Spatial Distribution Of Soil Types Research Articles

Influence of Morphometric Relief Parameters on Soil Depth Changes and Humus Horizon Thickness in Relation to Erosion-Accumulation Processes: A Study in the Ipeľská Pahorkatina Hills, Slovakia

Abstract This study examines the spatial distribution of soil types and their susceptibility to erosion and accumulation processes in a study area in Slovakia. Field research involving 71 probes identified various soil types, with Regosols and Cutanic Luvisols being predominant. The study found that erosion-accumulation processes were detected in 69.97% of the probes, with changes observed in soil horizons. Soil analysis revealed different relations between soil depth, humus thickness, and terrain characteristics such as slope, slope length, and slope length and steepness factor (LS factor). Specifically, we confirmed a moderately strong positive correlation between soil depth and humus thickness (r = 0.597, n = 71, p < 0.001). Shallow soils (0–30 cm) exhibited a very strong positive correlation between soil depth and humus horizon thickness (r = 0.978, n = 33, p < 0.001). Conversely, no relationship was found in moderately deep soils (30–60 cm) (r = 0.018, n = 14, p < 0.948). For deep soils, we identified a moderately strong positive correlation (r = 0.345, n = 24, p = 0.098). While slope and slope length showed relationships with soil depth and humus thickness, the LS factor did not exhibit a clear correlation. These findings underscore the importance of understanding soil dynamics in informing land management practices, especially in areas susceptible to erosion. Recommendations include continued monitoring of eroded soils and implementing erosion control measures to maintain soil health and sustainability in agricultural production amidst climate change challenges.

Edaphic properties under Vachellia macracantha in an elevation gradient of dry scrub in southern Ecuador

Understanding plant-soil interactions within an ecosystem can be valuable for land management. At species level, legumes promote biotic interactions and soil carbon (C) and nitrogen (N) conservation within dry tropical ecosystems. The objective of this research was to determine if Vachellia macracantha (Humb. & Bonpl. Ex Willd) Seigler & Ebinger influences the edaphic properties in relation to an elevation gradient and distance from the plant stem. The species under study did not show an influence on the edaphic properties in relation to the distance from the plant stem, however, with respect to the elevation gradient, variation was observed in the texture, bulk density, moisture, pH, and stocks of C and N (p-value <0.0001). Soil organic C stocks were measured from 4.68 kg m−2 (1200 m.a.s.l.) to 9.13 kg m−2 (1800 m.a.s.l.). We consider elevation as a key driver, since it integrates climatic variables (temperature and precipitation), decomposition and net primary productivity and expresses the erosive processes that intervene in the spatial distribution of soil types.

Using remote sensing to identify soil types based on multiscale image texture features

Studying the spatial distribution of soil types is an important academic and practical issue in agriculture. With the rapid development of remote sensing technology, the role of image texture as an auxiliary variable in remote sensing identification of objects has increased. It is of great importance to ascertain the optimal window size for extracting texture features and the multiscale fusion of texture feature parameters under the optimal window for different soil types. To reach this goal, soil types in a typical area of the Jiaodong Peninsula were selected as the subject investigated, homogeneity and entropy were selected as the two texture feature parameters, and the ability to identify the different soil types based on the textural features was systematically analyzed by using Landsat 8 remote sensing images. Moreover, the optimal window sizes for extracting texture features were determined, and the role of multiscale textural features in the classification of the soil types was also evaluated. The results show that the accuracy of classification significantly increased with the addition of textural features. The optimal single-scale window sizes for the homogeneity and entropy feature parameters were 19 × 19 and 21 × 21, respectively. The fusion of multiscale textural features further improved the classification accuracy. The optimal multiscale window sizes for the homogeneity were 7 × 7, 13 × 13, 19 × 19 and 21 × 21 and those for entropy were 5 × 5, 15 × 15, 21 × 21 and 23 × 23. Therefore, the method of using texture information in remote sensing images as auxiliary variables in digital soil mapping was feasible. The method of multiscale fusion of texture features, which resulted in greater classification accuracy, was better than that of single-scale window. These conclusions could play an important guiding role in soil digital mapping with remote sensing.

