Wavelet analysis of soil variation at nanometre‐ to micrometre‐scales: an example of organic carbon content in a micro‐aggregate

This paper demonstrates the potential of wavelet analysis to investigate fine‐scale spatial variation in soil without statistical assumptions that are generally implausible. We analysed the optical densities of different forms of carbon which were measured at intervals of 50 nm along a 16‐µm transect on a soil micro‐aggregate using near‐edge X‐ray fine‐structure spectroscopy (NEXAFS). We found different patterns of scale‐dependent variation between the carbon forms, which could be represented by pair‐wise wavelet correlations at the different scales, and by principal components analysis of all the correlations at each scale. These results represent only one small soil micro‐aggregate and are not presented as general findings about soil carbon, but they do indicate that fine‐scale variation of soil carbon can be complex in ways that the wavelet analysis can accommodate but alternative spatial statistics such as variograms cannot. Among the patterns of variation that the analysis could identify were scale‐dependent correlations of the different forms of carbon. In some cases, positive correlations were found at coarser scales and negative at the finest scales, suggesting a multi‐scale pattern in which contrasting forms of carbon are deposited in common clumps but at finer scales either one or the other form dominates. Aromatic and carboxylic carbon varied jointly in this way. Other forms of carbon, such as carboxylic and aliphatic carbon, were strongly correlated at the finest scales but not the coarser scales. We found evidence for changes in the variance and correlation of forms of carbon along the transect, indicating that the spatial distribution of carbon at these fine scales may be very complex in ways that are inconsistent with the assumptions of geostatistics. This quantitative analysis of the spatial patterns of different soil components at micro‐scales offers a basis for formulating and testing specific hypotheses on replicated samples.     

Biogeochemical processes and arsenic enrichment around rice roots in paddy soil: results from micro‐focused X‐ray spectroscopy

The spatial distribution and speciation of iron (Fe), manganese (Mn) and arsenic (As) around rice roots grown in an As‐affected paddy field in Bangladesh were investigated on soil sampled after rice harvest. Synchrotron micro‐X‐ray fluorescence spectrometry on soil thin sections revealed that roots influence soil Fe, Mn and As distribution up to 1 mm away from the root–soil interface. Around thick roots (diameter around 500 µm), Mn was concentrated in discrete enrichments close to the root surface without associated As, whereas concentric Fe accumulations formed farther away and were closely correlated with As accumulations. Near thin roots (diameter < 100 µm), in contrast, a pronounced enrichment of Fe and As next to the root surface and a lack of Mn enrichments was observed. X‐ray absorption fine structure spectroscopy suggested that (i) accumulated Fe was mainly contained in a two‐line ferrihydrite‐like phase, (ii) associated As was mostly As(V) and (iii) Mn enrichments consisted of Mn(III/IV) oxyhydroxides. The distinct enrichment patterns can be related to the extent of O₂ release from primary and lateral rice roots and the thermodynamics and kinetics of Fe, Mn and As redox transformations. Our results suggest that in addition to Fe(III) plaque at the root surface, element accumulation and speciation in the surrounding rhizosphere soil must be taken into account when addressing the transfer of nutrients or contaminants into rice roots.     

Classification of soil types using geographic object-based image analysis and random forests

There is increasing interest in developing automatic procedures to segment landscapes into soil spatial entities that replace conventional, expensive manual procedures for delineating and classifying soils. Geographic object-based image analysis (GEOBIA) partitions remote sensing imagery or digital elevation models into homogeneous image objects based on image segmentation. We used an object-based methodology for the detailed delineation and classification of soil types using digital maps of topography and vegetation as soil covariates, based on the Random Forests (RF) classifier. We compared the object-based method's results with those of a pixel-based classification using the same classifier. We used 18 digital elevation model derivatives and 5 remote sensing indices that were related to vegetation cover and soil. Using 171 soil profiles with their associated environmental variable values, the RF method was used to identify the most important soil type predictors for use in the segmentation process. A stack of raster-geodatasets corresponding to the selected predictors was segmented using a multi-resolution segmentation algorithm, which resulted in homogeneous objects related to soil types. These objects were further classified as soil types using the same method, RF. We also conducted a pixel-based classification using the same classifier and soil profiles, and the resulting maps were assessed in terms of their accuracy using 30% of the soil profiles for validation. We found that GEOBIA was an effective method for soil type mapping, and was superior to the pixel-based approach. The optimized object-based soil map had an overall accuracy of 58%, which was 10% higher than that of the optimized pixel-based map.     

