# Comparing three approaches of spatial disaggregation of legacy soil maps based on 1 DSMART algorithm

Yosra Ellili, Brendan Philip Malone, Didier Michot, Budiman Minasny, Sébastien Vincent, Christian Walter, Blandine Lemercier

► To cite this version:

Yosra Ellili, Brendan Philip Malone, Didier Michot, Budiman Minasny, Sébastien Vincent, et al. Comparing three approaches of spatial disaggregation of legacy soil maps based on 1 DSMART algorithm. 2019. hal-02362155

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1 **Title:** Comparing three approaches of spatial disaggregation of legacy soil maps based on

2 DSMART algorithm

3

4 **Authors:**

5 Yosra Ellili¹, Brendan Philip Malone², Didier Michot³, Budiman Minasny⁴, Sébastien Vincent¹,

6 Christian Walter³ and Blandine Lemercier³

7

8 ¹UMR SAS, INRA, AGROCAMPUS OUEST 35000 Rennes, France

9

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11 ²CSIRO, Agriculture and Food, Canberra, ACT, Australia

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13 ³UMR SAS, AGROCAMPUS OUEST, INRA 35000 Rennes, France

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16 ⁴Sydney Institute of Agriculture, School of Life and Environmental Sciences, The University of

17 Sydney, NSW, Australia

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21 **Corresponding Author:** Yosra Ellili

22 **Corresponding Author's Institution:** UMR SAS, INRA, AGROCAMPUS OUEST 35000

23 Rennes, France

24 **Corresponding Author's contact** (email) yousraellili91@gmail.com

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26

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32
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33 **Abstract:**

34 Enhancing the spatial resolution of pedological information is a great challenge in the field of Digital Soil 35 Mapping (DSM). Several techniques have emerged to disaggregate conventional soil maps initially 36 available at coarser spatial resolution than required for solving environmental and agricultural issues. At the 37 regional level, polygon maps represent soil cover as a tessellation of polygons defining Soil Map Units 38 (SMU), where each SMU can include one or several Soil Type Units (STU) with given proportions derived 39 from expert knowledge. Such polygon maps can be disaggregated at finer spatial resolution by machine 40 learning algorithms using the Disaggregation and Harmonisation of Soil Map Units Through Resampled 41 Classification Trees (DSMART) algorithm. This study aimed to compare three approaches of spatial 42 disaggregation of legacy soil maps based on DSMART decision trees to test the hypothesis that the 43 disaggregation of soil landscape distribution rules may improve the accuracy of the resulting soil maps. 44 Overall, two modified DSMART algorithm (DSMART with extra soil profiles, DSMART with soil 45 landscape relationships) and the original DSMART algorithm were tested. The quality of disaggregated soil 46 maps at 50 m resolution was assessed over a large study area (6,775 km²) using an external validation based 47 on independent 135 soil profiles selected by probability sampling, 755 legacy soil profiles and existing 48 detailed 1:25,000 soil maps. Pairwise comparisons were also performed, using Shannon entropy measure, 49 to spatially locate differences between disaggregated maps. The main results show that adding soil landscape 50 relationships in the disaggregation process enhances the performance of prediction of soil type distribution. 51 Considering the three most probable STU and using 135 independent soil profiles, the overall accuracy 52 measures are: 19.8 % for DSMART with expert rules against 18.1 % for the original DSMART and 16.9 % 53 for DSMART with extra soil profiles. These measures were almost twofold higher when validated using 54 3x3 windows. They achieved 28.5% for DSMART with soil landscape relationships, 25.3% and 21% for 55 original DSMART and DSMART with extra soil observations, respectively. In general, adding soil 56 landscape relationships as well as extra soil observations constraints the model to predict a specific STU 57 that can occur in specific environmental conditions. Thus, including global soil landscape expert rules in 58 the DSMART algorithm is crucial to obtain consistent soil maps with clear internal disaggregation of SMU 59 across the landscape.

60 **Key words:** digital soil mapping, soil landscape relationships, spatial disaggregation, DSMART

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64
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65 1) Introduction

66 Characterizing soil variability especially over large areas, remains a crucial challenge to foster
67 sustainable management of agronomic and environmental issues and help stakeholders to design
68 regional projects (Chaney et al., 2016). At the regional as well as country level, soil maps are often
69 available at coarse spatial resolution (Bui and Moran, 2001) which limits their ability to depict
70 accurate soil information. For instance, the finest soils maps covering France were elaborated by
71 administrative region at 1:250,000 scale, via a set of polygons, called Soil Map Units (SMU) with
72 crisp boundaries. The delineation of SMU is based on soil survey programmes involving
73 pedologists' expertise. In a coarse scale map, each polygon includes one or several Soil Type Unit
74 (STU), which are not explicitly mapped, but their proportions and their environmental conditions,
75 as well as soil characteristics, are provided in a detailed database (Le Bris et al., 2013).

76 To improve soil variability knowledge and overcome the limitation of a coarse mapping scale,
77 several methods have emerged in the field of Digital Soil Mapping (DSM). These methods offer
78 useful tools to predict soil spatial pattern from scarce or limited soil datasets by exploiting the
79 availability of model based methods and an extensive array of spatialise (and more often than not
80 gridded) environmental variables. In recent decades, DSM techniques have been increasingly used
81 to downscale soil information and improve their spatial resolution. Depending on the quality of
82 data and the complexity of soil cover, Minasny and McBratney (2010) supply a workflow that
83 outlines different models that can be explored. In general, two main pathways can be distinguished:
84 point based DSM approaches and map disaggregation approaches (Odgers et al., 2014; Holmes et
85 al., 2015). Point DSM approaches used legacy soil profiles, which are irregularly distributed and
86 collected according to specific objectives rather than to optimise a statistical criterion (Holmes et
87 al., 2015). The spatial distribution of soil properties can be estimated by fitting geostatistical
88 models such as ordinary kriging (Odgers et al., 2014; Holmes et al., 2015; Chaney et al., 2016;
89 Vincent et al., 2018; Chen et al., 2018) or cokriging, which takes into account the spatial
90 interrelations among several soil properties (Webster and Oliver, 2007). Additionally, McBratney
91 et al. (2003) developed the SCORPAN soil landscape model. It is an empirical quantitative function
92 of environmental covariates, allowing predicting soil attributes (soil type or soil property) based
93 on correlative and statistical relationships with predictor variables.
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The second approach, known as spatial disaggregation, attempts to downscale the soil map unit information to delineate unmapped STUs (Bui and Moran, 2001; Odgers et al., 2014; Holmes et al., 2015). Alternatively, it can be defined as the process that allows estimating soil properties at a finer scale than the initial soil map. Several techniques have been demonstrated through soil science literature and tested in different case studies around the world. For instance, Kempen et al. (2009) have explored the use of multinomial logistic regression (MLR) for digital soil mapping. Other techniques have also been applied as decision trees using rule based induction (Bui and Moran, 2001), Bayesian techniques (Bui et al., 1999) and an area to point kriging method (Kerry et al., 2012).