Effectiveness of a soil mapping geomatic approach to predict the spatial distribution of soil types and their properties

A soil map (1:50,000 scale) was recently produced in Sardinia (Italy) using a cost-effective GIS approach. In this study we aimed to verify, in two pilot areas and by means of statistical analysis, the effectiveness of the adopted methodology in representing and predicting the spatial distribution of soil types and properties. We focused on evaluation of 1) the influence of landforms and parent materials on soil types (WRB Reference Soil Groups) and selected soil properties and 2) the suitability of the adopted methodology for calibrating a model to predict land unit composition in terms of different soil types.Leptosols, Regosols and Cambisols were prevalent on slopes, with Leptosols being more frequent on convex slopes and Regosols and Cambisols on concave slopes. In flat areas, soil types mainly depended on the type and age of parent material, with Regosols and Cambisols prevailing on Holocene deposits and highly developed soils (mainly Luvisols) largely prevailing on Pleistocene deposits. On hard rock, Leptosols were very frequent on terrigenous metamorphic rock and frequent on granite. Besides Leptosols, Regosols occurred more frequently than Cambisols on both parent materials. Landforms strongly influenced soil depth and available water capacity. Soils on plains were deeper than those on slopes, where convex forms had shallower soils than concave forms. A similar trend applied to the available water capacity. The parent material had a significant effect on topsoil properties (thickness, texture, pH and organic carbon content) of soils belonging to the same WRB Reference Soil Group (analysis done on the most relevant WRB Reference Soil Groups, i.e. Leptosols, Regosols and Cambisols).We calibrated and tested stepwise multiple linear regressions (MLR) and general linear models (GLM) to predict the composition of map units in terms of different WRB Reference Soil Groups. The two models gave very similar results, with distinct distribution patterns that were coherent with the relationships observed between soil groups and specific combination of terrain attributes and parent materials. Results showed that both models were more reliable in predicting the absence rather than presence of a given soil type.

Evaluating Emissions of Nitrous Oxide from Cropland Soils Under Different Rotations in Mato Grosso, Brazil: A Scenario Simulation Study

Expansion of cropland involves immense land use changes, and the resulting intensified management practices have a strong influence on the functioning of the underlying soil. For instance, increased application of nitrogen (N) fertilizer is known to enhance fluxes of nitrous oxide (N2O) from the soil to the atmosphere. The emission factor (EF) proposed by the Intergovernmental Panel on Climate Change (IPCC) assumes a linear relationship between added N and N2O-N fluxes, but it does not account for environmental factors, such as soil properties or climate. Due to the high spatial and temporal variability of N2O-N fluxes, mechanistic models are preferable in terms of extrapolation to larger scales. In this study, we evaluated simulated N2O-N fluxes from soils under agricultural use in the Brazilian state, Mato Grosso, using the CANDY (Carbon and Nitrogen Dynamics) model. A control tool was developed in order to enable the simulation of 1 650 scenarios covering different sites (soil + climate) and management regimes (crop rotation + amount of applied fertilizer + sowing and harvesting dates). Results suggested that the sites had a very strong influence on calculated emissions, which is not accounted for by static EF. Furthermore, most fertilizer-induced N2O-N fluxes derived from the scenario simulations were best described by a non-linear function. For sounder budgeting on the federal and national scale, there is still a strong need for long-term observations of continuous crop rotations and spatial distribution of soil types and their specific characteristics. The presented results provide a valuable starting point for developing further scenario simulations and adapting experimental campaigns for N2O emission study.

Predicting soil moisture condition in arbitrary agricultural lands using the digital soil map and soil physical properties database

To predict soil moisture condition in arbitral agricultural lands by taking spatial distribution of soil type in a watershed into account, we investigated the possible proper methods of determining the soil hydrological parameters using available soil physical properties database of Japanese soils, “SolphyJ”, and the digital soil map. In this study, simulation of soil moisture was conducted at three neighboring locations, including monitoring sites each in Toyama and Fukui cities. The simulated results of soil moisture appeared to be improved when hydraulic parameters were obtained by fitting water retention data of SolphyJ compared to the parameters estimated by neural network with soil texture and dry bulk density data. It is probably because peculiar soil structure in each field could affect the hydraulic properties more than the soil texture. Simulation results also indicated that soil moistures are much different even if they are located in neighbors (<5 km). We concluded that combination of these two databases is useful for estimating soil hydraulic parameters and to predict soil moisture condition in arbitrary agricultural lands.

