Spatial Patterns Of Soil Properties Research Articles

Interpreting the spatial distribution of soil properties with a physically-based distributed hydrological model

Digital soil maps are commonly data-driven as the development of physically-based models for soil mapping is difficult due to the complexity of soils. However, physically-based hydrologic models have been successful in simulating water dynamics. Since water movement is a major driver of pedogenesis, the physical rules that govern water movement might help explain and predict the spatial variation of soil properties. Here, we demonstrate the novel use of a physically-based, distributed hydrologic model to inform the spatial distribution of soil properties. The Distributed Hydrology Soil Vegetation Model (DHSVM) was utilized to simulate soil moisture content (SM) and water table depth (WTD) in two hillslope catchments under pasture and forest management wherein hydrologic model outputs were then compared with soil properties measured in situ. SM sensors and wells were installed in both catchments to validate simulations of soil water movement via Nash-Sutcliffe Efficiency (E). In-situ observations were made at 87 sites within both catchments to study the connection between simulated water movement (SM and WTD) and observed soil properties, namely the depth and thickness of the argillic (Bt), fragic (Btx), and C horizons, and the depth of redoximorphic features. The simulated time series of SM and WTD were also clustered per season using Dynamic Time Warping (DTW), which identified similarity among time series at varying timescales. Model validation suggested that simulations of surficial SM (0–20 cm) were reasonable (E = 0.45), however, simulated subsurface SM (45–60 cm) and WTD were not sufficiently accurate. The thickness of Btx horizons were spatially grouped into different populations by SM clusters from every season except spring. For the other properties, only SM dynamics of specific seasons grouped into significantly different populations, suggesting that the explanatory power of simulated water movement varies seasonally and was greater during winter. Here, we show clusters of simulated SM separated soil properties into statistically different populations, showing that hydrologic models could inform areas that followed different water dynamics related to pedogenic trajectories and related biogeochemical processes not necessarily simulated by the model. As such, physically-based modeling of water dynamics can, therefore, inform and advance digital soil mapping by linking water movement patterns stemming from hydrologic model outputs to spatial patterns of soil properties and pedogenesis.

A high spatial resolution soil carbon and nitrogen dataset for the northern permafrost region based on circumpolar land cover upscaling

Abstract. Soils in the northern high latitudes are a key component in the global carbon cycle; the northern permafrost region covers 22 % of the Northern Hemisphere land surface area and holds almost twice as much carbon as the atmosphere. Permafrost soil organic matter stocks represent an enormous long-term carbon sink which is in risk of switching to a net source in the future. Detailed knowledge about the quantity and the mechanisms controlling organic carbon storage is of utmost importance for our understanding of potential impacts of and feedbacks on climate change. Here we present a geospatial dataset of physical and chemical soil properties calculated from 651 soil pedons encompassing more than 6500 samples from 16 different study areas across the northern permafrost region. The aim of our dataset is to provide a basis to describe spatial patterns in soil properties, including quantifying carbon and nitrogen stocks. There is a particular need for spatially distributed datasets of soil properties, including vertical and horizontal distribution patterns, for modeling at local, regional, or global scales. This paper presents this dataset, describes in detail soil sampling; laboratory analysis, and derived soil geochemical parameters; calculations; and data clustering. Moreover, we use this dataset to estimate soil organic carbon and total nitrogen storage estimates in soils in the northern circumpolar permafrost region (17.9×106 km2) using the European Space Agency's (ESA's) Climate Change Initiative (CCI) global land cover dataset at 300 m pixel resolution. We estimate organic carbon and total nitrogen stocks on a circumpolar scale (excluding Tibet) for the 0–100 and 0–300 cm soil depth to be 380 and 813 Pg for carbon, and 21 and 55 Pg for nitrogen, respectively. Our organic carbon estimates agree with previous studies, with most recent estimates of 1000 Pg (−170 to +186 Pg) to 300 cm depth. Two separate datasets are freely available on the Bolin Centre Database repository (https://doi.org/10.17043/palmtag-2022-pedon-1, Palmtag et al., 2022a; and https://doi.org/10.17043/palmtag-2022-spatial-1, Palmtag et al., 2002b).