In the DSM field, machine learning techniques are increasingly used to elucidate the spatial distribution of both soil type and soil properties across a large range of scale (Bui and Moran, 2001; Scull et al., 2005; Lacoste et al., 2011; Lemercier et al., 2012; Nauman and Thompson, 2014; Holmes et al., 2015; Vaysse and Lagacherie, 2015; Ellili et al., 2019). They were also applied to disaggregate superficial geology maps available at 1: 250 000 scale in Australia (Bui and Moran, 2001). The main advantage of these approaches is they allow handling both quantitative and categorical (ordinal or nominal) soil and environmental variables, as explanatory covariates (Bui and Moran, 2001).

Odgers et al. (2014) have developed a machine learning algorithm entitled Disaggregation and Harmonisation of Soil Map Units Through Resampled Classification Trees (DSMART) to predict STU as a function of the high resolution environmental data supplied over different study areas in Australia. The DSMART algorithm is based on a calibration dataset derived from a random selection of a fixed number of sampling points within each soil polygon. Each sampling point is then assigned to one soil type following a weighted random allocation procedure based on the proportions informed in the soil map database. The same procedure was applied by Chaney et al. (2016) to spatially disaggregate the soil map of the contiguous United States at a 30m spatial resolution using petascale High Performance Computer (HPC). Because integration of pedological knowledge has been recognized as an effective way to improve digital soil mapping approaches (Cook et al., 1996; Walter et al., 2006; Stoorvogel et al., 2017; Machado et al., 2018; Møller et al., 2019), Vincent et al. (2018) have applied the DSMART algorithm with additional expert soil landscape rules describing soil distribution in the local context of the Brittany region (France). By
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adding supplement sampling points to the calibration dataset selected according to soil parent material, soil redoximorphic conditions and topographic features, and by integrating soil landscape relationships in the DSMART sample allocation scheme, the authors obtained a coherent soil spatial distribution observing soil organisation along hillslopes and occurrence of intensely waterlogged soils in the stream neighbourhood, as observed in Brittany.

This study aimed to test the hypothesis that adding soil landscape relationships in the disaggregation procedure improved the accuracy of produced disaggregated soil maps. This involves assessing the contribution of soil landscape relationships implemented in the DSMART algorithm by Vincent et al. (2018). To achieve this objective, we compared disaggregated soil maps either derived from the original DSMART algorithm, the DSMART algorithm with extra soil observations and the DSMART algorithm fed by soil landscape relationships over an area of 6,775 km² in the eastern part of Brittany, France.

## 2) Materials and methods

### 2.1) Study area

The Ille et Vilaine department covers an area of 6,775 km² and is located at the eastern part of Brittany, France (48°N, 2° W) (Fig 1). It is drained by the rivers Ille and Vilaine and their tributaries. Its climate is oceanic, with a mean annual rainfall of 669 mm and mean annual temperature of 11.3° (Source: Climate Data EU). Main land uses comprise arable land, temporary and permanent grasslands, woodland, and urban areas. In the present study, anthropogenic areas were not considered. Elevation ranges between 0_20 m in the coastal zone and 20_150 m almost everywhere expect in the western part of the department where it tills 256 m. The topography is generally gentle with maximum slopes not exceeding 16%. The Ille et Vilaine department is part of the Armorican Massif with complex geology (BRGM, 2009): intrusive rocks (granite, gneiss and micaschist) in northern and north western zones, sedimentary rocks (sandstone) and metamorphic rocks (Brioverian schist) in the central and southern zones, and superficial deposits (Aeolian loam with decreasing thickness from north to south overlaying bedrock, alluvial and colluvium deposits). According to the World Reference Base of Soil Resources, soils occurring in Ille et Vilaine include Cambisols, Luvisols, Stagnic Fluvisols, Histosols, Podzols, and Leptosols (IUSS Working Group WRB, 2014).

### 2.2) Soil data
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2.2.1) Regional soil database at 1:250 000 scale

In Brittany, soils are represented through a regional geographic database called “Référentiel Régional Pédologique (RRP)” available at 1:250,000 scale (INRA Infosol, 2014). This regional database identifies soils within Soil Map Units (SMUs), each containing one to several soil types called Soil Type Units (STUs). STUs are defined as areas with homogeneous soil forming factors, such as morphology, geology, and climate. In the study area, 96 SMU and 171 STU have been distinguished and represented by a spatial coverage of 479 polygons.

In the regional database, SMUs were spatially delimited with crisp boundaries, while STUs were not explicitly mapped, but their proportion in each SMU as well as associated environmental and soil characteristics were accurately described in a semantic database (Le Bris et al., 2013; INRA Infosol, 2014).

2.2.2) Soil validation data

To assess the quality of disaggregated soil maps, three validation datasets were used (Fig. 1):

135 136 137 • 135 soil profiles chosen following a stratified random sampling design and specifically described and sampled from March to May 2017 for independent validation purposes in the framework of the Soilserv research project (Ellili et al., 2019, submitted).

140 141 142 • 755 legacy soil profiles collected between 2005 and 2008 during the “Sols de Bretagne” programme (INRA Infosol, 2014). These profiles were sampled to characterize hydromorphic soil conditions and soil landscape heterogeneity.

145 146 147 Existing detailed soil maps (1:25,000) covering 87,150 ha, surveyed according to Rivière et al. (1992) and revised later to adapt to the STU typologies developed in the RRP (Le Bris et al., 2013).

152 153 154 All soil profiles were allocated after description and analysis by an expert to a suitable STU. Both legacy soil profiles and detailed maps were converted to raster format to perfectly meet the prediction raster at 50m spatial resolution.