Modeling crop water consumption and water productivity in the middle reaches of Heihe River Basin

Heihe River Basin is located in the arid inland area of northwest China and is facing serious water shortage problems. Since irrigation is the largest water consumer in the middle reaches of the Basin, it is crucial to study the crop yields and water consumption in order to improve the agricultural water productivity and to support sustainable economic development in this region. Based on field experiments in 2012 on typical crops, AquaCrop model was calibrated for seed maize, field maize and spring wheat; the models were validated using monitored data in 2013. Then considering the spatial distribution of soil types, groundwater depth, agricultural management and cropping patterns, ArcGIS was applied for the pre/post processing of the AquaCrop to quantify the spatial distribution of water consumption and water use efficiency (WUE) in a typical irrigation district and the whole middle reaches. Results indicate that the AquaCrop model can reasonably simulate the canopy cover development, biomass accumulation and crops yield, as well as the evolution of soil moisture in this area. For example, the Nash–Sutcliffe efficiency index for seed maize canopy cover was at least 0.91 during calibration and 0.96 during validation. Spatial analysis of simulated water consumption showed that total water consumption decreased from east to west due to the nature of the crops and the area cultivated. WUE for all the crops was above unity, with the vegetables recording the highest in 2012 and 2013 of 2.74kgm−3 and 3.19kgm−3 respectively. The least WUE was recorded for spring wheat, i.e. 1.19kgm−3 and 1.67kgm−3 in 2012 and 2013 respectively. Further simulations under future possible climate change scenarios showed that WUE of seed maize and field maize might rise to some extent, while WUE for spring wheat might decrease by 0.39% in 2030 but increase by 14.63% in 2050 under climate change scenario SRES B2.

Soil distribution modeling using inductive learning in the eastern part of permafrost regions in Qinghai–Xizang (Tibetan) Plateau

Soil–landscape models serve as a basis for understanding the relationships between soils and landscape, and such models also allow us to study soil distribution, and classification. To understand the distribution of soils and landform relationships in permafrost regions, we developed a soil–landscape model by using See5.0 decision tree software in the Wenquan region of Xinghai County, in the eastern part of permafrost regions of Qinghai–Xizang (Tibetan) Plateau (QTP). The model was based on soil pedon data from 2009. Nine environmental factors closely related to permafrost-affected-soil formation were selected as variables for the model: land surface temperature for warm season and cool season, elevation, slope gradient, slope aspect, planform and profile curvatures, wetness index and NDVI. A 5-fold cross-validation method was applied to verify the effectiveness of the soil–landscape model. The model results were consistent with field observations, and the slope was the most strongly correlated factor with soil type of the nine environmental variables. The soils in the modeled area are mainly Ustic Cambosols and Ustic Isohumisols, which cover about 60.0% and 25.6% of the total area, respectively. Gelic cambosols occur mainly in permafrost region, while Ustic isohumosols occur in the transition region between permafrost and seasonally frozen ground. Further studies are required to utilize the soil–landscape model to predict the spatial distribution of soil types over the QTP.

Suitable regions for date palm cultivation in Iran are predicted to increase substantially under future climate change scenarios