Porosity measurements and form analysis of mineral grains in thin sections from oil- gas reservoir rocks using quantimet 720 and besi

Submicroscopic and image analyzer techniques have been applied for the measurement of the porosities of oil—gas—water reservoir rocks from thin sections. Backscattered electron scanning images (BESI) were made predominantly at low magnifications of × 30 and × 60. Porosity data from linear traverses were obtained and differences in porosities between two sandstone reservoirs and a carbonate reservoir were determined. A large number of the measured pores contained clayey material. The surface occupied by this material was subtracted from the apparent porosity measurements with the image editor of the Quantimet 720 to give the real porosity.

A sample from one of two sandstone reservoirs contained oil and could not be hardened using the common procedure for impregnation of the sample by a polyester resin. Gamma radiation was applied and hardening occurred in one weekend after 5 Mrad (50 kGy) had been absorbed. Hardening of this sample gave no problems, if the oil had been removed by monostyrene before a start was made with the impregnation process; such a sample was used for the present porosity study.

Form analysis was done of individual mineral grains using BESI and Quantimet. The grains were represented in a number of shape classes of a diagram used for shape classification. Form-separation of individual minerals was also possible.     

Harvesting European knowledge on soil functions and land management using multi‐criteria decision analysis

Soil and its ecosystem functions play a societal role in securing sustainable food production while safeguarding natural resources. A functional land management framework has been proposed to optimize the agro‐environmental outputs from the land and specifically the supply and demand of soil functions such as (a) primary productivity, (b) carbon sequestration, (c) water purification and regulation, (d) biodiversity and (e) nutrient cycling, for which soil knowledge is essential. From the outset, the LANDMARK multi‐actor research project integrates harvested knowledge from local, national and European stakeholders to develop such guidelines, creating a sense of ownership, trust and reciprocity of the outcomes. About 470 stakeholders from five European countries participated in 32 structured workshops covering multiple land uses in six climatic zones. The harmonized results include stakeholders’ priorities and concerns, perceptions on soil quality and functions, implementation of tools, management techniques, indicators and monitoring, activities and policies, knowledge gaps and ideas. Multi‐criteria decision analysis was used for data analysis. Two qualitative models were developed using Decision EXpert methodology to evaluate “knowledge” and “needs”. Soil quality perceptions differed across workshops, depending on the stakeholder level and regionally established terminologies. Stakeholders had good inherent knowledge about soil functioning, but several gaps were identified. In terms of critical requirements, stakeholders defined high technical, activity and policy needs in (a) financial incentives, (b) credible information on improving more sustainable management practices, (c) locally relevant advice, (d) farmers’ discussion groups, (e) training programmes, (f) funding for applied research and monitoring, and (g) strengthening soil science in education.     

Effects of iron-based amendments on soil structure: a lab experiment using soil micromorphology and image analysis of pores

Purpose

Recent trends in soil green and sustainable remediation require an increased attention on environmental effects. The physical consequences of remediation practices on soil structure are very rarely investigated.

Material and methods

A laboratory experiment was carried out by adding iron grit to a sand (S), a silt loam (L), and a clay (C) soil subjected to several wetting-drying cycles. The physical effects of the treatment on soil pore system were identified and quantified combining physical measurements on repacked samples with image analysis of pores on resin-impregnated soil blocks and micromorphological analysis on thin sections.

Results and discussion

A negligible reduction of total porosity (P) resulted in S, and a slight increase was observed in the L and C soils. However, an important impact on soil structure was identified in pore size range >10 μm for the L and C soils, with the formation of new pores related to the differential shrink-swell behavior between soil matrix and added iron grains. Different plasticity of these soils also played a role in planar pore formation.

Conclusions

Effects of the addition of iron grit on soil pore system are strongly dependent on soil physical properties. The performed experiment showed that iron-based amendments can improve soil structure in low-plastic shrink-swell soil increasing porosity in the range of transmission pores (50–500 μm). This study showed the high potential of soil micromorphology and pore image analysis in order to evaluate the environmental impact of soil remediation practices.     