Temporal Changes in Spatial Patterns of Soil Properties During a Period of Rice Growth

Growing rice is complicated because of the interrelationship among production components, one of which is soil conditions. Determination of soil conditions is usually conducted by employing some soil properties. It is no doubt that soil properties have spatial and temporal characteristics. This research used two soil properties: Electrical conductivity (EC) and pH. This study aimed to use and evaluate Inverse Distance Weighted to uncover the temporal changes in pH and EC during the rice growth period. The methods used in the research were field survey, soil sampling, laboratory analysis, and data processing in Geographical Information Systems (GIS). The sample used was 30 and repeated three times under the growth period (vegetative, generative, and ripening). The results showed that there were changes in pH values of the soil in one growing season, and EC values tend to increase from the vegetative to the ripening period.

Effect of recreation on the spatial variation of soil physical properties

Recreation affects various components of ecosystems. A significant transformation occurs in the soil cover in urban parks. The physical properties of soil are sensitive indicators of the level of anthropogenic transformation, and also allow to assess the state of soil as a habitat for plants and soil animals. The question of quantitative patterns of soil properties variability under the influence of recreation is not solved. There is also little information on the spatial aspect of the variability of soil physical properties in urban ecosystems. The aim of our study is to test the hypothesis that the recreational loads cause the formation of spatial patterns of soil properties, which by their extent greatly exceed the zone of direct influence. The spontaneous walkways within an urban park were investigated as an example of recreational loading. The physical soil properties were measured on a regular grid. The distance to the walkway was treated as a proxy variable that indicates recreational load. The application of multivariate statistical methods allowed to reveal the components of the variation of soil properties of different nature. The effect of recreational load is superimposed on the natural variability of properties. The peculiarity of the influence of recreation consists in sharp increase of soil penetration resistance in the upper soil layers and decrease of this index in the lower layers. The recreational load affects the physical properties of the soil. The soil compaction is the main direction of transformation. This effect gradually attenuates with distance from the source of exposure while occupying a significant portion of the space. The variation of soil properties affects the redistribution of soil moisture and soil air, which significantly affects the living conditions of soil biota.

Fertile islands lead to more conspicuous spatial heterogeneity of bacteria than soil physicochemical properties in a desert ecosystem

The “fertile islands” resulting from discrete plants are important for the structure and function of dryland ecosystems. To understand the spatial heterogeneity of soil biotic and abiotic resources as a result of fertile islands in deserts, soil bacterial and physicochemical profiles were measured at four soil depths (0–10 cm, 10–20 cm, 20–40 cm, and 40–60 cm) in three soil patches (under mid-canopies, at the edge of canopy cover, and in bare soil) around legumes (Hedysarum scoparium shrubs) and non-legumes (Artemisia ordosica shrubs). Unexpectedly, the patches of the two plants showed similar soil abiotic and biotic patterns. Copiotrophs and potential decomposers were enriched under mid-canopies, with more phylogenetically clustered bacterial communities, and co-occurrence networks with lower connectivity and higher modularity than in bare soil. In bare soil, oligotrophs and potential nitrate reducers were enriched. Soil properties showed less conspicuous spatial heterogeneity than bacterial communities. The only soil physicochemical properties enriched under mid-canopies were soil organic matter, dissolved organic carbon, and nitrate; their enrichment was significantly related to bacterial communities. This study elucidates the spatial patterns of soil properties and their relationships, which are observed in fertile islands of desert ecosystems, and provides further insights into the mechanisms of soil nutrient redistribution.

Short‐term spatiotemporal variation of soil CO2 emission, temperature, moisture and aeration in sugarcane field reform areas under the influence of precipitation events

