Spatial Variability Of Soil Texture Research Articles

Electromagnetic sensing and infiltration measurements to evaluate turfgrass salinity and reclamation

Scarce freshwater resources in arid and semiarid regions means that recreational landscapes should use recycled or low-quality waters for irrigation, increasing the risk of salinity and infiltration problems. We map salinity distribution within turf fields using electromagnetic sensing, evaluate need for leaching and evaluate post leaching results for subsequent management decisions. Electromagnetic measurements were made with two EM38 instruments positioned vertically and horizontally in order to determine salinity distribution. Sensor readings were coupled to GPS data to create spatial salinity maps. Next, optimal calibration point coordinates were determined via Electrical Conductivity Sampling Assessment and Prediction (ESAP) software. Soil samples from 0–15 and 15–30 cm depths were used for each calibration point. Laboratory soil saturation percentage, moisture content, electrical conductivity (ECe) and pHe of saturation extracts were determined for calibration to convert resistivity measurements to ECe. Next, ECe maps were created using ESAP software. Leaching for reclamation was performed by means of sprinkling. Treated municipal wastewater was utilized both for irrigation and for reclamation leaching. Low water content and high spatial variability of soil texture adversely affected the accuracy of the readings. Pre and post leaching surveys indicate that in one fairway there was only a 43% and 58% decrease in soil salinity at 0–15 and 15–30 cm depths, respectively which is very low relative to expected results considering the amount of water applied. This relatively low reduction in salinity and the lack of runoff during irrigation combined with infiltration measurements suggests that aeration techniques for healthier grasses led to water bypassing small pores thus limiting leaching efficiency. In this instance practices to improve infiltration lead paradoxically to less salinity reclamation than expected.

Spatial Variability of Soil and Plant Water Status and Their Cascading Effects on Grapevine Physiology Are Linked to Berry and Wine Chemistry.

The relationships between differences in plant water status, induced by spatial variability in soil texture, and the changes in berry and wine composition were investigated in an irrigated Cabernet Sauvignon (Vitis vinefera L.) vineyard for 2 years. A stratified and an equidistant grid were overlaid on the vineyard to characterize the soil texture by proximal sensing, soil sampling, and grapevine physiological and berry chemical development. Based on the mid-day stem water potential (Ψstem) integrals, the vineyard was divided into two functional homogenous zones: Zone 1 with higher water stress and Zone 2 with lower water. Zone 1 consistently had lower Ψstem, net carbon assimilation, and stomatal conductance in both years. Berry weight and titratable acidity were lower in Zone 1 at harvest. Zone 2 reached 26 and 24°Bx total soluble solids (TSS) at harvest in Years 1 and 2, respectively, with higher TSS values of 30 and 27°Bx in Zone 1. Ravaz index did not vary spatially. Fruits were harvested differentially in both years and vinified separately from the two zones. In Year 1, all berry skin anthocyanin derivatives, tri-, di- hydroxylated, and total anthocyanins concentrations were higher in Zone 2. However, in Year 2, only malvidin, tri-hydroxylated, and total anthocyanins were higher in Zone 1. There were no differences in wine flavonoids in Year 2 when harvest commenced earlier. In both years, Ψstem, berry weight, and TSS were directly related to soil bulk electrical conductivity (EC). Our results indicated vineyard variability stemmed from soil texture that affected long-term plant water status which does not affect spatial variability of Ravaz Index. In conclusion, our work provides fundamental knowledge about the applicability of soil bulk EC sensing in the vineyards, and its potential directional utilization by connecting proximal soil sensing to spatial distribution of whole-plant physiological performance together with berry and wine chemistry.

