Digital Soil Mapping
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POLARIS: A 30-meter probabilistic soil series map of the contiguous United States

Check out our new paper about the POLARIS dataset. Free download until June 3. https://t.co/js895BUSxs #soilscience #pedometrics
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Detailed mapping unit design based on soil–landscape relation and spatial variability of magnetic susceptibility and soil color

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The objective was to identify landscape areas with different patterns of variability using a statistic protocol with data of magnetic susceptibility (MS) and soil color that are covariate attributes of soil formation factors and processes. The studied area, of 380 ha, is located in Northeast of São Paulo State, Brazil. An amount of 86 samples was collected using 30 m intervals on the transect. At the transect sides, 150 samples were collected at 159 m intervals (a point each 2.5 ha). First the accuracy limits have been validated in the transect using the technique of Split Moving Windows — SMW. The limits identified in the transect were extrapolated to the sides using the contours of variability maps. The MS peaks SMW, for both depths, presented a correlation with the peaks of clay content (r = 0.7; P < 0.01), hue (varying from 0:37; P < 0.05 to 0.61; P < 0.01) and Normalized Difference Vegetation Index—NDVI (varying from − 0.25 to − 0.35, P < 0.05). The errors of the MS spatial variability maps (6.22–11.85%) were similar to the clay content ones (6:22 to 14:16%). MS was more efficient in the compartmentalization of the landscape (identification of areas with different patterns of variability) than the hue determined by diffuse reflectance spectroscopy in Oxisols under the transition Basalt and Colluvial–Elluvial–Alluvial Deposits. The results of this study can lead to using an alternative strategy that is a mapping of soil attributes and identification of areas with different patterns of pedogenic iron oxide variability.

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Algorithms for quantitative pedology: A toolkit for soil scientists

Algorithms for quantitative pedology: A toolkit for soil scientists | Digital Soil Mapping | Scoop.it
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Soils are routinely sampled and characterized according to genetic horizons, resulting in data that are associated with principle dimensions: location (x, y), depth (z  ), and property space (p). The high dimensionality and grouped nature of this type of data can complicate standard analysis, summarization, and visualization. The “aqp” (algorithms for quantitative pedology) package was designed to support data-driven approaches to common soils-related tasks such as visualization, aggregation, and classification of soil profile collections. In addition, we sought to advance the study of numerical soil classification by building on previously published methods within an extensible and open source framework. Functions in the aqp package have been successfully applied to studies involving several thousand soil profiles. The stable version of the aqp package is hosted by CRAN (http://cran.r-project.org/web/packages/aqp), and the development version is hosted by R-Forge (http://aqp.r-forge.r-project.org).

 
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Soil and Landscape Grid of Australia

Soil and Landscape Grid of Australia | Digital Soil Mapping | Scoop.it
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The Soil and Landscape Grid of Australia's, Australia-wide Soil Attribute Maps were generated using measured soil attribute data from existing databases in the national soil site data collation and spectroscopic estimates made with the CSIRO's National spectroscopic database (Viscarra Rossel & Webster, 2012). The spatial modeling was performed using decision trees with piecewise linear models and kriging of residuals. Fifty environmental covariates that represent climate, biota, terrain, and soil and parent material were used in the modeling. Uncertainty was derived using a bootstrap (Monte Carlo-type) approach to derive for each pixel a probability density function (pdf), from which we derived 90% confidence limits. The approach is described in Viscarra Rossel et al. (2015a).

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Digital Library

Digital Library | Digital Soil Mapping | Scoop.it

Soil profile descriptions have largely relied on morphometrics by which soil profile properties are mechanically measured and visually observed. These observations are then combined with chemical, physical, and mineralogical data or thin sections from soil horizons. Official guidelines and handbook for describing soils include the Soil Survey Manual (Soil Survey Division Staff, 1993) and the Field Book for Describing and Sampling Soils (Schoeneberger et al., 2012). Detailed soil observations are made for a whole range of purposes (e.g., mapping, classification, land evaluation, and pedological investigation). Commonly, a soil pit is dug, but observations are also made using augers, samplers, push probes, slice shovels, trenches, road cuts, or in quarries. The overall purpose of describing a soil profile is to preserve the image of the soil, and a full soil profile description consists of reference and geographic location, profile environment (climate and geology), site and area description, and a description of the soil horizons and its attributes and properties. The traditional field toolbox for soil profile descriptions includes augers, pickaxe, spade, knife, spatula, rock hammer, Munsell charts, maps, notebook, water bottle, HCl, sample bags, tape measure, clinometer, compass, altimeter or GPS, and camera (Fig. 1). These are used to measure and observe soil properties and horizons.