Quantification of the effects of soil compaction on water flow using dye tracers and image analysis

Abstract. Soil compaction has long been considered to be a problem in arable land, primarily because it causes damage to soil structure, which can lead to serious reduction in crop yields. However, few studies have sought to investigate the effects of soil compaction on the water transport regime of modified soil pore systems. We attempted to quantify the effects of soil compaction on the initiation of preferential flow by using dye tracers and image analysis. A laboratory methodology involving rainfall simulation enabled us to quantitatively evaluate differences in the mechanisms of water flow between two soil types at several degrees of compaction. The results suggested significant differences in the types of water flow pathways between clay loams and sandy loams at different extents of compaction. In the sandy loam, it was concluded that a high degree of compaction led to an increased likelihood of preferential flow, whereas a more uniform movement of soil water occurred at less compaction. By contrast, preferential routing of soil water was recorded in the clay loam, except at the highest measured compaction. The results indicate that the visual techniques of dye tracing and image analysis could enable improved understanding of flow pathways of soil water associated with soil compaction.     

A systems analysis of soil and forest degradation in a mid‐hill watershed of Nepal using a bio‐economic model

Forest degradation, manifested through decline in forest cover, and the resulting soil erosion and organic carbon losses, is a serious problem caused by a complex coupling of bio‐physical, socio‐economic and technological factors in the Himalayan watersheds. Greater understanding of the linkages between these factors requires a systems approach. We have proposed such an approach using a bio‐economic model to explore the system behaviour of forest degradation, soil erosion, and soil C losses in the forest areas. The outcome of the model simulation over a 20‐year period indicates that soil erosion and C loss rates may increase more than four‐fold by the year 2020 under the existing socio‐economic and biophysical regime (the base scenario). Reductions in the population growth rate, introduction of improved agricultural technology and increase in the prices of major agricultural crops can help slow down the rates of forest decline, soil erosion and C loss or even stabilize or reverse them. The results suggest that economic incentives may be highly effective in the reduction of soil loss, as well as C release to the atmosphere. Copyright © 2005 John Wiley & Sons, Ltd.     

Enhancing the productivity of high‐magnesium soil and water resources in Central Asia through the application of phosphogypsum

Recent evidences from some irrigated areas worldwide, such as Central Asia, suggest that water used for irrigation contains magnesium (Mg²⁺) at levels higher than calcium (Ca²⁺). Excess levels of Mg²⁺ in irrigation water and/or in soil, in combination with sodium (Na⁺) or alone, result in soil degradation because of Mg²⁺ effects on the soil's physical properties. More than 30 per cent of irrigated lands in Southern Kazakhstan having excess levels of Mg²⁺ are characterized by low infiltration rates and hydraulic conductivities. The consequence has been a gradual decline in the yield of cotton (Gossypium hirsutum L.), which is commonly grown in the region. These soils require adequate quantities of Ca²⁺ to mitigate the effects of excess Mg²⁺. As a source of Ca²⁺, phosphogypsum—a byproduct of the phosphorous fertilizer industry—is available in some parts of Central Asia. In participation with the local farming community, we carried out a 4‐year field experiment in Southern Kazakhstan to evaluate the effects of soil application of phosphogypsum—0, 4·5, and 8·0 metric ton per hectare (t ha⁻¹)—on chemical changes in a soil containing excess levels of Mg²⁺, and on cotton yield and economics. The canal water had Mg²⁺ to Ca²⁺ ratio ranging from 1·30 to 1·66 during irrigation period. The application of phosphogypsum increased Ca²⁺ concentration in the soil and triggered the replacement of excess Mg²⁺ from the cation exchange complex. After harvesting the first crop, there was 18 per cent decrease in exchangeable magnesium percentage (EMP) of the surface 0·2 m soil over the pre‐experiment EMP level in the plots where phosphogypsum was applied at 4·5 t ha⁻¹, and a 31 per cent decrease in EMP in plots treated with phosphogypsum at 8 t ha⁻¹. Additional beneficial effect of the amendment was an increase in the soil phosphorus content. The 4‐year average cotton yields were 2·6 t ha⁻¹ with 8 t ha⁻¹ phosphogypsum, 2·4 t ha⁻¹ with 4·5 t ha⁻¹ phosphogypsum, and 1·4 t ha⁻¹ with the control. Since the amendment was applied once at the beginning, exchangeable Mg²⁺ levels tended to increase 4 years after its application, particularly in the treatment with 4·5 t ha⁻¹ phosphogypsum. Thus, there would be a need for phosphogypsum application to such soils after every 4–5 years to optimize the ionic balance and sustain higher levels of cotton production. The economic benefits from the phosphogypsum treatments were almost twice those from the control. Copyright © 2007 John Wiley & Sons, Ltd.     