Enhanced hydrologic simulation may not improve downscaled soil moisture patterns without improved soil characterization

AbstractFine‐resolution soil moisture maps (10–30 m grid cells) are useful for many applications, including agricultural production and off‐road vehicle mobility. Fine‐resolution maps of soil moisture can be produced by downscaling coarse‐resolution soil moisture data from remote sensing. Soil moisture downscaling has been conducted by modeling key hydrologic processes in the soil, but current downscaling methods neglect two key aspects of soil hydrology: surface runoff and residual water content. The objective of this study is to understand how these hydrologic considerations affect spatial patterns of soil moisture and whether their inclusion improves downscaling performance. The Equilibrium Moisture from Topography, Vegetation, and Soil (EMT+VS) downscaling model is used to address these questions. Surface runoff is introduced to the model by implementing an infiltration capacity based on the saturated hydraulic conductivity. Residual water content is included by assuming that all hydrologic processes cease when soil moisture is at or below the residual water content. The pre‐existing EMT+VS model is compared with the new models using data from northeastern Colorado. The results show that both model additions introduce more complex dependence on the spatial variability of soil texture, which is difficult to estimate accurately. Including surface runoff provides only a small improvement in model performance, and residual water content shows almost no effect on the performance in this case. Thus, the simplifying assumptions of the pre‐existing model provide an appropriate level of complexity, given the uncertainties of the spatial input data.

Analysis of the Spatial Variability of Soil Texture in a Tropical Highland: The Case of the Jema Watershed, Northwestern Highlands of Ethiopia.

This study sought to analyze the degree of spatial association of soil texture with agro-climatic zones and slope classes on the farmlands of the Jema watershed, in the Northwestern Highlands of Ethiopia. The agro-climatic zones (elevation zones) determine the micro-climate and biota of the study area. Thirty six soil composite samples for texture (the proportion of clay, silt and sand) analysis from four agro-climatic (elevation) zones and seven slope classes were collected. One-Way-ANOVA was employed to compute the mean variability of texture among the identified terrain classes, and linear regression was used to analyze the degree of association between texture and the terrain attributes. The measured values of sand, silt and clay in the watershed ranged from 11.4 to 43.4, 6.0 to 34.8, and 21.8 to 77.8, respectively. The One-Way-ANOVA indicated a significant (p < 0.05) soil texture variation in both slope and agro-climatic zone classes. Heavy clay, clay and clay loam were identified as the major texture classes in the lower, middle and upper parts of the watershed, respectively. The regression analysis showed that texture was more influenced by the difference in the elevation values than in slope values in the watershed. The standardized beta coefficients of slope and elevation for clay particles were 0.499 and 0.767, respectively. For sand, the regression coefficients for slope and agro-climatic zone were 0.485 and 0.812, respectively. This implies that an interactive effect of micro-climate and biota governed by elevation influenced the spatial distribution of soil texture more than slope.

Reliably Mapping Clay Content Coregionalized with Electrical Conductivity

Core Ideas Surface clay content was derived through a combination of clay measurements and a proximal sensor. Apparent electrical conductivity (ECa) supported reliable clay content estimation. Predictions of clay coregionalized with ECa performed well, even for one soil sample per two hectares. Understanding the spatial variability of soil texture within field soils is important due to its influence on a large number of soil and plant related processes and for site‐specific application of inputs that are crucial to crop production. It remains a problem to obtain a reliable clay content map based on a limited number of sampling locations. The objective of this study was to identify spatial variability of soil clay content and the behavior of the estimation result for different spatial resolutions of measured clay content (0–20 cm depth) in combination with a coregionalization approach using apparent electrical conductivity (ECa). In a silty loam soil, soil clay content was measured at 96 points in a 50‐m by 50‐m grid within an agricultural field. ECa was measured using a contact sensor Veris 3150. Data were analyzed with ordinary kriging and cokriging while using ECa at a shallow depth. We analyzed different sampling scenarios based on clay subsamples of 48, 24, and 12 data points distributed over the 27‐ha field. In all scenarios investigated here, the RMSE stayed in the range of 3 to 4% by using different validations, with cokriging performing constantly better than ordinary kriging. Clay content maps estimated with cokriging maintained a satisfactory precision when the sampling density was reduced to one sample per two hectares, a result that leads to the conclusion that electrical conductivity in combination with spatial coregionalization demonstrated to be a promising tool to estimate the spatial variation of clay content even at a low clay sampling density.