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Improved estimation of soil clay content by the fusion of remote hyperspectral and proximal geophysical sensing

Improved estimation of soil clay content by the fusion of remote hyperspectral and proximal geophysical sensing | Digital Soil Mapping | Scoop.it

Planning sustainable soil exploitation and land resource evaluation require up-to-date and accurate maps of soil properties. In that respect, geophysical techniques present particular interests given their non-invasiveness and their fast data acquisition capacity, which permit to characterize large areas with fine spatial and/or temporal resolutions. We investigated the relevancy of combining data from airborne hyperspectral (Hs), electromagnetic induction (EMI) and far-field ground-penetrating radar (GPR) for mapping soil properties, in particular soil clay content, at the field scale. Data from the three techniques were acquired at a test site in Mugello (Italy) characterized by relatively strong spatial variations of soil texture. Soil samples were collected for determining ground truth clay content. For the frequencies used in this study (200–650 MHz), the GPR surface reflection is mainly determined by soil dielectric permittivity, itself primarily influenced by soil moisture. In contrast, EMI is mostly sensitive to soil electrical conductivity, which integrates several soil properties including in particular soil moisture and clay content. Taking advantage of the complementary information provided by the two instruments, the GPR and EMI data were combined and correlated to local ground-truth clay content data to provide high-resolution clay content maps over the entire field area. Besides, a relationship was also observed between Hs data and clay content measurements, which permitted to produce a Hs-derived clay content map. EMI–GPR and Hs maps showed close spatial patterns and a relatively high correlation was observed between both clay content estimates, as well as between clay content estimates and ground-truth clay content measurements. Moreover, data fusion allowed constraining the EMI–GPR and Hs information and reduced the uncertainty of mapped clay content estimates. These results demonstrated great promise for integrated, digital soil mapping applications.

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Spatial Disaggregation – A Primer

Spatial Disaggregation – A Primer | Digital Soil Mapping | Scoop.it
Spatial Disaggregation – A Primer. Tom D’Avello – NRCS-NSSC-GRU c ontact: tom.davello@wv.usda.gov Travis Nauman – NRCS-NSSC-GRU, WVU c ontact: tnauman@mix.wvu.edu. Overview. Define ‘Disaggregation’ Approaches and Tools West Virginia Illinois Arizona Summary
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"A first generation digital soil map of a portion of the Uasin Gishu Pl" by John A Lomurut

The western Kenyan highlands are among the most highly populated and productive areas in Kenya's "breadbasket" regions. It is important, therefore, to make optimal use of available land to enhance food security. The objective of this project was to develop a first generation digital soil map of a portion of the Uasin Gishu Plateau to be used for both teaching and extension. To support digital map production, we sampled five representative pedons and analyzed them for organic matter, pH, extractable K+, Ca 2+, Mg2+, Al+3, and P, effective cation exchange capacity (ECEC), base saturation, soil texture, and clay mineralogy. Pedon KN12 is a poorly drained Vertisol (Typic Endoaquert) in a depression at ~2280 m elevation; pedons KN13 and KN14 are well-drained Oxisols at ~2230 m elevation with a petroferric contact within ~80 cm of the soil surface (Petroferric Eutrudox), and pedons KN15 and KN16 are well-drained Oxisols (Humic Eutrudox) at ~2780 m. All 5 pedons had clay textures throughout. There were no statistically significant differences (p>0.05) in extractable P and K+ levels, but the remaining parameters showed significant differences (p<0.05) among the sites. The Vertisol (KN12) had significantly higher ECEC, Mg2+ , Ca2+, base saturation, and pH, and lower Al +3 saturation than the Oxisols. As expected, base saturation was positively correlated with Ca2+, Mg2+, ECEC, and pH, and negatively correlated with Al+3. X-ray diffraction showed that the clay fraction of the Oxisols was predominately kaolinite with smaller amounts of mica. Goethite and rutile were also identified in KN13, KN15and KN16. The clay fraction of the Vertisol contained interstratified kaolinite-smectite and discrete kaolinite. One Oxisol (KN16) contained hydroxyl-interlayered vermiculite in addition to kaolinite. A Digital Elevation Model (DEM) was used to generate covariates such as topographic wetness index (WTI), percent slope, geomorphons, and altitude above channel network. These covariates were used to create a soil class map of a portion of the Usain Gishu Plateau that is significantly more detailed than the currently available soil maps of the area. A major constraint limiting digital map production in Kenya at this time is the poor spatial resolution (90 m) of the available DEM data.^ Keywords: XRD, x-ray diffraction; DSM, digital soil mapping, DEM, digital elevation model, geomorphons.^
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SoilOptix - High Resolution Top Soil Mapping System

SoilOptix is a very unique, non-contact technology that generates high resolution digital top soil maps. These maps deliver solid, detailed high definition l...
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Event: Second Training Workshop on Digital Soil Mapping for Eastern and Southern Africa - African Regional Coverage

Event: Second Training Workshop on Digital Soil Mapping for Eastern and Southern Africa - African Regional Coverage | Digital Soil Mapping | Scoop.it
This is the second of two events organized by the Global Soil Partnership (GSP), with the support of the European Commission, to follow up on priorities identified during launch workshops for the African Soil Partnership held in Accra, Ghana and...