2.3) Environmental covariates

160 161 162 The SCORPAN concept (McBratney et al., 2013) allows one to predict STU as a function of a set of covariates describing seven soil forming factors, namely soil properties (s), climate (c), organisms (o), relief (r), parent material (p), age (a) and geographic position (n). In this study, ten
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183 environmental variables (Table 1) were considered as covariates in the disaggregation process at a 50m spatial resolution. Terrain attributes included elevation, slope, Compound Topographic Index (CTI) (Beven and Kirkby, 1979, Merot et al., 1995) and Topographic Position Index (TPI) (Vincent et al., 2018) that together were derived from a 50m resolution Digital Elevation Model (IGN, 2008).
184
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186 These attributes were computed using ArcGIS 10.1 (ESRI, 2002) and MNT surf software (Squividant, 1994).
187
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189 Environmental attributes describing soil parent material (Lacoste et al., 2011) and hydromorphic soil conditions via waterlogging index (Lemercier et al., 2013) were obtained using decision tree methods. Waterlogging index derives from a natural soil drainage prediction. Four classes were distinguished: well drained, moderately drained, poorly drained and very poorly drained. Aeolian silt deposits and Soil Map Units boundaries are environmental covariates also obtained via expert knowledge from soil scientists.
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195 Landscape units reflecting vegetation, land use, and relief attributes were derived from a MODIS imagery by supervised classification (Le Du Bayo et al., 2008). The Airborne gamma ray spectrometry variable (K:Th ratio) (Messner, 2008), characterizing the degree of weathering of the geological material, was also taken into account.
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199 All soil environmental covariates were converted to raster format at 50 m spatial resolution.

200 2.4) Disaggregation procedure: DSMART algorithm

201 2.4.1) Original DSMART algorithm (Method 1)

202 The open source DSMART algorithm (Odgers et al., 2014) was applied to spatially disaggregate
203 the existing legacy soil map at 1:250,000 scale. DSMART algorithm uses machine learning
204 classification trees implemented in C5.0 (Quinlan, 1993) to build a decision tree from a target
205 variable (STU) and the environmental covariates supplied. The DSMART algorithm was written
206 in the Python programming language by Odgers et al. (2014) and was recently translated in the R
207 programming language.
208

208 Running DSMART algorithm requires four main steps (Fig. 2):
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209

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1) Polygon sampling by a random selection of a fixed number of sampling points (n=30) within each polygon. This procedure allowed to select a total of 14,370 sampling points, per iteration, covering the study area and ensured that all polygons were sampled.

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2) Soil Type Unit (STU) assignment to each sampling point following a weighted random allocation method. This step was based on the proportion of each STU informed in the RRP database.

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3) Decision tree generation: the full set of sampling points were spatially intersected with the selected environmental covariates. This georeferenced dataset was then used as a calibration dataset to build the decision tree allowing the prediction of an STU as a function of environmental covariates. C5.0 created explicit models, which were applied to the covariates rasters to generate a realisation of STU distribution over the study area at 50 m resolution.

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222

These three steps were repeated 100 times to generate 100 realisations of the potential soil type distribution over the study area at 50 m of resolution.

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4) Computing the probabilities of occurrence: the 100 realisations were stacked to calculate the probability of occurrence of each predicted STU by counting the frequency of each STU at each pixel. This procedure led to a set of 171 rasters depicting the probability of occurrence of 171 STU.

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228

2.4.2) Original DSMART algorithm + soil observations (method 2)

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236

This disaggregation approach is similar to the original DSMART algorithm. However, the main difference is that 755 additional soil profiles, spatially collocated, were added to the calibration dataset to build decision trees. These soil profiles make it possible to incorporate real field observations with established soil landscape relationships. For each realisation, a calibration dataset (15, 125 samples) including virtual samples randomly selected from polygon units, as well as soil observations were used to model soil type with environmental covariates. The model was then extrapolated over the study area.

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238

2.4.3) Original DSMART algorithm + expert rules (Method 3)
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239 Including soil landscape relationships in the disaggregation process was explored by Vincent et al. (2018) in a specific regional pedoclimatic context in Brittany (France). Expert soil landscape relationships were used to assign STU to sampling points. These relationships were based on expert pedological knowledge, which takes into account soil parental material as well as topography and waterlogging in the UTS allocation procedure. This approach combines two sources of the dataset to calibrate the model. The first one was derived from semantic information for each SMU/STU combination. It consists in attributing a barcode to each SMU/STU combination, derived from a concatenation of four features contained in the RRP database (parent material, SMU identifier, TPI and waterlogging index), and to compare these barcodes to a stack of regional covariates representing the same four features, to assign each pixel of the study area to a suitable STU. This procedure allowed matching soil exhibiting specific features with their potential spatial distribution. For instance, hydromorphic soils occur with slope sequences and valley positions, while well drained soils occur in upslope or middle slope positions. Using a random sampling stratified by SMU's area, a set of sampling points was selected with a proportion of one sample for every 5 hectares and a minimum of five samples per polygon unit.

245 The second dataset was derived from a random sampling of a fixed number of sampling points in each polygon unit. This procedure ensured that all polygons had been sampled. STU allocation was based on the soil map unit proportions. The full set of each realisation (18, 320 samples) combining expert calibration dataset as well as dataset derived from random sampling procedure was spatially intersected with existing environmental covariates and used as a unique calibration dataset to build decision trees.

260

261 2.4.4) Prediction of the most probable STUs

262 From all soil type probability rasters obtained, only the three most probable STUs (with the highest probability of occurrence) were considered: for each pixel, the final prediction was the combination of the three most probable predicted STUs (1st STU, 2nd STU, and 3rd STU) and their associated probability of occurrence.

266 The classification confusion index (CI) between the first most probable STU and the second most probable STU was calculated following Eq.1:

268 $CI = 1 - (P_{1st STU} - P_{2nd STU})$ [1]
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269 Where $P_1^{st STU}$ and $P_2^{sd STU}$ denote respectively the highest probability of occurrence for 1st STU and the second highest probability of occurrence for 2nd STU, calculated at each pixel (Burrough et al., 1997; Odgers et al., 2014).

270
271 This index was considered as an indicator of certainty assessment about the most probable predicted soil class and is ranging between 0 and 1. It tends to 1. When the 1st STU and 2nd STU are predicted with similar probability of occurrence and zero when the probability of occurrence of the 2nd STU is close to zero.

276

277 2.5) Validation of disaggregated soil maps

278 The quality of soil maps resulting from the three DSMART algorithm based approaches was assessed by combining both spatial and semantical validation methods. Spatial validation is divided into 2 sub approaches (“pixel to pixel” and “window of 3x3 pixels”). For detailed soil maps and accurate soil profiles, “pixel to pixel” validation consists in checking, at each pixel, if the predicted STU respects the observed STU value (Heung et al., 2014; Nauman et al., 2014; Chaney et al., 2016; Møller et al., 2019). The “window of 3x3 pixels” validation assumes that, for each pixel, the predicted STU respects the observed STU value if it matches at least one of its 9 surrounding neighbours (Heung et al., 2014; Chaney et al., 2016). This method provides some flexibility by compensating spatial referencing error of soil maps and avoids the impact of fine scale spatial noise.