How Well Does Noah-MP Simulate the Regional Mean and Spatial Variability of Topsoil Water Content in Two Agricultural Landscapes in Southwest Germany?

Abstract The spatial variability of topsoil water content (SWC) is often expressed through the relationship between its spatial mean 〈θ〉 and standard deviation σθ. The present study tests the concept that a reasonably performing land surface model (LSM) should be able to produce σθ–〈θ〉 data pairs that fall into a polygon, spanned by the cloud of observed data and two anchor points: σθ at the permanent wilting point σθ–〈θwp〉 and σθ at saturation σθ–〈θs〉. A state-of-the-art LSM, Noah-MP, was driven by atmospheric forcing data obtained from eddy covariance field measurements in two regions of southwestern Germany, Kraichgau (KR) and Swabian Alb (SA). KR is characterized with deep loess soils, whereas the soils in SA are shallow, clayey, and stony. The simulations series were compared with SWC data from soil moisture networks operating in the two study regions. The results demonstrate that Noah-MP matches temporal 〈θ〉 dynamics fairly well in KR, but performs poorly in SA. The best match is achieved with the van Genuchten–Mualem representation of soil hydraulic functions and site-specific rainfall, soil texture, green vegetation fraction (GVF) and leaf area index (LAI) input data. Nevertheless, most of the simulated σθ–〈θ〉 pairs are located outside the envelope of measurements and below the lower bound, which shows that the model smooths spatial SWC variability. This can be mainly attributed to missing topography and terrain information and inadequate representation of spatial variability of soil texture and hydraulic parameters, as well as the model assumption of a uniform root distribution.

Spatial variability of Australian soil texture: A multiscale analysis

Understanding how soil variability changes with spatial scale is critical to our ability to understand and model soil processes at scales relevant to decision makers. The compilation of large legacy data sets has opened up new possibilities to model spatial variability at the continental or even global scale. Using the National Soil Site Collation (NSSC) dataset of Australia we created empirical variograms for sand and clay fraction at extents from 1km to continental. The NSSC dataset is highly spatially clustered; a typical feature of legacy datasets. This leads to lumpy artefacts in the variograms. To reduce this lumpiness we employed grid based declustering. We used the declustered empirical variograms to calculate the Hausdorff Besicovitch Dimension – a unitless measure of spatial roughness. We first fit a power model to each declustered variogram and calculated the Hausdorff Besicovitch dimension at each modelled scale. This allowed us to assess the roughness or variability at each modelled extent, however this assessment was somewhat arbitrary and showed that roughness depends on the extent. We have proposed a new model that allows us to calculate the Hausdorff Besicovitch dimension continuously across all extents. The conceptual basis of this model moves away from a multi-fractal framework typically used by soil scientists. It allows us to describe spatial variability or stochasticity as a continuous function of spatial separation. Both our new model and the continental scale variograms of texture emphasise the high degree of short range variability in soil texture. Empirical variograms indicate that around 50% of spatial variability occurs at <10km, and 30% at <1km. Spatial variability of soil texture increases with depth consistently across all modelled extents. Beyond extents of around 100km, the Hausdorff Besicovitch Dimension remains relatively stable. Soil spatial variability is highly stochastic at fine scales however it changes gradually with extent and scale rather than abruptly.

Characterizing Spatial Variability of Soil Textural Fractions and Fractal Parameters Derived from Particle Size Distributions