SUMMARYThe objective of the present paper is to use CLIMEX software to project how climate change might impact the future distribution of date palm (Phoenix dactylifera L.) in Iran. Although the outputs of this software are only based on the response of a species to climate, the CLIMEX results were refined in the present study using two non-climatic parameters: (a) the location of soils containing suitable physicochemical properties and (b) the spatial distribution of soil types having suitable soil taxonomy for dates, as unsuitable soil types impose problems in air permeability, hydraulic conductivity and root development. Here, two different Global climate models (GCMs), CSIRO-Mk3.0 (CS) and MIROC-H (MR), were employed with the A2 emission scenario to model the potential date palm distribution under current and future climates in Iran for the years 2030, 2050, 2070 and 2100. The results showed that only c. 0·30 of the area identified as suitable by CLIMEX will actually be suitable for date palm cultivation: the rest of the area comprises soil types that are not favourable for date palm cultivation. Moreover, the refined outputs indicate that the total area suitable for date palm cultivation will increase to 31·3 million ha by 2100, compared with 4·8 million ha for current date palm cultivation. The present results also indicate that only heat stress will have an impact on date palm distribution in Iran by 2100, with the areas currently impacted by cold stress diminishing by 2100.

Land use and climate control the spatial distribution of soil types in the grasslands of Inner Mongolia

The spatial distribution of soil types is controlled by a set of environmental factors such as climate, organisms, parent material and topography as well as time and space. A change of these factors will lead to a change in the spatial distribution of soil types. In this study, we use a digital soil mapping approach to improve our knowledge about major soil type distributing factors in the steppe regions of Inner Mongolia (China) which currently undergo tremendous environmental change, e.g. climate and land use change. We use Random Forests in an effort to map Reference Soil Groups according to the World Reference Base for Soil Resources (WRB) in the Xilin River catchment. We benefit from the superior prediction capabilities of RF and additional interpretive results in order to identify the major environmental factors that control spatial patterns of soil types. The nine WRB soil groups that were identified and spatially predicted for the study area are Arenosol, Calcisol, Cambisol, Chernozem, Cryosol, Gleysol, Kastanozem, Phaeozem and Regosol.Model and prediction performances of the RF model are high with an Out-of-Bag error of 51.6% for the model and a misclassification error for the predicted map of 28.9%. The main controlling factors of soil type distribution are land use, a set of topographic variables, geology and climate. However, land use and climate are of major importance and topography and geology are of minor importance. The visualizations of the predictions, the variable importance measures as result of RF and the comparisons of these with the spatial distribution of the environmental factors delivered additional, quantitative information of these controlling factors and revealed that intensively grazed areas are subjected to soil degradation. However, most of the area is still governed by natural soil forming processes which are driven by climate, topography and geology. Most importantly though, our study revealed that a shift towards warmer temperatures and lower precipitation regimes will lead to a change of the spatial distribution of RSGs towards steppe soils that store less carbon, i.e. a decrease of spatial extent of Phaeozems and an increase of spatial extent of Chernozems and Kastanozems.

Spatial distribution pattern changes of oasis soil types in Manasi River Basin, arid northwestern China

Soil distribution pattern play a significant role in the stability conservation and economic development of oasis in arid regions. Hence, ecologists and agrology scientists have a central interest in understanding the spatial distribution changes of soil types. The aim of this study was to analyze the main processes of soil distribution pattern changes from 1987 to 2006 through the landscape indexes. Soil types and soil distribution pattern changes were assessed and compared by using two soil maps made in 1987 and 2006. 14 soil types were classified and analyzed in the study area. Results indicated many differences among the changes of different soil types. During the period from 1987 to 2006, there were widespread changes in spatial distribution of soil types in Manasi River Basin at class-level. The area of Petrocambids decreased, whereas Aquicambids increased. The small patches began to coalesce into large ones and the patch numbers decreased during the past 20 years, which brought about the fragmentation decrease in Manasi River Basin. In contrast to the decrease of the patch density, the average patch area of 12 soil types increased. With the increasing man-made disturbance, more soil type patches, especially the agricultural soil patches were close to square in shape. During the recent 20 years, the decreased patch shape indexes occupied about 57% of all while the increased patch shape indexes were over 40%. The split index of most soil types has also declined during the same period. The landscape-level indexes also reflected the spatial distribution changes of oasis soil types. The landscape diversity index and landscape evenness index have increased while the landscape dominance index has decreased in the recent 20 years, which showed that more equirotal soil patches were formed and various soil types dominated the soil landscape in Manasi River Basin. Changes of different soil types are one of major indictors to show environment changes and impacts of human activities. Therefore, it is necessary to emphasize the study of soil type changes in the arid and semiarid region.

