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Indices derived from remotely-sensed imagery are commonly used to predict soil properties with digital soil mapping (DSM) techniques. The use of images from single dates or a small number of dates is most common for DSM; however, selection of the appropriate images is complicated by temporal variability in land surface spectral properties. We argue that hyper-temporal remote sensing (RS) (i.e., hundreds...
Fine-resolution soil maps constitute important data for many different environmental studies. Digital soil mapping techniques represent a cost-effective method to obtain detailed information about soil types and soil properties over large areas. The main objective of the study was to extend predictions from 1:25,000 legacy soil surveys (including WRB soil groups, soil depth and soil texture classes)...
Soil pH controls the availability of the majority of plant nutrients, if not all, and determines the growth environment for plant roots. Profile depth functions have been used to represent the vertical distribution of soil attributes and to predict them at continuous depths. This paper proposes a new model to predict pH for a whole soil profile. Soil properties including pH are often similar within...
Soil depth has played a key role in the development of soil survey, implementation of soil-specific management and validation of hydrological models. Generally, soil depth at field scale is difficult to map due to complex interactions of factors of soil formation at field scale. As a result, the conventional sampling schemes to map soil depth are generally laborious, time consuming and expensive....
Machine-learners used for digital soil mapping are generally trained using either data derived from field-observed soil pits or from soil survey polygons - although no direct comparison of the accuracy resulting from the two methods has yet to be undertaken. This study examined such a comparison over the Okanagan Valley and Kamloops region of British Columbia where good quality soil pit and soil survey...
In this paper a spatial downscaling method is explored for generating appropriate farm scale digital soil maps. The digital soil map product to be downscaled is an Australian national extent soil carbon map (100m grid resolution). Taking into account the associated prediction uncertainties of this map, we used a simulation approach based on Gaussian random fields to generate plausible mapping realisations...
Digital Soil Mapping (DSM) products are simplified representations of more complex and partially unknown patterns of soil variations. Therefore, any prediction of a soil property that can be derived from these products has an irreducible uncertainty that needs to be mapped. The objective of this study was to compare the most current DSM method – Regression Kriging (RK) – with a new approach derived...
The limited availability of soil information has been recognized as a main limiting factor in digital soil mapping (DSM) studies. It is therefore important to optimize the joint use of the three sources of soil data that can be used as inputs of DSM models, namely spatial sets of measured sites, soil maps and soil sensing products.In this paper, we propose to combine these three inputs, through a...
Multi-scale soil variations are increasingly employed to improve the accuracy for digital soil mapping (DSM). In this study, we attempted to explore a methodology of wavelet analysis on this topic. The terrain attributes of a study area were decomposed using the wavelet analysis, and the resulted components were applied to map soil organic carbon (SOC) content, pH and clay content using multiple linear...
Estimation of carbon and nitrogen stocks is important for quantifying carbon and nitrogen sequestration as well as greenhouse gas emissions and inventorying national carbon and nitrogen balances. For Liaoning province of China, we estimated the vertical distribution of soil organic carbon (SOC), soil total nitrogen (STN), bulk density (BD), and mapped their spatial distribution at five standard soil...
Digital soil mapping (DSM) involves the use of georeferenced information and statistical models to map predictions and uncertainties related to soil properties. Many remote regions of the globe, such as boreal forest ecosystems, are characterized by low sampling efforts and limited availability of field soil data. Although DSM is an expanding topic in soil science, little guidance currently exists...
Leaching large amounts of acidity and metals into recipient watercourses and estuaries, acid sulfate (a.s.) soils constitute a substantial environmental issue worldwide. Mapping of these soils enables measures to be taken to prevent pollution in high risk areas. In Denmark, legislation prohibits drainage of areas classified as potential a.s. soils without prior permission from environmental authorities...
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