Soil particle size distribution (PSD) is a fundamental physical property affecting other soil properties. Characterizing spatial variability of soil texture is very important in environmental research. The objectives of this work were: 1) to partition PSD of 75 soil samples, collected from a flat field in the University of Guilan, Iran, into two scaling domains using a piecewise fractal model to evaluate the relationships between fractal dimensions of scaling domains and soil clay, silt, and sand fractions and 2) to assess the potential of fractal parameters as an index used in a geostatistical approach reflecting the spatial variability of soil texture. Features of PSD of soil samples were studied using fractal geometry, and geostatistical techniques were used to characterize the spatial variability of fractal and soil textural parameters. There were two scaling domains for the PSD of soil samples. The fractal dimensions of these two scaling domains (D1 and D2) were then used to characterize different ranges of soil particle sizes and their relationships to the soil textural parameters. There was a positive correlation between D1 and clay content (R2 = 0.924), a negative correlation between D1 and silt content (R2 = 0.801), and a negative correlation between D2 and sand content (R2 = 0.913). The geometric mean diameter of soil particles had a negative correlation with D1 (R2 = 0.569) and D2 (R2 = 0.682). Semivariograms of fractal dimensions and soil textural parameters were calculated and the maps of spatial variation of D1 and D2 and soil PSD parameters were provided using ordinary kriging. The results showed that there were also spatial correlations between D1 and D2 and particle size fractions. According to the semivariogram models and validation parameters, the fractal parameters had powerful spatial structure and could better describe the spatial variability of soil texture.

Impacts of terrain attributes and human activities on soil texture class variations in hilly areas, south-west China.

Knowledge of soil texture variations is critical for agricultural and engineering applications because texture influences many other soil properties. This study used random forest method to evaluate the effects of human activities and topographic parameters on the spatial variability of soil texture in hilly areas where soil parent material was uniform. The study site covers 252km2 and is located in the Upper Yangtze River Basin of south-west China. A total of 3636 samples were collected from the cultivated soils at a depth of 20cm of dryland (sloping field and terraced land) landscape. The soil texture class for each sample was estimated by experienced soil scientists in the field. Two soil texture classes (loam and clay) were observed in the watershed. Eleven terrain parameters were derived from a digital elevation model with a resolution of 30m. Compared with loamy soils, clayey soils were mostly observed in the areas with lower elevation and gentle slopes. The outcome of random forest indicated that human activities and elevation had strong effects on soil texture class variations across the study site. Further results showed that the relative importance of terrain parameters to soil texture class variations varied with dryland landscape. Topographic wetness index and elevation were the most important variables for sloping field and terraced land landscapes, respectively.

Assessment of Spatial Variability and Mapping of Soil Texture in Western of Syria using Geographic Information System Techniques (GIS)

The study aimed to study the spatial variability of soil texture for soils around AL-Basel Lake in Safita Region which located in Tartous Governorate west of Syrian Arab Republic. Spatial maps of soil properties are invaluable in agricultural Production for assessing soil quality, planning land use and determining the suitability of cropping patterns. Geostatistics has been extensively used for quantifying the spatial pattern of soil properties and Inverse Distance Weight technique are proving sufficiently robust for estimating values at unsampled locations. The experiment was conducted on the soils of villages around AL-Basel lake The aim of this work is to study the spatial variability of soil texture. For this purpose ,90 Samples were collected. Results of Laboratory analysis of studied indexes were imported into ArcGis9.3 software and presented in form of Digital Maps that show the spatial Distribution of soil texture using Geostatistical Analyst and Inverse Distance Weight method was used in the Spatial Interpolation. Results of texture analysis showed that the soils have a texture between Loam and Silty Loam. Geostatistical analyst was used for unsampled points. Since 70.94 % of study area has Sand percentage (30-40) %, for Silt 57.73 % of study area has Silt percentage (40-50)% ,for Clay 71.97% of study area has Clay percentage (10-20)%,81.76% of study area has loam texture. This study thus provided a methodology that can help improve the accuracy and efficiency of soil texture mapping in areas using.