Scaling analysis of soil water storage with missing measurements using the second‐generation continuous wavelet transform

Information on the spatial variability of soil water storage (SWS) at different scales is important for understanding various hydrological, ecological and biogeochemical processes in the landscape. However, various obstructions such as roads or water bodies may result in missing measurements and create an irregular spatial series. The wavelet transform can quantify spatial variability at different scales and locations but is restricted to regular measurements. The objective of this study was to analyse the spatial variability of SWS with missing measurements using the second‐generation continuous wavelet transform (SGCWT). Soil water content (converted to SWS by multiplying with depth) was measured with a neutron probe and time‐domain reflectrometry along a transect of 128 points. Because there were missing measurements, I used SGCWT to partition the total variation into different scales and locations. Whilst there were some small‐scale variations (< 20 m) along the transect, the medium scale variations (20–70 m with an average of about 30–45 m) were mainly concentrated within the depressions along the transect. The strongest variations were observed at around 90–110 m scale, representing the variations resulting from alternating knolls and depressions. Similar spatial patterns at different scales were observed during different seasons, indicating temporal stability in the spatial pattern of SWS. Among the controlling factors, the wavelet spectra of relative elevation (RE) and organic carbon (OC) were very similar to that of SWS. The wavelet covariance was also large between SWS and RE and OC at all seasons. As the OC reflects the long‐term history of water availability and might be controlled by topographic setting or elevation, it can be concluded that elevation is an important controlling factor of SWS irrespective of seasons in this type of landscape. The SGCWT provides a new way of analysing the spatial variability of regularly measured soil properties or those with missing measurements.     

Residence time of fine‐root carbon using radiocarbon measurements of samples collected from a soil archive

Fine roots from the soil archive of the Swedish National Forest Soil Inventory, collected in 1964, 1972, 1985, and 1998, were analyzed for ¹⁴C contents. Two different methods of estimating residence time were compared. Residence time of root C was estimated to be 8 y using a steady state 1‐pool model.     

Development of a technique for the measurement of root geometry in the soil using resin-impregnated blocks and image analysis

A method is described that permits the measurement of roots in situ in the soil using computerized image analysis to process images obtained from planar faces cut from a resin-fixed pot. Soil sample pots are initially impregnated with rapid-curing epoxy resin. Blocks are then cut from the impregnated pot with a diamond saw to expose a planar face. Modification of the present resin-impregnation procedure is necessary as impregnation results were mostly unsatisfactory. Roots are contrasted from the soil matrix by enhancing autofluorescence using longwave ultraviolet light and appropriate filters. After image capture and segmentation to show regions of root material, measurements of volume fraction of roots, volumetric surface area, horizontal root length and horizontal root separation, were made with a larger resolution with soil depth than previously.     

Prediction of soil organic matter using a spatially constrained local partial least squares regression and the Chinese vis–NIR spectral library

We need to determine the best use of soil vis–NIR spectral libraries that are being developed at regional, national and global scales to predict soil properties from new spectral readings. To reduce the complexity of a calibration dataset derived from the Chinese vis–NIR soil spectral library (CSSL), we tested a local regression method that combined geographical sub‐setting with a local partial least squares regression (local‐PLSR) that uses a limited number of similar vis–NIR spectra (k‐nearest neighbours). The central idea of the local regression, and of other local statistical approaches, is to derive a local prediction model by identifying samples in the calibration dataset that are similar, in spectral variable space, to the samples used for prediction. Here, to derive our local regressions we used Euclidean distance in spectral space between the calibration dataset and prediction samples, and we also used soil geographical zoning to account for similarities in soil‐forming conditions. We tested this approach with the CSSL, which comprised 2732 soil samples collected from 20 provinces in the People's Republic of China to predict soil organic matter (SOM). Results showed that the prediction accuracy of our spatially constrained local‐PLSR method (R² = 0.74, RPIQ = 2.6) was better than that from local‐PLSR (R² = 0.69, RPIQ = 2.3) and PLSR alone (R² = 0.50, RPIQ = 1.5). The coupling of a local‐PLSR regression with soil geographical zoning can improve the accuracy of local SOM predictions using large, complex soil spectral libraries. The approach might be embedded into vis–NIR sensors for laboratory analysis or field estimation.     