AbstractSoil CO2 emission (FCO2) in agricultural areas results from the interaction of different factors such as climate and soil conditions. Our objective was to investigate the spatiotemporal variation of FCO2, temperature (Tsoil), moisture (Msoil) and air‐filled pore space (AFPS), as well as their interactions, during the sugarcane field reform. The study was conducted on a 90 × 90 m sampling grid with 100 points at 10 m spacings. Ten assessments of FCO2, Tsoil and Msoil were carried out at each point over a 28‐day period. The greatest mean values of FCO2 (0.74 g m−2 hr−1) and Msoil (31.7%) were obtained on Julian day 276, 2013, being associated with precipitation events at the study site. Also, the smallest values of AFPS (19.17%) and Tsoil (20.90°C) were observed on the same day. The spatial variability of FCO2, Tsoil, Msoil and AFPS was best described by an adjusted spherical model, although an exponential model better fitted some results. The spatial pattern of all soil attributes showed little temporal persistency, indicating a high complexity for FCO2 during precipitation. Correlation maps assisted in identifying regions where Msoil and AFPS better controlled the emission process and where Tsoil was important. A major challenge for world agriculture is to increase the efficiency of conventional soil management practices. We highlight the importance of the spatial pattern of soil properties that directly influence the CO2 emission dynamics. Future mitigation actions should involve less intense tillage and ensure homogeneous applications of soil inputs, thereby reducing production costs and the contribution of these activities to CO2 emissions during the sugarcane field reform.

Effects of Growing-Finishing Pig Stocking Rates on Bermudagrass Ground Cover and Soil Properties.

Simple SummaryA common challenge for most livestock industries is to identify more productive, efficient and sustainable pasture-based production systems that have a positive effect on animal welfare, biodiversity and long-term operation profitability without negatively influencing the environment. Implementing best management practices allows producers to achieve profitability and environmental goals. Maintaining an appropriate ground cover, minimizing the use of external inputs as fertilizers and pesticides and adopting agroecological approaches are key for sustainable pasture management. Pasture-based pig production systems are considered animal welfare and environmentally friendly. However, the number of animals grazing can influence the vegetation ground cover and the amount of nutrients imported to the systems. This study compared the effects of four different pig stocking rates (37, 74, 111 or 148 pigs ha−1) over two 14-week grazing periods, on the vegetation ground cover and soil properties of bermudagrass paddocks. Increasing the number of animals aggravated the damage to the vegetative ground cover and raised the amount of nutrients deposited on the soil. For conservation purposes, the number of pigs grazing bermudagrass should be equal to or less than 37 pigs ha−1.This study compares four stocking rates (37, 74, 111 and 148 pigs ha−1) for growing to finishing pigs (18.4 ± 0.5 kg and 118.5 ± 2.0 kg and 35.7 ± 2.1 kg and 125.7 ± 2.3 kg initial and final BW for grazing periods 1 and 2, respectively) and their effect on ground cover and soil traits in bermudagrass (Cynodon dactylon [L.] Pers) pastures, over two 14-week grazing periods (July–September and May–August). The study was conducted at the Center for Environmental Farming systems at the Cherry Research Station, Goldsboro North Carolina. A continuous stocking method was implemented to manage the pasture. The percent ground cover was estimated with a modified step point technique. Soil samples were collected in three sampling positions (center, inner and outer areas of the paddocks) and two soil sampling depths (0–30 and 30–90 cm). The experimental design was a completely randomized block with three field replicates. Data were analyzed using the PROC GLIMMIX procedure of SAS/STAT ® Version 9.4. Greater ground cover and lesser soil nutrient concentrations were registered in bermudagrass paddocks managed with 37 pigs ha−1. The results of this study also validated the existence of a spatial pattern of soil properties, which differed among sampling positions and depths.

Spatial variation of soil properties impacted by aquaculture effluent in a small-scale mangrove

Small-scale mangroves serve ecological functions similar to large-scale mangroves regarding biological conservation, environmental purification, and supporting biogeochemical processes. The rising aquaculture neighboring mangroves results in their serving as an important sink for massive nutrients and pollutants from aquaculture effluent. We assessed how long-term aquaculture effluent discharge influenced the soil properties of a mangrove-tidal flat continuum using field survey and geostatistics. Common soil physical-chemical properties presented significant spatial variability. Continued aquaculture effluent discharge caused a significant cumulation of soil total organic carbon (SOC) (64.13 g·kg−1), total nitrogen (TN) (2.44 g·kg−1) and total phosphorus (TP) (1.12 g·kg−1) in the mangrove soil, which were as 2–3 times as those on the mudflat. Most of the soil properties changed significantly with increasing distance from the effluent outlet along a tidal channel, and the maximum concentrations of SOC, TN and TP all occurred at 50 m away from the outlet. The results of principal component analysis indicated that aquaculture effluent significantly affected the spatial pattern of soil properties along the mangrove-tidal flat continuum. Continued aquaculture effluent input rendered extensive accumulation of SOC, TN and TP in the mangroves. The spatial heterogeneity of mangrove is the key driver to process the nutrient input spatially differently.