Determining soil moisture by assimilating soil temperature measurements using the Ensemble Kalman Filter

This study investigates the potential to estimate the vertical profile of soil moisture by assimilating temperature observations at a limited number of depths into a coupled heat and moisture transport model (Hydrus-1D). The method is developed with a view to assimilating temperature data from distributed temperature sensing (DTS) to estimate soil moisture at high resolution over large areas. The correlation between temperature and soil moisture in the shallow soil (top ∼ 50 cm) ensures that soil moisture can be estimated using just soil temperature observations. Synthetic tests across a range of soil textures show that with data assimilation both modeled temperature and the moisture profile are improved considerably compared to the ensemble open loop model simulations. In addition, employing data assimilation provides a means to quantitatively account for different sources of uncertainty. This is particularly relevant in the context of DTS given the influence of spatial variability in soil texture and its impact on estimation error. The data assimilation approach could also be used to determine, the number of temperature observations required and the depths at which they should be made. Results suggest that temperature observed at two depths is typically sufficient to estimate soil moisture using this approach. The root mean square error (RMSE) in soil moisture was reduced by up to 75% in the top 20 cm. Furthermore, this approach solves many of the challenges identified in the application of an inversion approach to estimate soil moisture from DTS.

Investigating soil controls on soil moisture spatial variability: Numerical simulations and field observations

Due to its complex interactions with various processes and factors, soil moisture exhibits significant spatial variability across different spatial scales. In this study, a modeling approach and field observations were used to examine the soil control on the relationship between mean (θ¯) and standard deviation (σθ) of soil moisture content. For the numerical experiments, a 1-D vadose zone model along with van Genuchten parameters generated by pedotransfer functions was used for simulating soil moisture dynamics under different climate and surface conditions. To force the model, hydrometeorological and physiological data that spanned over three years from five research sites within the continental US were used. The modeling results showed that under bare surface conditions, different forms of the θ¯–σθ relationship as observed in experimental studies were produced. For finer soils, a positive θ¯–σθ relationship gradually changed to an upward convex and a negative one from arid to humid conditions; whereas, a positive relationship existed for coarser soils, regardless of climatic conditions. The maximum σθ for finer soils was larger under semiarid conditions than under arid and humid conditions, while the maximum σθ for coarser soils increased with increasing precipitation. Moreover, vegetation tended to reduce θ¯ and σθ, and thus affected the θ¯–σθ relationship. A sensitivity analysis was also conducted to examine the controls of different van Genuchten parameters on the θ¯–σθ relationship under bare surface conditions. It was found that the residual soil moisture content mainly affected σθ under dry conditions, while the saturated soil moisture content and the saturated hydraulic conductivity largely controlled σθ under wet conditions. Importantly, the upward convex θ¯–σθ relationship was mostly caused by the shape factor n that accounts for pore size distribution. Finally, measured soil moisture data from a semiarid region were retrieved from the Automated Weather Data Network. The observed moisture data showed that based on soil texture, a positive θ¯–σθ relationship existed for sandy soils, while an upward convex one was observed for silty soils. The difference in the observed θ¯–sigmaθ relationship can be attributed to the differences in water holding capacities between sand and silt, which is consistent with the modeling results. The field data also revealed that increasing spatial variability in soil texture led to increased variability in soil moisture (e.g., the maximum σθ). Therefore, the effect of soil texture for verifying remotely sensed soil moisture products should be considered.

Error analysis of spatial interpolation of soil texture under different sampling schemes

为研究土壤质地的合理取样方式, 进而研究其空间变异情况, 为田间施肥及灌溉提供依据, 本试验利用地统计学方法和GIS技术, 在重庆市彭水县重庆烟草试验站, 利用289个表层土样, 研究了16 m间距的栅格取样法(对照, 253个土样, 扣除36个验证样点)、34 m间距的栅格取样法(115个土样)和随机取样法(115个土样)3种取样方式下土壤质地的空间插值精度。3种土壤颗粒指标中粉粒占68.43%, 砂粒含量最少, 占12.68%, 黏粒含量略高于砂粒。砂粒和黏粒具有中等强度的变异性, 粉粒具弱变异性, 且数据符合正态分布。地统计分析显示, 在分析该区域土壤质地时, 采用栅格取样方法应适当增大取样间距, 而采用随机取样方法可适当缩小取样间距。交叉检验显示, 土壤质地成分在3种取样方式下的插值精度均以对照最大, 栅格取样次之, 随机取样最小。综合考虑插值误差、样品采集和分析成本及时效性等因素, 本研究建议在该区域进行土壤质地空间变异规律分析为生产服务时应采用随机取样。