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287 The semantical validation was also performed considering either each STU or a group of STUs sorted by expert on the basis of similar pedogenesis factors and similar diagnostic horizons (Vincent et al., 2018; Møller et al., 2019). From the initial 171 STUs described in the soil database, the sorting procedure led to 78 groups and 11 STU remained single.

292

293 2.6) Pair wise comparisons of disaggregated soil maps

294 To compare the soil type rasters derived from the three DSMART based approaches, pairwise comparisons were performed using *Vmeasure* method implemented as open source software in an R package called Spatial Association Between REgionalisations (SABRE) (Rosenberg and Hirschberg, 2007). This is a spatial method developed to compare maps in the form of vector objects and it was commonly used in computer science to compare (non spatial) clustering.

298
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299 We divide the entire study area into 2 different sets of regions, referred to as regionalizations R and Z. The first regionalization R divides the domain into n regions $r_i$ (i=1 to n) and the second regionalization Z divides the domain into m zones $z_j$ (j=1 to m). Superposition of the 2 regionalization R and Z divides the domain into n x m segments having $a_{ij}$ area. The total area of a region $r_i$ is $A_i = \sum_{j=1}^{m} a_{ij}$, the total area of a zone $z_j$ is $A_j = \sum_{i=1}^{n} a_{ij}$ and the total of the domain is $A = \sum_{j=1}^{m} \sum_{i=1}^{n} a_{ij}$.

305 The SABRE package calculates a degree of spatial agreement between two regionalizations using an information theoretical measure called the V measure. V measure provides two intermediate metrics: homogeneity and completeness. Homogeneity is a measure of how well regions from the first map fit inside zones from the second map (Eq 2). Completeness measures how well zones from the second map fit inside regions from the first map (Eq 5). The final value of V measure is calculated as the weighted harmonic mean of homogeneity and completeness (Eq 8). All metrics range between 0 and 1, where larger values indicate better spatial agreement. V measure, homogeneity, and completeness are global measures of association between the two regionalizations.

314 Additional indicators of disaggregation quality were calculated using Shannon entropy index of regions and zones (Shannon 1948; Nowosad and Stepinskie, 2018). These indicators qualify local associations by highlighting the region's inhomogeneities (Eq 3, Eq 4), or zone's inhomogeneities (Eq 6, Eq 7). Two normalized Shannon entropy was also computed using the ratios ($S_j^R/S^R$) and ($S_i^Z/S^Z$) to derive maps of local spatial agreement between the two regionalizations R and Z. These measures have a range between 0 and 1.

320 When $S_j^R$ (Eq 3) is close to zero, this denotes that the zone j is homogenous in terms of regions (each zone is within a single region). However, when $S_j^R$ value increases the zone is increasingly inhomogeneous in terms of regions (it overlays an increasing number of regions). Therefore, $S_j^R$ (Eq 3) assesses the degree of this inhomogeneity or a variance of region in zone j. A global indicator that measures a homogeneity of a given zone in terms of regions is given via Eq 2.

325 Analogous to homogeneity but with the roles of regions and zones reversed, the dispersion of zones over the entire area is also computed using Shannon entropy (Eq 4 and Eq 7), and a global indicator C (Eq 5) measures a homogeneity of a given region in terms of zones.
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328 $h = 1 - \sum_{j=1}^{m} \left( \frac{A_j}{A} \right) \left( \frac{\text{Variance of regions in zone}_j = S_j^R}{\text{Variance of regions in the domain} = S^R} \right)$ [2]

329 $S_j^R = - \sum_{i=1}^{n} \left( \frac{a_{i,j}}{A_j} \right) \log \left( \frac{a_{i,j}}{A_j} \right)$ [3]

330 $S^R = - \sum_{i=1}^{n} \left( \frac{A_i}{A} \right) \log \left( \frac{A_i}{A} \right)$ [4]

331 $c = 1 - \sum_{i=1}^{n} \left( \frac{A_i}{A} \right) \left( \frac{\text{Variance of zones in region}_i = S_i^Z}{\text{Variance of zones in the domain} = S^Z} \right)$ [5]

332 $S_i^Z = - \sum_{j=1}^{m} \left( \frac{a_{i,j}}{A_i} \right) \log \left( \frac{a_{i,j}}{A_i} \right)$ [6]

333 $S^Z = - \sum_{j=1}^{m} \left( \frac{A_j}{A} \right) \log \left( \frac{A_j}{A} \right)$ [7]

334 $V_\beta = \frac{(1+\beta)hc}{(\beta h) + c}$ [8]

335 $\beta$ is a coefficient that allows promoting the first or the second regionalization, and by default, $\beta$ equals 1. $V_\beta$ has a range between 0 and 1. It equals 0 in case of no spatial association and 1 in case of perfect association.

338 The *V measure* method was applied in two main situations (DSMART+expert rules, Original DSMART) and (DSMART + expert rules, DSMART+extra soil observations). The reference map is always the map derived from DSMART algorithm with expert soil landscape relationships.

341 3) Results

342 3.1) Disaggregated soil maps

343 Applying DSMART based approaches yielded a set of soil maps and associated probability of occurrence rasters. The original DSMART approach allowed to disaggregate the 96 SMUs into 108 STUs while DSMART with expert rules approach yielded 158 STUs and DSMART with extra soil observations approach yielded 172 STUs with respect to the first most probable STU map. A total of 171 STUs were identified in the Ille et Vilaine department within the RRP database. Unpredicted STUs correspond mainly to rare STUs with low proportions ranging between 2 and 10% within the SMUs containing them.

350 Figure 3 shows the three maps of the 1st most probable STU derived from each approach as well as the original soil map. Overall, the three most probable STUs maps captured the main pattern of
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soil distribution of the coarse soil map. As one could expect according to the geological parent material map (Lacoste et al., 2011), extensive areas of deep silty soils are developed in Aeolian loam deposits encountered in the north east as well as in the north central parts of the study area. Colluvial and alluvial soils were mainly predicted in the north coast part and large valleys zones.