Spatial variation of ammonia volatilization from soil and its scale‐dependent correlation with soil properties

Quantitative predictions of ammonia volatilization from soil are useful to environmental managers and policy makers and empirical models have been used with some success. Spatial analysis of the soil properties and their relationship to the ammonia volatilization process is important as predictions will be required at disparate scales from the field to the catchment and beyond. These relationships are known to change across scales and this may affect the performance of an empirical model. This study is concerned with the variation of ammonia volatilization and some controlling soil properties: bulk density, volumetric water content, pH, CEC, soil pH buffer power, and urease activity, over distances of 2, 50, 500, and >2000 m. We sampled a 16 km × 16 km region in eastern England and analyzed the results by a nested analysis of (co)variance, from which variance components and correlations for each scale were obtained. The overall correlations between ammonia volatilization and the soil properties were generally weak: –0.09 for bulk density, 0.04 for volumetric water content, –0.22 for CEC, –0.08 for urease activity, –0.22 for pH and 0.18 for the soil pH buffer power. Variation in ammonia volatilization was scale‐dependent, with substantial variance components at the 2‐ and 500‐m scales. The results from the analysis of covariance show that the relationships between ammonia volatilization and soil properties are complex. At the >2000 m scale, ammonia volatilization was strongly correlated with pH (–0.82) and CEC (–0.55), which is probably the result of differences in parent material. We also observed weaker correlations at the 500‐m scale with bulk density (–0.61), volumetric water content (0.48), urease activity (–0.42), pH (–0.55) and soil pH buffer power (0.38). Nested analysis showed that overall correlations may mask relationships at scales of interest and the effect of soil variables on these soil processes is scale‐dependent.     

Field level digital mapping of soil mineralogy using proximal and remote‐sensed data

Primary (e.g., quartz) and secondary (clay) minerals are key factors determining the physical and chemical characteristics of soil. Understanding spatial distribution of minerals at the field scale would, therefore, be of potential benefit for soil management. However, current analysis requires time‐consuming laboratory procedures and computational quantification analysis (e.g., SIROQUANT). Furthermore, mineral composition (e.g., quartz, kaolinite, illite and expandable clay minerals) must sum to 100. We aimed to add value to laboratory data by developing multiple linear regression (MLR) relationships between mineralogy and ancillary data such as digital numbers (DNs) (i.e., Red [R], Green [G] and Blue [B]) acquired from remotely sensed air‐photographs and soil apparent electrical conductivity (EC_a – mS/m) measured from proximal sensing electromagnetic (EM) instruments (i.e., EM38 and EM31). To account for composition, we compare results from the MLR approach with those from additive log‐ratio (ALR) transformation of mineralogy prior to MLR modelling. This approach together with various ancillary data and trend surface parameters (i.e., scaled Easting and Northing) has greater precision and less bias of prediction than the MLR approach using untransformed data. Our approach also enables predictions to sum to 100. We conclude that the most useful ancillary data to predict the abundance of quartz, kaolinite and illite are B DNs and EM31, while expandable clays are best predicted with R DNs, EM38 and scaled Northing. The use of ancillary data to map mineralogical components combined with ALR‐MLR is an effective approach, with resulting maps providing insights into soil and water management issues consistent with farmer experience.     

Advances in characterization of soil clay mineralogy using X‐ray diffraction: from decomposition to profile fitting

Structural characterization of soil clay minerals often remains limited despite their key influence on soil properties. In soils, complex clay parageneses result from the coexistence of clay species with contrasting particle sizes and crystal chemistry and from the profusion of mixed layers with variable compositions. The present study aimed to characterize the mineralogy and crystal chemistry of the <2 μm fraction along a profile typical of soils from Western Europe and North America (Neo Luvisol). X‐ray diffraction (XRD) patterns were interpreted using: (i) the combination of XRD pattern decomposition and indirect identification from peak positions commonly applied in soil science; and (ii) the multi‐specimen method. This latter approach implies direct XRD profile fitting and has recently led to significant improvements in the structural characterization of clay minerals in diagenetic and hydrothermal environments. In contrast to the usual approach, the multi‐specimen method allowed the complete structural characterization of complex clay parageneses encountered in soils together with the quantitative analysis of their mineralogy. Throughout the profile, the clay paragenesis of the studied Neo Luvisol systematically includes discrete smectite, illite and kaolinite in addition to randomly interstratified illite‐smectite and chlorite‐smectite. Structural characteristics of the different clay minerals, including the composition of mixed layers, did not vary significantly with depth and are thus indicative of the parent material. The relative proportion of the <2 μm fraction increased with increasing depth simultaneously with smectite relative proportion. These results are consistent with the leaching process described for Luvisols in the literature.