Via Dr Lendy Spires
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A comparative assessment of support vector regression, artificial neural networks, and random forests for predicting and mapping soil organic carbon stocks across an Afromontane landscape

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Opportunities for information model driven exchange and on-line delivery of GlobalSoilMap data and related products.

Opportunities for information model driven exchange and on-line delivery of GlobalSoilMap data and related products. | Digital Soil Mapping | Scoop.it
The GlobalSoilMap vision is for numerous organisations, countries and nodes to generate and deliver soil data that complies with a set of agreed Specifications. While these Specifications define...
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Digital Soil Assessment of Agricultural Suitability, Versatility and Capital in Tasmania, Australia

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Digital Soil Assessment (DSA) is the application of interpretations to digital soil mapping (DSM). Since 2010, an operational DSA program has been underway in Tasmania, Australia, primarily for the assessment of agricultural land suitability for 20 different crops in newly commissioned irrigation schemes. This involves development of functional soil attribute and climate grids, initially undertaken in two pilot areas totalling 70,000 ha, with comprehensive soil sampling and temperature sensor networks. Through the Tasmanian State Government ‘Water for Profit Program’, this pilot land resource assessment has become operational and applied to the entire State (68,401 km2), covering a total of 19 irrigation schemes. Using a combination of newly collected and legacy soil data and a suite of spatial explanatory covariates, a total of 218 80 m resolution 3D soil attribute grids were produced using the digital mapping approach, together with quantified prediction uncertainties. These grids have contributed to the ‘Soil and Landscape Grid of Australia’ and the ‘GlobalSoilMap’ projects. Using a similar approach, functional climate grids were generated for chill-hours, growing degree-days and frost risk. The digital soil and climate grids were applied to pre-defined enterprise suitability rulesets to produce 20 different maps of enterprise suitability, including opium poppies, and a range of perennial horticultural, cereal and vegetable crops, uploaded to a publically accessible spatial internet portal (Land Information Services Tasmania; LISTmap), which includes functionality to identify soil and climate limitations, as an indication of potential land management inputs. The suitability surfaces provide a regional indication of potential areas to expand or diversify into a range of cropping enterprises. However, some informative supplementary products were also developed to provide an overall spatial guide to the more versatile agricultural areas. This included an enterprise versatility index (by combining all suitability surfaces to identify areas more suited to more enterprises); and application of individual commodity ‘financial gross-margins’ to identify the highest-valued agricultural land in terms of earning potential. These products demonstrate the utility of functional soil property grids and the collective capacity of DSA to answer questions of agricultural potential; this can ensure the appropriate land is targeted for appropriate uses to stimulate agricultural markets and maintain food security.

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Deriving World Reference Base Reference Soil Groups from the prospective Global Soil Map product — A case study on major soil types of Africa

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Soils are routinely sampled and characterized according to genetic horizons, resulting in data that are associated with principle dimensions: location (x, y), depth (z  ), and property space (p). The high dimensionality and grouped nature of this type of data can complicate standard analysis, summarization, and visualization. The “aqp” (algorithms for quantitative pedology) package was designed to support data-driven approaches to common soils-related tasks such as visualization, aggregation, and classification of soil profile collections. In addition, we sought to advance the study of numerical soil classification by building on previously published methods within an extensible and open source framework. Functions in the aqp package have been successfully applied to studies involving several thousand soil profiles. The stable version of the aqp package is hosted by CRAN (http://cran.r-project.org/web/packages/aqp), and the development version is hosted by R-Forge (http://aqp.r-forge.r-project.org).