The visual comparison of disaggregated soil maps highlighted global similarities in the soil spatial distribution markedly affected by SMU boundaries. The three approaches distinguished very well soils developed in marsh parent material in the coastal part (north) of the study area. However, DSMART with soil landscape expert rules map as well as DSMART with extra soil observations map remained more detailed and underlined a clear internal disaggregation of SMUs especially in the north and the central parts of the Ille et Vilaine department. Visual inspection of the obtained DSMART with extra soil observations map as well as DSMART with expert rules map showed an increase in soil heterogeneity when compared to Original DSMART map. More importantly, legacy soil profiles made it possible to take into account some rare soil types with low probability to be predicted. Therefore, adding supplement sampling points via the expert calibration dataset and the 755 extra soil profiles allowed to predict STUs characterized in the soil database with a low spatial extent. Nevertheless, the three DSMART based approaches spatially disaggregated the most frequent components disregarding the less frequent ones.

Figure 4 shows maps of the global probability of redoximorphic soils across the study area. STU probability rasters, depicting hydromorphic soils, were added together to produce continuous maps of hydromorphic soil probability. Visual inspection of three maps highlighted global similarities, but local differences were recorded along the hydrographic network and in the southern part of the study area. As could be expected, DSMART with expert rules well predicted hydromorphic soils in valleys and coastal areas, with a probability of occurrence exceeding 80%. Adding soil landscape relationships in the allocation process constrained hydromorphic soil predictions in specific landscape positions. The same trend characterized DSMART with extra soil observations map, particularly in the central part of the study area. Therefore, including 755 soil profiles had an important role in the disaggregation process in the northern and the central parts where these profiles were located.

The quality of maps resulting from DSMART based approaches was quantified via the probabilities of occurrence of each STU predicted and the confusion index maps (Fig. 5). The latter measure
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382 indicated areas where the probability of occurrence of the two most probable soil types was close.

383 Over the study area, the average probability of occurrence of the most probable soil type achieved

384 respectively 0.41 for DMSART map, 0.68 for DMSART with expert rules and 0.28 for DSMART

385 with extra soil observations maps. Meanwhile, the average confusion index reached 0.8 for the

386 original DSMART approach while DSMART with extra soil observations and DSMART with

387 expert rules achieved 0.9 and 0.43, respectively. Although the most probable soil classes provide

388 plausible maps of soil distribution, there is a significant prediction uncertainty as depicted by these

389 measures.

390 In regions where disaggregated soil maps showed low confusion index, particularly in northwest

391 and north coast areas of Ille et Vilaine department, high confidence in predictions was recorded.

392 These areas were predominantly deep loamy soils or developed in alluvial and colluvium deposits.

393 Figure 6 compares the cumulative area of the STUs estimated from the three disaggregated maps

394 and that derived from the regional soil database. For each STU, its relative predicted area was

395 estimated by counting the number of pixels where it was predicted. For the regional soil database,

396 each STU area was computed from total SMU area multiplied by the proportion of the STU. This

397 comparison shows that some STUs were overestimated by the disaggregation approaches when

398 comparing to the soil database. DSMART with extra soil observations and original approaches

399 showed similar cumulative STU areas under the curve whereas DSMART with expert rules had a

400 shape similar to the regional soil database.

401 The most abundant STU in the database (431: Stagnic Fluvisol developed from alluvial and

402 colluvium deposits) was predicted as the most frequent STU by DSMART with extra soil

403 observations and DSMART with expert rules, and it was predicted as the second most abundant

404 STU by the original DSMART algorithm. The 10 most abundant STUs in the soil database covers

405 almost 43% of the study area. Of them, 7 belong to the 10 STU most predicted by the three

406 disaggregation approaches (Table 2).

407 3.2) Covariates importance in the decision trees

408 Figure 7 gives the relative importance of the covariates used in DSMART based approaches. Soil

409 parent material and SMU boundaries were used systematically in condition rules regardless of the

410 disaggregation method. This was consistent with the contrasting pattern of geology and the
<figure><img src="image_27.png" /></figure>

<figure><img src="image_28.png" /></figure>

dependence relationship between SMU and its soil components. Considering the original DSMART approach (Fig. 7.a), distribution functions of Aeolian silt deposits, airborne gamma ray spectrometry variable (K:Th ratio) and elevation contributions were more dispersed according to the STU considered than those of other covariates. For instance, Aeolian silt deposits contribution varied between 20 and 80% with a median value of 42%, whereas slope contribution ranged between 20 and 40 % with a median value of 28%. Aeolian silt deposits have an important weight in STU predictions, due to its ability to represent soils inherited from this superficial parent material, which is poorly represented in lithological maps.

DSSMART with soil landscape relationships (Fig. 7.b) showed almost the same distribution function of all covariates except for elevation where its distribution function was more dispersed. Since a part of training samples was chosen with expert knowledge based on three environmental covariates: TPI, a waterlogging index and soil parent material, we would expect the prominent role of waterlogging index and TPI to constrain hydromorphic soils predictions and to achieve STU distribution in the appropriate order along the toposequence. This most likely explains the dominance of Fluvisol Stagnic in valleys areas followed by a transition to Cambisols commonly found at upslope and midslope positions along the toposequences.

Analogous to the original DSMART algorithm, DSMART with extra soil observations (Fig. 7.c) highlighted almost the same distribution of use of soil environmental covariates in the decision trees, except for aeolian silt deposits, K:Th ratio and elevation. The latter covariates contributions remained less dispersed compared to the original DSMART approach.

### 3.3) Validation of disaggregated soil maps

The validation procedure was performed for each DSMART based approach applied, considering the three most probable soil types and using both semantic objects (STU or soil group) and spatial neighbourhood (per pixel or 3x3 window of pixels).

Considering 755 legacy soil profiles prospected in the framework of “Sols de Bretagne” project, per pixel validation accuracy reached 27%, for original DSMART maps and 34 % for DSMART with expert rules (Table 3). A similar comparison using 135 validation sites derived from Soilserv project showed that 18.1 % of soil profiles match DSMART maps, 19.8 % match DSMART with expert rules maps and only 16.9 % match DSMART with extra soil observations maps (Table 3).
<figure><img src="image_29.png" /></figure>

<figure><img src="image_30.png" /></figure>

440 Using a 3 x 3 window of pixels markedly improves the global accuracies, which increased for the
441 two validation datasets (Table 3). DSMART with soil landscape relationships remained the best
442 performing method.

443 When compared to accurate soil maps (1:25,000), the validation procedure showed that DSMART
444 with extra soil observations as well as DSMART with soil landscape expert rules had almost the
445 same performance (37% and 38%) while best accuracy (44%) was observed for Original DSMART
446 maps (44%) (Table 3). These scores were clearly improved by considering soil groups and 3x3
447 pixels neighbourhood. For instance, the accuracy of DSMART with expert rules maps using soil
448 group reached 45.9% and increased to 62.1 % when considering 3x3 pixels windows (Table 3).