Hydrostratigraphic analysis of the Darling River valley (Australia) using electromagnetic induction data and a spatially constrained algorithm for quasi-three-dimensional electrical conductivity imaging

Efforts are being made to improve the irrigation efficiency in the Murray-Darling River Basin, Australia, to deal with predicted rainfall decline and to reduce the incidence of secondary soil and water salinization. The latter commonly occurs as a result of locating water reservoirs upon relic drainage channels. To better manage irrigation, information is required about the spatial distribution of soil type and the stratigraphic features capable of redistributing deep-draining water. In previous research, electromagnetic (EM) induction instruments (e.g. EM38 and EM34) have been used to map the distribution of soil type, hydrological processes (e.g. deep drainage) and vadose-zone features. The aim of this research is to demonstrate how a joint inversion of EM38 and EM34 data, using a one-dimensional spatially constrained algorithm for quasi three-dimensional (quasi-3D) electrical conductivity imaging, can be used to infer the areal distribution of soil types and physiographic and hydrogeological units. The quasi-3D modeling of true electrical conductivity provides a framework for future environmental monitoring and management to mitigate the hydrological processes that drive localized secondary salinization in the study area.

Soils fieldwork, analysis, and interpretation to support hydraulic and hydrodynamic modelling in the Murray floodplains

There are limited datasets which cover the heavy clays found in the Murray floodplain area. To understand the processes associated with the water balance within the Koondrook–Perricoota Forest (KPF), detailed hydraulic and hydrodynamic modelling of the flood inundation patterns and overland flow in the KPF is required. Reliable and accurate soils information is critical for any credible hydrologic or hydrodynamic modelling results. Extensive fieldwork across the entire KPF and detailed laboratory testing of the collected samples was undertaken to produce soils information including: spatial distribution of soil types, soil stratigraphy along the surface and subsurface flowpaths, soil hydraulic properties, soil salinity, and soil organic matter. Soil sampling and soil profile descriptions were undertaken at 26 sites spread across the forest. Deep drilling was done at 12 sites to check the existence of ancestral streams and for salinity profiles; soil hydrology testing to estimate infiltration rates was undertaken at 10 sites. Rapid appraisal methods for soil infiltration were developed for the project. Results were compared to soil pedotransfer functions generated from laboratory results; soil indexes including the dispersibilty index and electrochemical stability index; and typical infiltration and permeability rates inferred from soil texture and structure. The results from this study and the archived soil physical and hydraulic datasets can be used for any detailed hydraulic or hydrodynamic modelling exercise in the Murray floodplain area with similar soil properties.

Land Use and Land Cover Map of Ninevah GovernorateUsing Remote SensingData

Changes in the nature of land use activities, in time and space, often result in land cover changes and, as such, are an indication of land management activities. This research represents an attempt to evaluate the ability of using the new version of Anderson's classification system up to the third level in the land use and land cover classifications of Ninevah Governorate using Landsat TM7 imagery. The study area represents one of the most varied and complex geomorphic landscapes in Iraq, and characterized by irregular land use and different land cover in the geomorphic distribution of landforms. Geomorphic agent represent the main factors in the spatial distribution of soil types, lithological escarpments, and the existing land use patterns. The land use and land cover map shows the whole of the apparent activities up to the third level according to the USGS system, and 27 classes of land use are shown in the final map. The results show that visual analyses of satellite images, applying methods similar to those commonly used for aerial photo analysis, are sufficient for observing features shown on 1:100,000 scale Landsat images. Digital classification is appropriate for some applications, but it was found that land use and land cover mapping projects of the type described here are not among them. These data were utilized to draw a new regional land use and land cover map of Ninevah Governorate at a scale of 1:250,000 .