Microtopography shapes soil pH in flysch regions across Switzerland

As topography is a key factor controlling soil genesis and strongly influences physical and chemical soil properties, terrain attributes are routinely used in digital soil mapping to spatially predict soil properties. Forests on flysch sediments along the northern slopes of the Swiss Alps often have a strong microrelief. The dominant soil types are Gleysols in depressions and Cambisols on ridges, with large pH variation within short distances. Based on the theory of soil development we expected that soil-forming processes driven by micro-scale topographic variation shape similar micro-scale spatial patterns of soil properties at different sites within the flysch region. Therefore, the main objective of the study was to investigate model extrapolation within flysch regions, which has turned out to be difficult on many other geological substrates. At three sites, each of about 2 ha, we first built three local models to examine whether a relationship between microtopography and topsoil pH could be inferred from high-resolution terrain attributes and pH measurements. Using data from all three sites we then calibrated a joint model and examined model extrapolation by calibrating models with data from two sites and predicting pH at the third. All models were based on multiple linear regressions that used 0.5 m resolution terrain attributes derived by a multiscale approach as explanatory variables. The cross-validated R2 for the local pH models varied between 0.56 and 0.77, and the corresponding RMSE between 0.57 and 0.64 pH units. The R2 and RMSE for the joint model were 0.60 and 0.76, respectively. The results of the local models suggest that microtopography is a dominant soil-forming factor on flysch sediments that triggers soil genesis on a spatial scale from submetre to metres. Although the extrapolated models showed a reduced prediction ability with R2 values of 0.25, 0.46 and 0.53, the selected terrain attributes were relatively similar among the models, which may indicate the common driving processes. The results for the joint model suggest that using high-resolution terrain attributes yields a fairly accurate spatial prediction of the highly variable topsoil pH in forests on flysch sediments across Switzerland.

Estimating the field capacity and permanent wilting point at the regional scale for the Hexi Corridor in China using a state-space modeling approach

The field capacity (FC) and permanent wilting point (PWP) are important soil hydraulic properties that determine the maximum available water for plants, and they are crucial parameters for biophysical models and irrigation management. However, previous estimates of the FC and PWP failed to consider their spatial correlations at the regional scale. Therefore, we estimated the FC and PWP using the state-space equation by considering the spatial correlations between soil properties. We estimated the FC and PWP using a first order autoregressive state-space equation based on the elevation (Elev), bulk density (BD), soil texture (Clay, Silt, Sand), soil organic carbon (SOC), and land use (LU) with a data set obtained from 104 in situ sampling sites across the entire Hexi Corridor. The results indicated that the distributions of the FC and PWP exhibited moderate variations in the Hexi Corridor, with values of 0.127 ± 0.060 and 0.075 ± 0.034 g/g (mean ± 1 standard deviation (SD)), respectively. According to t tests, the autocorrelation coefficients for FC, PWP, Elev, LU, Silt, Sand, and SOC as well as the cross-correlation coefficients between FC, PWP, and pertinent variables were significant with one lag distance (approximately 40 km) (p ˂ 0.05). Calculations of the coefficient of determination (R2) and root mean square error (RMSE) showed that the state-space models performed better at estimating FC and PWP than multiple linear stepwise regression models (SMLRs). Bivariate state-space equations based on Silt and LU were the optimal models for estimating FC (R2 = 0.999, RMSE = 0.002 g/g) and PWP (R2 = 0.997, RMSE = 0.002 g/g). According to the coefficients in the optimal state-space models, soil texture and LU were the dominant factors that affected the spatial variability in FC and PWP. After neglecting the spatial correlations between variables, the SMLRs showed that BD and soil texture were the best variables for estimating FC and PWP. The state-space approach is recommended as a useful tool for quantifying larger-scale spatial patterns in soil properties.