449 3.4) Comparing disaggregated maps

450 Figure 8 shows inhomogeneity maps measured by Shannon entropy. The map derived from
451 DSMART with soil landscape relationships was chosen as a reference map. This map deeply
452 disaggregates the initial SMUs into 120,653 regions with irregular shapes. By contrast, Original
453 DSMART map remained very similar to the original map and delineated the study into 40,459
454 regions. Both disaggregated maps reflect the main pattern of soil distribution over the study area
455 despite the difference in the disaggregation process. Visual inspection of maps DSMART with soil
456 landscape rules map and Original DSMART map revealed an overall similarity between
457 disaggregated maps, but local differences between them were depicted.

458 We calculated h₁= 0.49, c₁ = 0.58 and V₁ = 0.53 as global measures of spatial agreement between
459 the two maps (DSMART+expert rules and Original DSMART). The average homogeneity of the
460 DSMART with soil landscape rules map with respect to the Original DSMART map was qualified
461 via *h* homogeneity index. Similarly, the average homogeneity of the Original DSMART map with
462 respect to the DSMART with soil landscape rules map was qualified via *c* completeness index.
463 Visually, the Fig. 8.b map seemed to be more homogeneous than the map Fig. 8.a in agreement
464 with the statistical assessment *c* > *h*. The large number of DSMART with soil landscape rules map
465 regions, which was three times higher than Original DSMART map zones, might explain this
466 difference. It is more likely that DSMART with soil landscape rules map regions cross through
467 multiple Original DSMART map zones than vice versa. However, two disaggregated maps
468 remained spatially associated according to the high *V*₁ score. The two inhomogeneity maps (Figs.
<figure><img src="image_31.png" /></figure>

<figure><img src="image_32.png" /></figure>

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8a and 8b) highlighted the locations of greatest differences between two maps, mainly along the hydrographic network.

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## 4) Discussion

### 4.1) Performance of the disaggregation procedures

Produced disaggregated soil maps closely resemble the abundant soils in the original soil map (Holmes et al., 2015; Fig.3). The 1st most probable STU map derived from DSMART based approaches captured the main spatial pattern of soil distribution across the study area. More internal variation within SMUs was found when using DSMART with added point observations and DSMART with soil landscape relationships. Local soil heterogeneity reflecting inherent pedological complexity was depicted by the 1st STU maps which deliver a deterministic soil landscape distribution, continuously varying with landscape features.

External validation was performed to assess the quality of disaggregated soil maps. Using 135 independent soil profiles and a per pixel validation approach, the overall accuracy reached 18.1% for DSMART algorithm 1st STU map, 19.8% for DSMART with expert rules 1st STU map and
<figure><img src="image_33.png" /></figure>

<figure><img src="image_34.png" /></figure>

16.9% for DSMART with extra soil profiles 1st STU map. In the DSM literature, researchers who applied classification tree decision methods founded similar validation results. For instance, by applying DSMART algorithm in eastern Australia and using 285 legacy soil profiles, Odgers et al. (2014) achieved an overall accuracy of 23%. Similarly, Nauman and Thompson (2014) explored the use of expert rules for soil landscape relationships in the United States and achieved global accuracy ranged between 22% and 24%. Similar disaggregation performance was recorded by Holmes et al. (2015) in Western Australia (20%), Chaney et al. (2016) in the United States (17%) and Møller et al. (2019) in Denmark (18%) using DSMART algorithm (Table 4). In contrast to the latter studies, a large number of STU (171 STU) compose our soil dataset. This could certainly decrease the chance of predicting the right STU, even through mobilizing relevant geographic dataset to implement soil landscape relationships.

When considering a window of 3x3 pixels, the overall accuracy increased considerably for the three DSMART based approaches maps, but DSMART with expert soil landscape relationships achieved the highest accuracy scores. Chaney et al. (2016) highlighted a high degree of spatial noise in the predictions by including pixel validation neighbours. Overall, prediction accuracy increased twofold with a 3x3 pixel validation window and when grouping soils to a coarser level of soil classification (171 versus 89 soil group). This was recorded for all disaggregated maps regardless of the disaggregation procedure and suggests that fine soil taxonomic dissimilarities can not be accurately mapped by disaggregation processes.

## 4.2) Legacy soil data

Legacy soil data used in this study provide an overall representation of soil over large areas (1: 250,000 scale). This database was derived from several soil surveys and pedological expert knowledge. SMUs were spatially delineated, and their spatial organisation, as well as STUs features, were described according to available soil data and pedological expertise. STUs and their associated landscape characteristics were identified as accurately as possible using legacy soil profiles collected according to a not probabilistic sampling design between 1968 and 2012. Hence, differences in survey methods covering a large area over a long sampling period could lead to errors in the STU definition or uncertainties in the estimation of their area in a given SMU.
<figure><img src="image_35.png" /></figure>

<figure><img src="image_36.png" /></figure>

Moreover, soil survey intensity was not uniform within SMUs. Thus, SMU components may be derived from the unequal representation of soil samples across SMUs.

Harmonising soil data to reduce the number of STU is a great challenge by itself. Grouping some STUs regarding their pedological similarities such as sharing comparable morphological criteria, having similar pedogenic horizons and occurring in analogous environmental conditions is worthwhile to be investigated. More importantly, unifying soil data according to more functional aspects such as soil agricultural potential allows also to generate a relevant regional soil database easily handled by soil users to satisfy their needs. Many countries around the world have already harmonized their soil databases such as Denmark and Australia, where high pedological complexity was captured with a reasonable STU number, with not exceeding 23 soil groups in Denmark (Møller et al., 2019) and 73 soil groups in Australia (Holmes et al., 2015).

### 4.3) Taxonomic similarities

In the recent DSM literature, DSMART approach is considered as an efficient tool to disaggregate existing coarse soil maps. In this study, we compared variants of the DSMART based approach, which differed by the training dataset used to calibrate the C5.0 model and the allocation procedure.

Modified DSMART algorithms used additional calibration datasets derived from supplement soil observations and expert sampling of polygons. Hence, taxonomic similarities were not taken into account neither in the calibration process nor in the current component assignment scheme. Even if there is a large number of STUs addressing inherent soil landscape heterogeneity, there is most likely a short taxonomic distance between many of them. As a result, these STUs may have similar forming conditions, making it a challenge to suitably constrain the prediction probabilities using DSMART algorithm. This likely explains the high confusion index scores recorded in the present study, particularly for original DSMART and DSMART with extra soil profiles approaches. As demonstrated by Minasny and McBratney (2007), including taxonomic distance in decision trees using pedological knowledge is a relevant way to decrease the misclassification error. Therefore, future effort and improvements of the DSMART algorithm should take into account the taxonomic distance between STU in the disaggregation procedure.