 
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Comparing and evaluating digital soil mapping methods in a Hungarian forest reserve

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Validating digital soil maps using soil taxonomic distance: A case study of Ireland

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Recently, there has been a marked increase across the world in the demand for digital soil information, in which digital soil mapping research plays a key role. Methods to validate these digital soil maps are needed. Soil maps, and in particular soil taxonomic maps contain embedded information that represent an understanding of the functioning of the soil within its landscape and the contributing soil forming factors. These cannot be easily validated by a straight point-to-polygon comparison. Furthermore, the uncertainty associated with a misclassification is not binary, but rather a more complex measure that accounts for the degree of divergence between the point observation and map unit that takes into account these underlying relationships between soils, landscape and function. Here we present a map validation approach based on the soil taxonomic divergence and compare this to the outcome from validation based on a straight binary presence/absence evaluation of the map units. We do so for the newly generated soils map of Ireland at a scale of 1:250,000. We find that the overall accuracy calculated through the presence absence method was 69% accurate, whereas the minimum taxonomic distances concept, has an overall accuracy of 90.1%. In particular, soil map units with large spatial coverage tended to be assessed as being very uncertain using the presence/absence method, the confidence around these map units was significantly improved using the minimum taxonomic distances approach. Where large differences were observed between field observations and mapped soil units, we found the taxonomic distance measure a more informative diagnostic as why these differences were observed.

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SPATIAL DISAGGREGATION OF LAND SYSTEMS MAPPING I THE BURNETT CATCHMENT (SOUTH-EAST QUEENSLAND)

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Technological developments for spatia prediction of soil properties, and Dani Krige’s influence on it

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Técnicass e ferramentas de mapeamento digital de solos aplicadas as condições brasileiras para auxiliar levantamento de solos.

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CSIRO PUBLISHING - Soil Research

CSIRO PUBLISHING - Soil Research | Digital Soil Mapping | Scoop.it
Soil Research is an international journal of soil science publishing high quality research on: soil genesis, soil morphology and classification; soil physics and hydrology; soil chemistry and mineralogy; soil fertility and plant nutrition; soil biology and biochemistry; soil and water management and conservation; soil pollution and waste disposal
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80-metre Resolution 3D Soil Attribute Maps for Tasmania, Australia


Darren Kidd, Mathew Webb, Brendan Malone, Budiman Minasnay, Alex McBratney
Abstract
Until recently, Tasmanian environmental modelling and assessments requiring important soil inputs has had to rely on conventionally-derived soil polygons that were mapped up to 75 years ago. Following on from the ‘Wealth from Water’ project, where digital soil mapping (DSM) was used in a pilot project to map the suitability of twenty different agricultural enterprises over 70,000ha, the Tasmanian Department of Primary Industries Parks Water and Environment has applied DSM to existing soil datasets to develop enterprise suitability predictions across the whole state in response to further irrigation scheme expansion. The generated soil surfaces have conformed and contributed to the TERN (Terrestrial Ecosystem Research Network) Soil and Landscape Grid of Australia (www.csiro.au/soil-and-landscape-grid), a superset of GlobalSoilMap.net specifications. The surfaces were generated at 80m resolution for six standard depths and 13 soil properties (including pH, EC, organic carbon %, sand %, silt % and coarse fragments), in addition to several Tasmanian enterprise suitability soil attribute parameters. The modelling used soil site data with available explanatory state-wide spatial variables, including the SRTM-DEM and derivatives, gamma-radiometrics, surface geology, and multi-spectral satellite imagery. Regression trees were used to model the predicted spatial value, with upper and lower predictions estimated at the 90% confidence interval using a ‘leave-one-out-cross-validation’ within each ‘tree’ or partition. A ‘ten-fold-cross-validation’ was used to test overall model validation, and the final output derived by averaging each of the k-fold outputs to produce more-robust and less-biased outputs. The DSM has delivered realistic mapping for most attributes, with acceptable validation diagnostics and relatively low uncertainty ranges in ‘data-rich’ areas, but performed marginally in terms of uncertainty ranges in areas such as the world-heritage listed south-west of the state, with a low existing soil site density. The version 1.0 soil attribute maps form the foundations of a dynamic and evolving new infrastructure that will be improved and re-run with the future collection of new soil data. The Tasmanian mapping has provided a localised integration with the National Soil and Landscape Grid of Australia (www.csiro.au/soil-and-landscape-grid), and will help guide future investment in soil information capture by quantitatively targeting areas with both high uncertainties, and important ecological or agricultural value. 


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Soil Mapping Spacecraft Ready for Flight

NASA's Soil Moisture Active Passive (SMAP) spacecraft will be boosted into orbit aboard a United Launch Alliance Delta II rocket from Vandenberg Air Force Ba...
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Reflectance measurements of soils in the laboratory: Standards and protocols

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Pesquisa Agropecuária Brasileira - Digital soil mapping based on map extrapolation between physiographically similar areas

Pesquisa Agropecuária Brasileira - Digital soil mapping based on map extrapolation between physiographically similar areas | Digital Soil Mapping | Scoop.it
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