A new method for selecting sites for soil sampling, coupling global weighted principal component analysis and a cost-constrained conditioned Latin hypercube algorithm

Analysing spatial patterns of soil properties in a landscape requires a sampling strategy that adequately covers soil toposequences. In this context, we developed a hybrid methodology that couples global weighted principal component analysis (GWPCA) and cost-constrained conditioned Latin hypercube algorithm (cLHC). This methodology produce an optimized sampling stratification by analysing the local variability of the soil property, and the influence of environmental factors. The methodology captures the maximum local variances in the global auxiliary dataset with the GWPCA, and optimizes the selection of representative sampling locations for sampling with the cLHC. The methodology also suppresses the subsampling of auxiliary datasets from areas that are less representative of the soil property of interest. Consequently, the method stratifies the geographical space of interest in order to adequately represent the soil property. We present results on the tested method (R2 = 0.90 and RMSE = 0.18 m) from the Guinea savannah zone of Ghana.•It defines the local structure and accounts for localized spatial autocorrelation in explaining variability.•It suppresses the occurrence of model-selected sampling locations in areas that are less representative of the soil property of interest.

The spatial scaling of impact in edaphic and plant factors on the structuring of the soil macrofauna community

In this paper, the role of edaphic factors and plant, as well as the spatial variables was revealed in the structuring of the forest biogeocenosis soil macrofauna community in the river Dnepr arena within the nature reserve of Dnepr-Orelsky (Ukraine). 44 species of soil animals were found in the test polygon after manual sorting of soil samples. The density of the soil macrofauna in studied polygon is 321.5 ± 43.2 ind./m2. It is shown that the soil macrofauna community is structured under the influence of edaphic, plant and spatial factors. The role of these factors is different at fine-scale, medium-scale and broad-scale spatial levels. The variation of the community structure under the influence of edaphic and plant factors can be classified as deterministic processes, which is carried out within the framework of the ecological niche theory. The spatial component of the variation can be attributed to the result of the neutral nature of the factors. However, it should be noted that the spatial variation of plant community also is subject to the deterministic and neutral control. This manifests itself in the spatial structuring of plant factors. The spatial variation of soil properties has a similar nature. Soil as environment undergoes structuring influence of vegetation, where there are spatial patterns of soil properties. Scale effects of variation in space manifest themselves in different ways for the representatives of different ecological groups of macrofauna. For litter forms the most characteristic spatial patterns on large and medium-scale level, and for proper soil and burrowing forms - on fine-scale level.

Digital soil mapping in a Himalayan watershed using remote sensing and terrain parameters employing artificial neural network model

Digital soil mapping relies on field observations, laboratory measurements and remote sensing data, integrated with quantitative methods to map spatial patterns of soil properties. The study was undertaken in a hilly watershed in the Indian Himalayan region of Mandi district, Himachal Pradesh for mapping soil nutrients by employing artificial neural network (ANN), a potent data mining technique. Soil samples collected from the surface layer (0–15 cm) of 75 locations in the watershed, through grid sampling approach during the fallow period of November 2015, were preprocessed and analysed for various soil nutrients like soil organic carbon (SOC), nitrogen (N) and phosphorus (P). Spectral indices like Colouration Index, Brightness Index, Hue Index and Redness Index derived from Landsat 8 satellite data and terrain parameters such as Terrain Wetness Index, Stream Power Index and slope using CartoDEM (30 m) were used. Spectral and terrain indices sensitive to different nutrients were identified using correlation analysis and thereafter used for predictive modelling of nutrients using ANN technique by employing feed-forward neural network with backpropagation network architecture and Levenberg–Marquardt training algorithm. The prediction of SOC was obtained with an R2 of 0.83 and mean squared error (MSE) of 0.05, whereas for available nitrogen, it was achieved with an R2 value of 0.62 and MSE of 0.0006. The prediction accuracy for phosphorus was low, since the phosphorus content in the area was far below the normal P values of typical Indian soils and thus the R2 value observed was only 0.511. The attempts to develop prediction models for available potassium (K) and clay (%) failed to give satisfactory results. The developed models were validated using independent data sets and used for mapping the spatial distribution of SOC and N in the watershed.

