not only in geostatistics, but also for analyzing structural dependencies in contextual spatial modelling.
Separating Scale-Specific Spatial Variability in Two Dimensions using Bi-Dimensional Empirical Mode Decomposition
Publisher: Wiley
Date: 11-10-2013
Multi-scale digital soil mapping with deep learning
Publisher: Springer Science and Business Media LLC
Date: 15-10-2018
DOI: 10.1038/S41598-018-33516-6
Abstract: We compared different methods of multi-scale terrain feature construction and their relative effectiveness for digital soil mapping with a Deep Learning algorithm. The most common approach for multi-scale feature construction in DSM is to filter terrain attributes based on different neighborhood sizes, however results can be difficult to interpret because the approach is affected by outliers. Alternatively, one can derive the terrain attributes on decomposed elevation data, but the resulting maps can have artefacts rendering the approach undesirable. Here, we introduce ‘mixed scaling’ a new method that overcomes these issues and preserves the landscape features that are identifiable at different scales. The new method also extends the Gaussian pyramid by introducing additional intermediate scales. This minimizes the risk that the scales that are important for soil formation are not available in the model. In our extended implementation of the Gaussian pyramid, we tested four intermediate scales between any two consecutive octaves of the Gaussian pyramid and modelled the data with Deep Learning and Random Forests. We performed the experiments using three different datasets and show that mixed scaling with the extended Gaussian pyramid produced the best performing set of covariates and that modelling with Deep Learning produced the most accurate predictions, which on average were 4–7% more accurate compared to modelling with Random Forests.
Mapping gamma radiation and its uncertainty from weathering products in a Tasmanian landscape with a proximal sensor and random forest kriging
Publisher: Wiley
Date: 09-10-2014
DOI: 10.1002/ESP.3476
Determining the composition of mineral-organic mixes using UV–vis–NIR diffuse reflectance spectroscopy
Publisher: Elsevier BV
Date: 12-2006
Current and future assessments of soil erosion by water on the Tibetan Plateau based on RUSLE and CMIP5 climate models
Publisher: Elsevier BV
Date: 09-2018
DOI: 10.1016/J.SCITOTENV.2018.04.146
Abstract: 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
Environmental controls of soil fungal abundance and diversity in Australia's diverse ecosystems
Publisher: Elsevier BV
Date: 07-2022
Soil classification based on spectral and environmental variables
Publisher: Copernicus GmbH
Date: 20-11-2019
DOI: 10.5194/SOIL-2019-77
Abstract: Abstract. Given the large volume of soil data, it is now possible to obtain a soil classification using spectral, climate and terrain attributes. The idea was to develop a soil series system, which intends to discriminate soil types according to several variables. This new system was called Soil-Environmental Classification (SEC). The spectra data was applied to obtain information about the soil and climate and terrain variables to simulate the pedologist knowledge in soil-environment interactions. The most appropriate numbers of classes were achieved by the lowest value of AIC applying the clusters analysis, which was defined with 8 classes. A relationship between the SEC and WRB-FAO classes was found. The SEC facilitated the identification of groups with similar characteristics using not only soil but environmental variables for the distinction of the classes. Finally, the conceptual characteristics of the 8 SEC were described. The development of SEC conducted to incorporate applicable soil data for agricultural management, with less interference of personal/subjective/empirical knowledge (such as traditional taxonomic systems), and more reliable on automation measurements by sensors.
Environmental sensor networks for vegetation, animal and soil sciences
Publisher: Elsevier BV
Date: 10-2010
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