Spatial Interpolation of Soil Texture Using Compositional Kriging and Regression Kriging with Consideration of the Characteristics of Compositional Data and Environment Variables

The spatial interpolation for soil texture does not necessarily satisfy the constant sum and nonnegativity constraints. Meanwhile, although numeric and categorical variables have been used as auxiliary variables to improve prediction accuracy of soil attributes such as soil organic matter, they (especially the categorical variables) are rarely used in spatial prediction of soil texture. The objective of our study was to comparing the performance of the methods for spatial prediction of soil texture with consideration of the characteristics of compositional data and auxiliary variables. These methods include the ordinary kriging with the symmetry logratio transform, regression kriging with the symmetry logratio transform, and compositional kriging (CK) approaches. The root mean squared error (RMSE), the relative improvement value of RMSE and Aitchison's distance (DA) were all utilized to assess the accuracy of prediction and the mean squared deviation ratio was used to evaluate the goodness of fit of the theoretical estimate of error. The results showed that the prediction methods utilized in this paper could enable interpolation results of soil texture to satisfy the constant sum and nonnegativity constraints. Prediction accuracy and model fitting effect of the CK approach were better, suggesting that the CK method was more appropriate for predicting soil texture. The CK method is directly interpolated on soil texture, which ensures that it is optimal unbiased estimator. If the environment variables are appropriately selected as auxiliary variables, spatial variability of soil texture can be predicted reasonably and accordingly the predicted results will be satisfied.

Comparing Kriging and Regression Approaches for Mapping Soil Clay Content in a Diverse Danish Landscape

AbstractInformation on the spatial variability of soil texture including soil clay content in a landscape is very important for agricultural and environmental use. Different prediction techniques are available to assess and map spatial variability of soil properties, but selecting the most suitable

Spatial and temporal variability of soil electrical conductivity related to soil moisture

Soil electrical conductivity (ECa) is a soil quality indicator associated to attributes interesting to site-specific soil management such as soil moisture and texture. Soil ECa provides information that helps guide soil management decisions, so we performed spatial evaluation of soil moisture in two experimental fields in two consecutive years and modeled its influence on soil ECa. Soil ECa, moisture and clay content were evaluated by statistical, geostatistical and regression analyses. Semivariogram models, adjusted for soil moisture, had strong spatial dependence, but the relationship between soil moisture and soil ECa was obtained only in one of the experimental fields, where soil moisture and clay content range was higher. In this same field, coefficients of determinations between soil moisture and clay content were above 0.70. In the second field, the low soil moisture and clay content range explain the absence of a relationship between soil ECa and soil moisture. Data repetition over the years, suggested that ECa is a qualitative indicator in areas with high spatial variability in soil texture.

Spatial variability of soil texture in tobacco field

To understand the distribution mode of soil texture for enhanced precision fertilization in tobacco fields, spatial variability of soil texture in Pingdingshan tobacco field was analyzed via geostatistics method on Geographic Information System (GIS) platform. The spatial distributions of clay, sand and silt were then quantitatively built by ordinary Kriging — a linear unbiased interpolation method. The results show that soil granules have relatively large auto-correlation lengths, and with spatial heterogeneity scales of 609 m, 657 m and 429 m respectively for clay, sand and silt. About 80% of spatial variability of clay and sand is caused by structural factors. Directional semi-variogram analysis indicates that clay and sand have a relatively high spatial auto-correlation. Further analysis shows that while clay and sand are highly anisotropic, silt is feebly anisotropic. Our experiment demonstrates that the relatively strong spatial correlation and spatial variability of the soil texture may be the major factor responsible for spatial variations in soil properties, especially initial distributions of soil texture. It is therefore suggested that spatial variations in soil texture should be incorporated in fertilization scheme designs.