### 4.4) Mapping comparison
<figure><img src="image_37.png" /></figure>

563

564 A quantitative comparison between disaggregated soil maps was performed using a novel approach
called *V measure* method. This method was commonly used to assess the spatial agreement
565 between land cover maps and thematic biotic and abiotic factors maps, as done by Nowosad and
566 Stepinski (2018) in the United States, but never before for soil maps.
567

568 In the present study, V₁ (0.53) was larger than V₂ (0.47) suggesting that DSMART with expert soil
569 landscape relationships map is much more similar to Original DSMART map than DSMART with
570 extra soil observations map. This might be explained by the allocation procedure for training
571 samples. The original DSMART algorithm tends to promote most abundant STUs with high
572 proportions of occurrence within polygons and penalized STUs with low proportions (comprise
573 between 2 and 10%). Therefore, frequent STUs are more likely to be predicted rather than rare
574 STUs. Meanwhile, by adding supplement soil profiles, preliminarily assigned to a suitable STU to
575 the training dataset, we constrain STUs with low proportions of occurrence predictions.
576

577 Major differences between DSMART with expert rules map and DMSART with soil observations
578 were mainly observed in the southern part of the study area and valleys areas. In general, Fluvisol
579 Stagnic soils were overestimated by DSMART with extra soil observations. This was likely due to
580 the purposive sampling design followed to supplement soil observations. The 755 legacy soil
581 profiles were selected to characterize hydromorphic soil conditions and to characterize inherent
582 soil landscape variability supposed to be organized along the hillslope.
583

583

4.5) Improvements and future work

584

585 Even though this work emphasizes the contribution of pedological knowledge in the disaggregation
586 process, other pathways can also be explored to improve map's accuracy. As recommended by
587 Mulder et al. (2016), compensating the temporal changes and differences in laboratory analytics is
588 a good option to improve the quality of legacy soil data. This suggests harmonising local soil
589 database and regrouping some STUs with similar soil forming factors through statistical modelling.
590 Moreover, additional environmental covariates with high spatial resolution should be used to
591 capture micro landscape variability (Lacoste et al., 2014; Odgers et al., 2014; Chaney et al., 2016;
592 Møller et al., 2019). For example, adding a more detailed Digital Elevation Model allowed to
593 capture small terrain features, where may be particular, STUs occurs. Improving both polygon
<figure><img src="image_38.png" /></figure>

<figure><img src="image_39.png" /></figure>

sampling procedure and current components assignment scheme turned out to be important to reduce uncertainty prediction. This suggests drawing virtual soil samples proportionally to polygons areas and using supplement STU characteristics based on surveyor observations (slope shape, hillslope position, soil texture ...) to guide STU allocation procedure (Møller et al., 2019). Assuming that the decision tree can be built to relate STU descriptors to legacy soil data, this method can replace weighted random allocation procedure and should help minor STU prediction by constraining raster probabilities.

## 5) Conclusion

We applied three DSMART based approaches, including original DSMART algorithm, DSMART with extra soil observations and DSMART with soil landscape relationships, to disaggregate legacy soil polygons over a large area in Brittany (France). Regardless of the disaggregation approach, the produced soil maps at 50 m spatial resolution successfully address the main soil spatial pattern regarding prior pedological knowledge of our study area. Performance assessed against 135 independent soil profiles, 755 legacy soil profiles, and accurate 1:25,000 soil maps highlighted that DSMART with expert rules maps achieved highest validation measures. Overall, modified DSMART algorithms allowed minor STUs prediction, whereas original DSMART algorithm promoted abundant STUs prediction with poor spatial structure improvement. Adding pedological knowledge as well as extra soil observations in the prediction process constrained STU probabilities, even STUs with low proportions. However, some particular STUs reflecting hydromorphic soils or loamy soils were greatly overestimated for all the three DSMART based approaches.

Soil maps produced using the original DSMART and DSMART with expert rules have a high spatial agreement, but the latter map appeared more detailed and provided a spatially continuous and consistent STU's prediction. Therefore, generalizing soil landscape relationships taken to account several STU descriptors and landscape features should be implemented in the future version of DSMART algorithm to capture soil landscape heterogeneity and consequently guarantee coherent variability of soil properties.

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625 Acknowledgments

626 The authors gratefully acknowledge all farmers at the Ille et Vilaine site involved in our research.

627 We thank the technical staff who actively participated in field sampling and laboratory analysis.

628 This research was performed in the framework of the INRA "Ecoserv" metaprogram. This work

629 was also supported by Sols de Bretagne project and Soilserv program funded by ANR (Agence

630 Nationale de la Recherche) (ANR-16-CE32-0005-01).

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655
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656 **Figure captions**

657

658 **Figure 1: Location of the study area and the validation datasets**

659 **Figure 2: Schematic of the DSMART based approaches algorithm. The steps in DSMART are: 1)**

660 construct the calibration dataset; 2) train C5.0 model; 3) estimate STU maps and their associated

661 probabilities of occurrence

662 **Figure 3: Digital soil map of the most probable STU and their associated probability of occurrence**

663 for the whole study area and for a focus zone, a) Legacy soil map: most probable STU for each

664 SMU, b) original DSMART approach; c) DSMART with expert rules; d) DSMART with extra soil

665 observations

666 **Figure 4: Global probability of hydromorphic soils over the study area derived from a) original**

667 DSMART, b) DSMART with soil landscape relationships and c) DSMART with extra soil

668 observations. The probabilities of the three STU with highest prediction occurrence are summed if

669 they are hydromorphic

670 **Figure 5: Confusion index maps for a) Original DSMART approach; b) DSMART with expert**

671 rules; c) DSMART with extra soil observations

672 **Figure 6: Cumulative area of the 171 STUs estimated from the regional soil database and predicted**

673 by different DSMART based approaches

674 **Figure 7: Violin plots of the relative importance of each environmental covariate used in a) Original**

675 DSMART approach; b) DSMART with expert rules; c) DSMART with extra soil observations

676 **Figure 8: Spatial association between disaggregated maps of Ille et Vilaine department. a) map of**

677 inhomogeneity of DSMART with soil landscape relationships map in terms of original DSMART

678 map b) map of inhomogeneity of original DSMART map in terms of DSMART with soil landscape

679 relationships map c) map of inhomogeneity of DSMART with soil landscape relationships map in

680 terms of DSMART with extra soil observations map d) map of inhomogeneity of DSMART with

681 extra soil observations map in terms of DSMART with soil landscape relationships map.