Generalized Coupled Markov Chain Model for Characterizing Categorical Variables in Soil Mapping

We developed a general formulation of the Markovian transition probability model and the corresponding computational algorithm for characterizing heterogeneity in soil types. The generalized model is based on the previously developed coupled Markov chain (CMC) model in which spatial conditioning is done using transition probabilities that incorporate field observations. The generalized coupled Markov chain (GCMC) model is more flexible with respect to conditioning than the previous CMC model because there are no restrictions on the input data format, and a random sequence calculation algorithm is used. The GCMC model was compared with the sequential indicator simulation (SIS), and the results were quantitatively analyzed. When adequate soil sampling data are available, the GCMC model predicts the spatial distribution of soil types as well as or better than the SIS model. The GCMC model has the advantage of simple input variables (because preprocessing is not required) and faster computation time (by about 60%). The models were also tested with sparse data sets, and the GCMC model predicted the presence of soil types better than the SIS model, based on a metric derived from ensemble probabilities. Further studies are in progress to expand applications of the model to stationary and nonstationary soil type distributions, improve algorithm efficiency, address underestimation caused by undersampled lithology, and extend the model to three dimensions.

Impacts of soil quality differences on deforestation, use of cleared land, and farm income

Preparing regional development strategies for the Amazon Basin is a vexing task for policymakers. Forests continue to fall and agriculture to move in to a region with patchy (in terms of agronomic potential) yet broadly nutrient-poor soils. The spatial distribution of soil types is not well mapped at finer scales relevant for agriculture. There is, moreover, little evidence about how farm land use or farm household welfare varies by soil quality in this frontier setting. Despite these information gaps, regional planners continue to use soils as a basis for policy action, some of which may influence future options for the Amazon. This paper uses a farm-level bioeconomic model that captures soil-quality-specific degrading effects of agricultural activities to assess the impacts of soil quality differences on deforestation, use of cleared land, and smallholder income in the western Brazilian Amazon. Focusing on an archetypical area farm with reasonable market access but limited access to labor and credit, simulations show soil quality mattered more for income than for deforestation or land use, although extremely cash-strapped farmers on poor-quality soils could face displacement. Pasture dominated farm land use across all soil types as farm forest disappeared within a generation on successfully established farms. Good- and (viable) poor-soil farms had slightly slower deforestation rates than their medium-soil counterparts – rich-soil farms shifting small amounts of area (and labor) to the more nutrient- and labor-intensive annuals, and (even viable) poor-soil farms lacking sufficient resources to clear and farm additional land. Farms with good soils could generate about 44 per cent more income than their viable poor-soil counterparts, but the lower-income level still surpassed thresholds for meeting food security and other needs. At no combination of income level and soil quality explored did the (simulated) farmer find it worthwhile to purchase and apply chemical fertilizer; nutrients came instead from secondary forest fallow, whose area rose or fell in step with annual cropping area. The implications of these results for land-use zoning, forest conservation, poverty alleviation, and other policies are discussed.

Two‐dimensional Markov Chain Simulation of Soil Type Spatial Distribution

Soils typically exhibit complex spatial variation of multi‐categorical variables such as soil types and soil textural classes. Quantifying and assessing soil spatial variation is necessary for land management and environmental research, especially for accurately assessing the water and solute transport processes in watershed scales. This study describes an efficient Markov chain model for two‐dimensional modeling and simulation of spatial distribution of soil types (or classes). The model is tested through simulations of a simplified soil map. The application of the model for predictive soil mapping with parameters estimated from survey lines is explored. Analyses of both simulated maps and associated semi‐variograms show that the model can effectively reproduce observed spatial patterns of soil types and their spatial autocorrelation given an adequate number of survey lines. This indicates that the model is a feasible method for modeling spatial distributions of soil types (or classes) and the transition probability matrices of soil types in different directions can adequately capture the spatial interdependency relationship of soil types. The model is highly efficient in terms of computer time and storage. The model also provides an approach for assessing the uncertainty of soil type spatial distribution in areas where detailed survey data are lacking. The major constraint on applications of the model at this stage is that the minor soil types are relatively underestimated when survey lines are too sparse.