Gestão diferenciada das pastagens baseada na monitorização da condutividade eléctrica aparente do solo

Fertilization is the main cost of maintenance of permanent pastures. Its incorrect use results in important direct economic losses and in environmental pollution. Usually, soils utilised for pasture and livestock production in Southern region of Portugal have high spatial variability. Consequently, it is necessary to determine the spatial patterns of the main soil properties as the first stage in implementing site-specific management strategies. However, this has to be performed using an inexpensive technique because the profitability in this type of farms are very low, so owners need an economical, effective, and reliable method to know which zones have similar potential management. The use of soil apparent electrical conductivity (ECa), as it integrates many soil properties affecting crop productivity (for instance, soil texture, organic matter and cation exchange capacity), constitutes one of the most appropriate soil variables to characterize the management zones of a field. In the present study ECameasurements obtained with a Veris 2000 XA contact-type sensor and soil samples taken at 0-0.30m depth in two permanent pastures were used. The aim was to assess the spatial pattern of some soil properties, based on ECameasurements as ancillary information and a guided soil sampling. Relationships between ECa and some important soil properties were analyzed and, later, some maps were produced to visualize the spatial variability of the soil properties. Finally, homogeneous zones were delimited for attaining a site-specific management of the fields. Results showed that ECa can be considered as support information with the aim of obtaining data to be used to implement site-specific management (intelligent soil sampling or differential and optimal fertilization), in soils with permanent pastures and to contribute to the increased sustainability of these systems.

Interpolation Approaches for Characterizing Spatial Variability of Soil Properties in Tuz Lake Basin of Turkey

Soil management is an essential concern in protecting soil properties, in enhancing appropriate soil quality for plant growth and agricultural productivity, and in preventing soil erosion. Soil scientists and decision makers require accurate and well-distributed spatially continuous soil data across a region for risk assessment and for effectively monitoring and managing soils. Recently, spatial interpolation approaches have been utilized in various disciplines including soil sciences for analysing, predicting and mapping distribution and surface modelling of environmental factors such as soil properties. The study area selected in this research is Tuz Lake Basin in Turkey bearing ecological and economic importance. Fertile soil plays a significant role in agricultural activities, which is one of the main industries having great impact on economy of the region. Loss of trees and bushes due to intense agricultural activities in some parts of the basin lead to soil erosion. Besides, soil salinization due to both human-induced activities and natural factors has exacerbated its condition regarding agricultural land development. This study aims to compare capability of Local Polynomial Interpolation (LPI) and Radial Basis Functions (RBF) as two interpolation methods for mapping spatial pattern of soil properties including organic matter, phosphorus, lime and boron. Both LPI and RBF methods demonstrated promising results for predicting lime, organic matter, phosphorous and boron. Soil samples collected in the field were used for interpolation analysis in which approximately 80% of data was used for interpolation modelling whereas the remaining for validation of the predicted results. Relationship between validation points and their corresponding estimated values in the same location is examined by conducting linear regression analysis. Eight prediction maps generated from two different interpolation methods for soil organic matter, phosphorus, lime and boron parameters were examined based on R2 and RMSE values. The outcomes indicate that RBF performance in predicting lime, organic matter and boron put forth better results than LPI. However, LPI shows better results for predicting phosphorus.

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.

Fractal approach in characterization of spatial pattern of soil properties

The objective of the study was to characterize spatial pattern of soil properties (CaCO3, soil organic carbon, P2O5, K2O, and clay content) using fractal concept. Total of 141 top-soil samples (0-30 cm) were collected on 1850 ha in karst polje (Petrovo polje, Croatia) and analyzed for listed soil properties. The semi-variogram method was used to estimate fractal dimension (D) value which was performed from both of isotropic and anisotropic perspective. The D value of soil properties ranged between 1.76 to 1.97, showing a domination of the short-range variations. The SOC and K2O fractal D values 1.79 and 1.76 respectively, exhibited a spatial continuity at the entire analysed range of the scale. The D value for P2O5 (1.97) showed a nearly total absence of the spatial structure at all scales. The CaCO3 and clay content indicated a multifractal behavior mainly attributed to effects of alluviation, differences in geology and its spatial changes and transitions. The results of anisotropic analysis of soil properties pattern have showed strong relations with directions and partial self-similarity over limited ranges of scales defined by scale-break. Finally, our results showed that fractal analysis can be used as a appropriate tool for the characterization of spatial pattern irregularities of soil properties and detection of soil forming factors that cause it.

