Soil colour: its measurement and its designation in models of uniform colour space

Melville, M. D.; Atkinson, G.

doi:10.1111/j.1365-2389.1985.tb00353.x

Cited by 113 publications

(53 citation statements)

References 36 publications

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“…The soil horizon data were digitized using the following rules: (i) Munsell colours were converted into the CIELab coordinates (COLX, COLY, and COLZ) using the method published by Melville and Atkinson (1985), (ii) soil texture estimates were converted into central sand and clay contents (SAND, CLAY in %) of the respective texture classes of the USDA triangle, (iii) carbonate estimates (CARB) were placed along a 1 to 5 ordinal scale; the numbers stand for non-effervescent, very slightly effervescent, slightly effervescent, strongly effervescent, and violently effervescent soils, (iv) stoniness remained in the percentage (SKEL), and (v) soil horizons were rated by the intensity of illuvial silica clay accumulation (LUV) and mollic properties (MOL), which are two main processes in the studied soils, using the following ordinals: 1 -not applicable; here the layer does not constitute a part of either A or B horizon, 2 -weak; here the layer constitutes a part of A or Figure 2. Sketch map of the study area (Rišňovce, Slovakia) and location of soil profiles of the experiment B horizon, but the horizon does not meet all the criteria for mollic Am or luvic Bt horizon, and 3 -intense; here the layer constitutes a part of the Am or Bt horizon.…”

Section: Methodsmentioning

confidence: 99%

Digital soil mapping from conventional field soil observations

Balkovič¹,

Rampašeková²,

Hutar³

et al. 2013

Soil Water Res.

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Balkovič J., Rampašeková Z., Hutár V., Sobocká J., Skalský R. (2013): Digital soil mapping from conventional field soil observations. Soil & Water Res., 8: 13-25.We tested the performance of a formalized digital soil mapping (DSM) approach comprising fuzzy k-means (FKM) classification and regression-kriging to produce soil type maps from a fine-scale soil observation network in Rišňovce, Slovakia. We examine whether the soil profile descriptions collected merely by field methods fit into the statistical DSM tools and if they provide pedologically meaningful results for an erosion-affected area. Soil texture, colour, carbonates, stoniness and genetic qualifiers were estimated for a total of 111 soil profiles using conventional field methods. The data were digitized along semi-quantitative scales in 10-cm depth intervals to express the relative differences, and afterwards classified by the FKM method into four classes A-D: (i) Luvic Phaeozems (Anthric), (ii) Haplic Phaeozems (Anthric, Calcaric, Pachic), (iii) Calcic Cutanic Luvisols, and (iv) Haplic Regosols (Calcaric). To parameterize regression-kriging, membership values (MVs) to the above A-D class centroids were regressed against PCA-transformed terrain variables using the multiple linear regression method (MLR). MLR yielded significant relationships with R 2 ranging from 23% to 47% (P < 0.001) for classes A, B and D, but only marginally significant for Luvisols of class C (R 2 = 14%, P < 0.05). Given the results, Luvisols were then mapped by ordinary kriging and the rest by regression-kriging. A "leave-one-out" cross-validation was calculated for the output maps yielding R 2 of 33%, 56%, 22% and 42% for Luvic Phaeozems, Haplic Phaeozems, Luvisols and also Regosols, respectively (all P < 0.001). Additionally, the pixel-mixture visualization technique was used to draw a synthetic digital soil map. We conclude that the DSM model represents a fully formalized alternative to classical soil mapping at very fine scales, even when soil profile descriptions were collected merely by field estimation methods. Additionally to conventional soil maps it allows to address the diffuse character in soil cover, both in taxonomic and geographical interpretations.

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Section: Methodsmentioning

confidence: 99%

Digital soil mapping from conventional field soil observations

Balkovič¹,

Rampašeková²,

Hutar³

et al. 2013

Soil Water Res.

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“…Si son rôle n'est plus aussi dominant dans les classifications à vocation universelle, telles la classification française ou la Soil Taxonomy américaine, la couleur reste un élément très important de (SHIELDS et al, 1966 ;KARMANOV, 1970 ;MELVILLE & ATKINSON, 1985). Par (CERVELLE et al, 1977 …”

Section: Introductionunclassified

La couleur des sols : appréciation, mesure et relations avec les propriétés spectrales

Escadafal¹,

Girard²,

Courault³

1988

Agronomie

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Colour is a soil datum which is very frequently used in field studies as well as in classification systems. Nowadays, it is analysed along with the spectral properties of soils and their constituents, which are observed in the laboratory or through remote sensing. In this paper, some elements of colorimetry were studied and applied to a series of 84 highly diversified soil samples. Metamerism occurred with negligible frequency, so it was possible to model the relations between colour and initial spectral properties. In consequence, the soil colours observed in the field through the Munsell soil charts could be given a physical meaning, which allows them to be newly interpreted.Additional key words : Munsell soil charts, reflectance, visible spectrum.

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“…Bg(w)-horizon), 2 -moderately developed cambic features (e.g. Bgw-horizon), 3 -strongly developed cambic features (Bw-horizons) 4 Munsell hue, chroma and value data were transferred into CIELab coordinates as introduced by Melville & Atkinson (1985) 5 1 -without stones, 2 -less than 10%, 3 -10 to 25%, 4 -25 to 50%, 5 -more than 50%…”

Section: Model Descriptionmentioning

confidence: 99%

Mapping soils using the fuzzy approach and regression-kriging case study from the Považský Inovec Mountains, Slovakia

Balkovič¹,

Čemanová²,

Kollár³

et al. 2007

Soil Water Res.

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The paper introduces a method of digital mapping of spatial distribution of soil typological units. It implements fuzzy k-means to classify the soil profile data (study area from the Považský Inovec Mountains, Slovakia) and regression-kriging with the selected digital terrain and remote sensing data to draw membership maps of soil typological units. Totally three soil typological units were identified: Haplic Cambisols (Skeletic, Dystric), Albic Stagnic Luvisols, and Haplic Stagnosols (Albic, Dystric). We analysed the membership values to these units with respect to terrain and remote sensing data. The membership values appeared as spatially smoothly dependant on the terrain gradients (linearly or exponentially) whereas the residua showed spatial autocorrelation. Based on regression and kriging analyses, the regression-kriging model was successfully deployed to draw raster membership maps. These maps yield coefficients of determination between R<sup>2</sup> = 56% (Albic Stagnic Luvisols) to R<sup>2</sup>= 79% (Haplic Cambisols (Skeletic, Dystric)) when evaluated by cross validation. The grid-based continuous soil map represents an alternative to the classical polygon soil maps and can offer a wide range of interpretations for landscape studies.

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Soil colour: its measurement and its designation in models of uniform colour space

Cited by 113 publications

References 36 publications

Digital soil mapping from conventional field soil observations

Digital soil mapping from conventional field soil observations

La couleur des sols : appréciation, mesure et relations avec les propriétés spectrales

Mapping soils using the fuzzy approach and regression-kriging case study from the Považský Inovec Mountains, Slovakia

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