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This paper describes the construction of Australia-wide soil property predictions from a compiled national soils point database. Those properties considered include pH, organic carbon, total phosphorus, total nitrogen, thickness, texture, and clay content. Many of these soil properties are used directly in environmental process modelling including global climate change models. Models are constructed...
Our objective was to quantify the improvement in fine-resolution maps of soil organic carbon stock (CS, Mg C ha −1 ) resulting from utilizing multivariate sources of secondary information. Different geostatistical techniques for mapping CS in the top 0.3 m of soil with or without secondary information were assessed in three large no-till fields (49 to 65 ha) in Nebraska, which were sampled...
We propose a method for optimizing sampling for digital soil mapping in cases where no directly measured prior information of the primary variable of interest is available. Various ancillary variables (soil series, relative elevation, slope, electrical conductivity and soil surface reflectance) were assumed to provide indirect information about the spatial distribution of soil carbon stock (CS, Mg...
Soil scientists often have many covariates that they can use to predict soil properties by regression. They are ill-advised to use all available covariates uncritically, but methods for selection (whether informal or formal) that depend on data for both the predictors and the predictand are subject to selection bias. In this paper we propose an approach that uses automated methods for selecting variables,...
Spatial prediction with the presence of spatially dense ancillary variables has attracted research in pedometrics. While soil survey and analysis of soil properties are still expensive and time consuming, the secondary data can be made available on a dense grid for the whole area of interest. The main aim of using the ancillary data is to enhance prediction of soil properties by making use of the...
Legacy soil data form an important resource for digital soil mapping and are essential for calibration of models for predicting soil properties from environmental variables. Such data arise from traditional soil survey. Methods of soil survey are generally empirical and based on the mental development of the surveyor, correlating soil with underlying geology, landforms, vegetation and air-photo interpretation...
Over the last 10 years Digital Soil Mapping (DSM) has emerged as a credible alternative to traditional soil mapping. However, DSM should not be seen as an end in itself, but rather as a technique for providing data and information for a new framework for soil assessment which we call Digital Soil Assessment (DSA). Although still somewhat fluid, a procedural framework for DSM and DSA with its links...
This paper aims to investigate the potential of using soil-landscape pattern extracted from a soil map to predict soil distribution at unvisited location. Recent machine learning advances used in previous studies showed that the knowledge embedded within soil units delineated by experts can be retrieved and explicitly formulated from environmental data layers However, the extent to which the models...
Spatial estimates of tropical soil organic carbon (SOC) concentrations and stocks are crucial to understanding the role of tropical SOC in the global carbon cycle. They also allow for spatial variation of SOC in environmental process models. SOC is spatially highly variable. In traditional approaches, SOC concentrations and stocks have been derived from estimates for single or very few profiles and...
Digital soil mapping is currently experiencing a tremendous increase in available environmental covariates and resolution for spatial soil predictions, resulting in computational problems in terms of limited data handling capabilities of machine learning approaches. This is of particular importance when gridded spatial soil class maps are used as a basis for predictions containing large amounts of...
Digital soil mapping approaches that require quantitative data for prediction are difficult to implement in countries with limited data on soil and auxiliary variables. However, in many such cases there is a wealth of qualitative information available, such as profile descriptions, catenas or general purpose soil surveys. This type of information opens possibilities for more qualitative approaches...
Constructing a cost-effective and detailed digital soil map of Africa will require the extensive utilization of both legacy soil data and legacy soil-landscape knowledge — which in Africa is primarily available from reconnaissance-scale catena or association maps and related studies. We evaluated a hybrid approach for disaggregating reconnaissance scale soil maps: rapid and inexpensive delineation...
Digital soil mapping in mountain areas faces two major limitations: the small number of available observations and the non-linearity of the relations between environmental variables and soil properties.A possible approach to deal with these limitations involves the use of non-parametric models to interpolate soil properties of interest. Among the different approaches currently available, Support Vector...
The 1:50,000 national soil survey of the Netherlands, completed in the early 1990s after more than three decades of mapping, is gradually becoming outdated. Large-scale changes in land and water management that took place after the field surveys have had a great impact on the soil. Especially oxidation of peat soils has resulted in a substantial decline of these soils. The aim of this research was...
The history of digital soil mapping and modeling (DSMM) is marked by adoption of new mapping tools and techniques, data management systems, innovative delivery of soil data, and methods to analyze, integrate, and visualize soil and environmental datasets. DSMM studies are diverse with specialized, mathematical prototype models tested on limited geographic regions and/or datasets and simpler, operational...
There is a need for accurate, quantitative soil information for natural resource planning and management. This information shapes the way decisions are made as to how soil resources are assessed and managed. This paper proposes a novel method for whole-soil profile predictions (to 1m) across user-defined study areas where limited soil information exists. Using the Edgeroi district in north-western...
In digital soil mapping the spatial distribution of soil classes or properties is quantified by formulating empirical spatial or non-spatial soil inference systems between soil observations and spatially referenced environmental covariates. Uncertainty about the location of soil samples, however, will inflate the uncertainty in these predictive relationships. In this study we demonstrate the influence...
Fuzzy membership function is an effective tool to represent relationship between soil and environment for predictive soil mapping. Usually construction of a fuzzy membership function requires knowledge on soil-landscape relationships obtained from local soil experts or from extensive field samples. For areas with no soil survey experts and no extensive soil field observations, a purposive sampling...
Terrain attributes are the most widely used predictors in digital soil mapping. Nevertheless, discussion of techniques for addressing scale issues and feature selection has been limited. Therefore, we provide a framework for incorporating multi-scale concepts into digital soil mapping and for evaluating these scale effects. Furthermore, soil formation and soil-forming factors vary and respond at different...
Environmental factors that exert control over fine-scale spatial patterns of soil organic carbon (SOC) within profiles and across large regions differ by geographic location and landscape setting. Regions with large SOC storage and high variability can serve as natural laboratories to investigate how environmental factors generate vertical and horizontal SOC patterns across the landscape. This was...
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