Incorporation of satellite remote sensing pan-sharpened imagery into digital soil prediction and mapping models to characterize soil property variability in small agricultural fields

Soil prediction models based on spectral indices from some multispectral images are too coarse to characterize spatial pattern of soil properties in small and heterogeneous agricultural lands. Image pan-sharpening has seldom been utilized in Digital Soil Mapping research before. This research aimed to analyze the effects of pan-sharpened (PAN) remote sensing spectral indices on soil prediction models in smallholder farm settings. This research fused the panchromatic band and multispectral (MS) bands of WorldView-2, GeoEye-1, and Landsat 8 images in a village in Southern India by Brovey, Gram-Schmidt and Intensity-Hue-Saturation methods. Random Forest was utilized to develop soil total nitrogen (TN) and soil exchangeable potassium (Kex) prediction models by incorporating multiple spectral indices from the PAN and MS images. Overall, our results showed that PAN remote sensing spectral indices have similar spectral characteristics with soil TN and Kex as MS remote sensing spectral indices. There is no soil prediction model incorporating the specific type of pan-sharpened spectral indices always had the strongest prediction capability of soil TN and Kex. The incorporation of pan-sharpened remote sensing spectral data not only increased the spatial resolution of the soil prediction maps, but also enhanced the prediction accuracy of soil prediction models.Small farms with limited footprint, fragmented ownership and diverse crop cycle should benefit greatly from the pan-sharpened high spatial resolution imagery for soil property mapping. Our results show that multiple high and medium resolution images can be used to map soil properties suggesting the possibility of an improvement in the maps’ update frequency. Additionally, the results should benefit the large agricultural community through the reduction of routine soil sampling cost and improved prediction accuracy.

Topographic and Geologic Controls on Soil Variability in California's Sierra Nevada Foothill Region

Author(s): Beaudette, DE; O'Geen, AT | Abstract: We evaluated the feasibility of quantitative soil mapping in two catenas established on different lithologies (metavolcanic and granitic) in the Sierra Foothill Region of California. Indices of landform and microclimate were extracted from a 1-m elevation model. Variation in soil character (clay content, pH, color, cation-exchange capacity [CEC], and Feo/Fed) was partitioned across variables associated with terrain shape and microclimate, lithologic variability, and sampling depth. The potential for using digital elevation models (DEM)-derived indices of terrain shape to predict spatial patterns in soil properties varied greatly between our two experimental catenas. Terrain shape accounted for 4% (metavolcanic site) to 30% (granitic site) of variance in soil properties, while lithology accounted for 14% (metavolcanic site) to 22% (granitic site) of variance in soil properties. Sample depth accounted for 3% (metavolcanic site) to 12% (granitic site) of variance in soil properties. At the metavolcanic site, variability in lithology contributed more to soil variation than terrain shape, which makes digital soil modeling efforts a challenge in these regions. Up to 66% of the variance in soil properties was explained at the granitic site when considering terrain, lithology, sample depth, and associated interactions of these variables. Variance proportions can provide insight into the relative importance of soil-forming factors and is a useful tool when evaluating the efficacy of digital soil mapping projects.

Multifractal analysis of soil hydraulic properties in arid areas

Physical and hydraulic properties of soil are variable at different spatial scales. This indicates the necessity of understanding spatial patterns of soil properties. Scaling analysis, such as multifractal analysis, has been used to determine the spatial variability of soil properties. There are however limited numbers of studies concerning the applications of multifractal techniques applied to characterise spatial variability of soil properties in arid lands. The objective of this study was to quantify the scaling patterns of soil properties measured across a transect and to apply multifractal analysis in arid land areas. A transect with a length of 4.80km was selected, and soil properties were measured at 0–20cm depth every 145m along the transect. The soil properties analysed were: texture (sand, silt, clay), pH, electrical conductivity (EC), bulk density (BD), soil hydraulic properties (saturated hydraulic conductivity Ks and the van Genuchten soil water-retention equation’s parameters nv and αv), saturated water content (θs), and the slope of the soil water-retention curve at its inflection point (S). Results showed that the variability of pH and BD was characterised by quasi-monofractal behaviour. Results showed that soil hydraulic properties such as Ks, αn, nv, S, and θs were characterised by higher multifractal indices in the transects. EC showed the highest tendency to a multifractal type of scaling or the higher degree of multifractality.