Spatial Variability of Soil Properties and Rice Yield Along Two Catenas in Southeast China

In the light of an increasing demand for staple food, especially rice, in southeast China, investigations on the specific site potential expressed as the relationship between soil and crop yield parameters gain increasing importance. Soil texture and several soil chemical parameters as well as plant properties such as crop height, biomass and grain yield were investigated along two terraced catenas with contrasting soil textures cropped with wet rice. We were aiming at identifying correlative relationships between soil and crop properties. Data were analyzed both statistically and geostatistically on the basis of semivariograms. Statistical analysis indicated a significant influence of the relief position on the spatial distribution of soil texture, total carbon and total nitrogen contents. Significant correlations were found for the catena located in a sandstone area (Catena A) between rice yield and silt as well as total nitrogen content. Corresponding relationships were not detectable for paddy fields that developed from Quaternary clays (Catena B). As suggested by the nugget to sill ratio, spatial variability of soil texture, total carbon and nitrogen was mainly controlled by intrinsic factors, which might be attributed to the erosional transport of fine soil constituents, indicating the importance of the relief position and slope in soil development even in landscapes that are terraced. The crop parameters exhibited short ranges of influence and about one third of their variability was unexplained. Comparable ranges of selected crop and soil parameters, found only for Catena A, are indicative of close spatial interactions between rice yield and soil features. Our findings show that especially in sandstone-dominated areas, a site-specific management can contribute to an environmentally safe rice production increase.

On the Relationship Between Mean and Variance of Soil Moisture Fields1

Abstract: The objective of this work was to explain an apparent contradiction in the literature related to the relationship between mean and variance (or standard deviation) of soil moisture fields. Some studies found an increase in soil moisture variance with decreasing mean soil moisture, while others showed a decrease. The evidence of maximum variance in the mid‐range of mean soil moisture was also reported in the literature. In this paper, we focus on the effects of spatial variability of soil texture on the relationship between variance and mean of soil moisture during soil dry‐down processes. Soil texture influences soil moisture mean and variance through its direct effects on evaporation and drainage, which are two main factors controlling soil drying. A differential equation describing soil moisture dry down is proposed and studied. Our study shows that as mean soil moisture is greater than a threshold, variance increases with decreasing mean soil moisture. If mean soil moisture is less than the threshold, variance decreases with decreasing mean soil moisture. The threshold depends on soil texture and is between the field capacity and the wilting point. The soil moisture dry‐down equation is also applied to explain the apparent contradiction with regard to the relationship between mean and variance of soil moisture fields reported in the literature.

Using the EM38DD Soil Sensor to Delineate Clay Lenses in a Sandy Forest Soil

The objective of this study was to locate clay lenses in a sandy forest soil. The study site was a Scots pine (Pinus sylvestris L.) plantation in the Campine region of Belgium, which has been selected as a European Union‐funded Level II Research Site for soil monitoring. Typically, the soils in the area have homogeneous sandy soil profiles, but clay lenses occur locally within a depth of 2 m of the soil surface. Locating these clay lenses is necessary because they can have a substantial impact on soil processes. Therefore, we used the EM38DD soil sensor to measure the soil apparent electrical conductivity (ECa) simultaneously in two orientations. Apparent electrical conductivity maps were generated and it was found that the variation in ECa was mainly driven by the spatial variability of soil texture across the study site. The ratio of the two orientations (profile ratio or PR) clearly revealed a circular pattern with decreased PR values (&lt;1), which was identified as a clay lens. To delineate the extent of this lens, two numerical methods were used: (i) a fuzzy‐k‐means classification of the PR map focusing on the lowest centroid class, and (ii) a probability approach through indicator kriging. Using a validation image obtained from directed auguring, cell‐by‐cell comparisons were made for these two methods complemented with spatial accuracy measures. Although both methods tended to underestimate the spatial extent of the clay lens, the indicator kriging method was the most accurate, with an overall accuracy of 0.838, a proportion of error due to locational errors (κ‐loc) of 0.864, and an average similarity of 0.841.