Catenary soil development influencing erosion susceptibility along a hillslope in Uganda

Systematic variations of soil properties occur along the hillslope due to differences in water and energy conditions. Such catenary soil variations are often assumed, in turn, to influence erosion processes, but quantitative investigations dealing with the reciprocal relationship between catenary soil development and erosion processes are limited. This study models the influence of catenary soil development on erosion processes on a hillslope in Uganda. The Water Erosion Prediction Project (WEPP)- model was selected to determine the impact of spatial distribution of soil types on hillslope soil loss. A detailed soil survey confirmed a well-developed catenary sequence at the study site. Soils at the summit position had a thick solum due to the stable soil formation on the flat surface, whereas soils at the shoulder position had shallow A-horizons due to active erosion processes. Valley and footslope soils showed hydromorphic features and accumulation of soil material from upslope. The performance of the WEPP- model was evaluated by a sensitivity analysis, which proved that the model was sensitive to vertical changes in soil properties to a depth of 40 cm. High sensitivity to soil texture indicated that the catenary sequence at the study site may have a strong influence on model simulations. When the hillslope was modelled as a uniform soil-landscape unit using each individual soil profile separately, simulated outputs showed high variations with annual soil loss ranging between 2.5 and 9 t/ha. This variation was reduced by including an increasing number of distinct soil-landscape units representative for the individual slope sections. Simulations considering a catenary soil sequence showed a clear spatial demarcation between erosion and sedimentation zones, which was verified by soil investigations. This implies that simulations including a higher number of soil-landscape units generate a more realistic spatial distribution of erosion–sedimentation processes at a hillslope.

Variations in Reflectance of Tropical Soils: Spectral-Chemical Composition Relationships from AVIRIS data

The relationships between Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) surface reflectance values and constituents (total iron, organic matter, TiO 2, Al 2O 3, and SiO 2) of samples representative of three important soil types from central Brazil [Terra Roxa Estruturada (S TE), Latossolo Vermelho-Escuro (S LE), and Areia Quartzosa (S AQ)] were analyzed. End member spectra for green vegetation (GV), nonphotosynthetic vegetation (NPV), water (W), and the three soil types were selected by inspecting scatter plots derived from the principal components analysis (PCA) of 140 AVIRIS bands. They were then used to compose a six end member unmixing model to characterize the spectral reflectance variations associated with the different scene components, the spatial distribution of the soil types, and the effects of spectral mixing on the spectral-chemical composition relationships. Finally, regression equations fitted to soil constituents and their highly correlated spectral bands were used to produce maps showing the chemical variability in the scene for areas dominated by the presence of exposed soils, as indicated by the results from the unmixing model. The results showed a very good agreement between the spatial variability of the soil types and of the soil constituents. The largest squared correlation results were obtained for Fe 2O 3, TiO 2, and Al 2O 3, but the relationships were affected in the transition from the red to the near-infrared interval by the presence of nonsoil residues (e.g., senescent vegetation or litter) over the soil surfaces. In comparison with the light and loamy sand S AQ, the dark-red clay S TE and S LE presented higher contents of Fe 2O 3, Al 2O 3, and TiO 2, and consequently lower overall reflectance in the scene, because of the presence of greater amounts of opaque minerals. The prediction of these constituents from remote sensing data and their close association with the spatial distribution of the different soil types demonstrate the importance of the present investigation for soil mapping and soil erosion studies. All Rights Reserved.

Derivation of Soil Properties Using a Soil Land Inference Model (SoLIM)

AbstractSoLIM (Soil Land Inference Model) is a fuzzy inference scheme for estimating and representing the spatial distribution of soil types in a landscape. This study developed the inference method a step further to derive continuous soil property maps through two case studies. The first case illustrates the derivation of soil A horizon depth in a mountainous area in western Montana. It was found that the inferred depths are a closer fit to observed depths than those derived from the conventional soil map at both spatial and attribute levels. The second case shows the derivation of soil transmissivity values across a small catchment with a gentle environmental variation in Tumut, NSW, Australia. This case shows that the derived soil transmissivity map is comparable to the results from systematic field survey over a small area. SoLIM works well in an area where there is a good understanding of the relationships between soils and their formative environment and where the soil formative environment can be characterised using current geographical information system techniques. However, we experienced difficulty with the methodology when it was applied in an area where the environmental gradient is gentle and the soil formative environment cannot be very well described using the primitive environmental indices currently employed in SoLIM.