Soil Colour

General Background

Soil colour is arguably one of the most obvious and easily observed soil morphological characteristics. Soil scientists use soil colour to differentiate genetic soil horizons as well as for the classification of soil types, e.g. The Australian Soil Classification. From a trained or untrained eye, some inference on soils may be made from observation of soil colour in relation to organic carbon content, mineral composition, soil water content and moisture regime. Our interest in this study is making inference of a soils’ capacity to drain or soil drainage, based on observed characteristics of soil colour.

In Australia, prior work of mapping the colour of Australian soils was performed by Viscarra Rossel et al. (2010), but was limited to just surface soils, output mapping to 5km spatial resolution, and only utilised a relatively small collection of vis-NIR spectra (from which colour was inferred) to develop spatial soil colour models.

From data discovery via the Australian Soil Data Federator, we were able to compile over 300 000 soil colour field observations (dry soil condition) collected across Australia. About 160 000 were for topsoils, while about 140 000 were for subsoils. Rather than exclusively using vis-NIR spectra, a logical line of investigation is to exploit the availability of a comparatively larger field observed dataset.

The imperative in this present work is to develop the digital soil mapping approach needed derive updated spatial estimates of soil colour across Australia. This is done for a topsoil or surface soil layer of undefined thickness, and a master or major horizon layer, as this layer is often used for the classification of soil types. The maps are created to 90m spatial resolution.

Material and Methods

Colour Space Conversions

Field classification of soil colours are near exclusively recorded using the Munsell HVC (Hue, Value, Chroma) colour system. Munsell HVC soil colour descriptions are not conducive for quantitative studies (Robertson 1977). Using a lookup table, we performed a conversion from the Munsell HVC colour space to the CIELAB colour space. The CIELAB colour space can describe any uniform colour space by the three variables: L*, a*, and b*. Each variable represents the lightness of the colour (L* = 0 yields black and L* = 100 indicates diffuse white), its position between red/magenta and green (a*, negative values indicate green while positive values indicate magenta) and its position between yellow and blue (b*, negative values indicate blue and positive values indicate yellow).

Digital soil mapping

Random Forest machine learning was used to independently model L*, a*, and b* target variables as a function of a suite of available national extent environmental covariates. While we did investigate various options for combined target variable modelling given the covarying relationships of the colour variables, neither were able to match the prediction skill of the independently treated approach. The L* variable was modelled as a categorical variable, both a*, and b* were modelled as continuous variables. For both top- and subsoil models, a dataset (n=10000) was selected out of each of the available datasets prior to any modelling for the sole purpose of evaluating the goodness of fit of the fitted models, akin to an out-of-bag model evaluation.

After modelling, the combined L*, a*, and b* were post-processed to line up the nearest HVC colour space chip using Euclidean distance quantification.

For colour visualisation of the soil colour maps, predictions were transformed to the RGB colour space using the same lookup table as for the conversion form Munsell HVC to CIELAB.

Results

Final Word

Models of topsoil colour appear marginally more skilful than for the subsoil. Given that reddish colour tones dominate across Australia, predictive models of a* are better than for b* variable which capture the yellow soil colour tones. In any case, neither model of each of the CIELAB target variables are perfect, and when combined, errors could potentially propagate or increase, leading to very unlikely soil colour

inferences. Despite such uncertainties, soil colour patterns generated by these models are in large part a reflection of collected field data. More in-depth study is needed to interpret these patterns from both a pedological and geomorphological context. An easy general interpretation in context of the topsoil (at least on the continental fringes), soils appear mainly as brown and black colours, reflecting what we might expect to see, given the presence of relatively higher levels of soil organic carbon compared with the subsoils. Overall, these maps provide an indicative understanding of the soil colour patterns across Australia in a highly granular manner, in a way that has been done before. This is through harnessing digital soil mapping, and extensive collections of environmental covariate data, and of course the massive amounts of publicly available soil data observations.