682 Inhomogeneity (variance) is measured by normalised Shannon entropy

683
<figure><img src="image_43.png" /></figure>

<figure><img src="image_44.png" /></figure>

684 Table headings

685

686 Table 1. Description of the environmental covariates selected. Summary of environmental
687 covariates. P: parent material; S: soil properties; R: relief; O: Organisms; C: categorical; Q:
688 quantitative.

689 Table 2. Ten most extended STUs according to the regional soil database and their respective rank
690 by area using three DSMART based disaggregation procedures

691 Table 3. Overall accuracies (%) obtained using various external validation approaches for the three
692 most probable STU

693 Table 4: Comparison between the size areas covered, number of soil map units, soil type units of
694 the original legacy soil maps and the accuracy achieved in other studies using DSMART algorithm

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<figure><img src="image_51.png" /></figure>

<figure><img src="image_52.png" /><figcaption>Figure 1: Location of the study area and the validation datasets</figcaption></figure>
<figure><img src="image_53.png" /><figcaption>DSMART based-approches for spatial disaggregation of the 1:250, 000 soil map</figcaption></figure>

<figure><img src="image_54.png" /><figcaption>**Figure 2:** Schematic of the DSMART based approaches algorithm. The steps in DSMART are: 1) construct the calibration dataset; 2) train C5.0 model; 3) estimate STU maps and their associated probabilities of occurrence</figcaption></figure>
<figure><img src="image_55.png" /></figure>

<figure><img src="image_56.png" /><figcaption>Figure 3: Digital soil map of the most probable STU and their associated probability of occurrence for the whole study area and for a focus zone, a) Legacy soil map: most probable STU for each SMU, b) original DSMART approach; c) DSMART with expert rules; d) DSMART with extra soil observations</figcaption></figure>
<figure><img src="image_57.png" /></figure>

<figure><img src="image_58.png" /><figcaption>Figure 4: Global probability of hydromorphic soils over the study area derived from a) original DSMART, b) DSMART with soil landscape relationships (and c) DSMART with extra soil observations. The probabilities of the three STU with highest prediction occurrence are summed if they are hydromorphic.</figcaption></figure>
<figure><img src="image_59.png" /></figure>

<figure><img src="image_60.png" /></figure>

<figure><img src="image_61.png" /><figcaption>Figure 5 Confusion index maps for a) Classic DSMART approach; b) DSMART with expert rules; c) DSMART with extra soil observations</figcaption></figure>
<figure><img src="image_62.png" /></figure>

<figure><img src="image_63.png" /></figure>

<figure><img src="image_64.png" /><figcaption>Figure 6: Cumulative area of the 171 STUs estimated from the regional soil database and predicted by different DSMART based approaches</figcaption></figure>
<figure><img src="image_65.png" /></figure>

<figure><img src="image_66.png" /><figcaption>Figure 7: Violin plots of the relative importance of each environmental covariate used in a) Original DSMART approach; b) DSMART with expert rules; c) DSMART with extra soil observations</figcaption></figure>
<figure><img src="image_67.png" /></figure>

<figure><img src="image_68.png" /><figcaption>**Figure 8:** Spatial association between disaggregated maps of Ille et Vilaine department. a) map of inhomogeneity of DSMART with soil landscape relationships map in terms of original DSMART map b) map of inhomogeneity of original DSMART map in terms of DSMART with soil landscape relationships map c) map of inhomogeneity of DSMART with soil landscape relationships map in terms of DSMART with extra soil observations map d) map of inhomogeneity of DSMART with extra soil observations map in terms of DSMART with soil landscape relationships map. Inhomogeneity (variance) is measured by normalised Shannon entropy</figcaption></figure>
<figure><img src="image_69.png" /><figcaption>Table 2 Ten most extended STUs according to the regional soil database and their respective rank by area using three DSMART based disaggregation procedures</figcaption></figure>

<table><thead><tr><th rowspan="2">STU</th><th rowspan="2">Label</th><th rowspan="2">WRB classification</th><th rowspan="2">Parent material</th><th colspan="2">1 :250, 000 dataset</th><th colspan="2">Original DSMART approach</th><th colspan="2">DSMART with extra soil profiles</th><th colspan="2">DSMART with expert rules</th></tr><tr><th>Rank</th><th>Estimated area (km²)</th><th>Rank</th><th>Predicted area (km²)</th><th>Rank</th><th>Predicted area (km²)</th><th>Rank</th><th>Predicted area (km²)</th></tr></thead><tbody><tr><td>431</td><td>Fluvisol</td><td>Alluvial and colluvial deposits</td><td>1</td><td>688</td><td>2</td><td>757</td><td>1</td><td>983</td><td>1</td><td>740</td><td>1</td></tr><tr><td>248</td><td>Cambisol</td><td>Brioverian schists</td><td>2</td><td>480</td><td>1</td><td>1154</td><td>2</td><td>461</td><td>2</td><td>492</td><td>2</td></tr><tr><td>51</td><td>Cambisol</td><td>Brioverian schists</td><td>3</td><td>402</td><td>5</td><td>397</td><td>4</td><td>395</td><td>3</td><td>424</td><td>4</td></tr><tr><td>61<sup>3</sup><sub>36</sub></td><td>Cambisol</td><td>Gritty schists</td><td>4</td><td>227</td><td>9</td><td>177</td><td>30</td><td>53</td><td>14</td><td>128</td><td>9</td></tr><tr><td>183</td><td>Cambisol</td><td>Sandstone</td><td>5</td><td>216</td><td>11</td><td>162</td><td>5</td><td>308</td><td>10</td><td>192</td><td>11</td></tr><tr><td>256</td><td>Cambisol</td><td>Aeolian loam</td><td>6</td><td>200</td><td>6</td><td>385</td><td>3</td><td>418</td><td>6</td><td>314</td><td>6</td></tr><tr><td>286</td><td>Cambisol</td><td>Brioverian schists</td><td>7</td><td>179</td><td>23</td><td>62</td><td>9</td><td>187</td><td>24</td><td>80</td><td>23</td></tr><tr><td>86</td><td>Cambisol</td><td>Brioverian schists</td><td>8</td><td>169</td><td>12</td><td>126</td><td>15</td><td>124</td><td>4</td><td>358</td><td>12</td></tr><tr><td>340</td><td>Albeluvisol</td><td>Granite and gneiss</td><td>9</td><td>168</td><td>7</td><td>347</td><td>10</td><td>177</td><td>11</td><td>189</td><td>7</td></tr><tr><td>54</td><td>Cambisol</td><td>Brioverian schists</td><td>10</td><td>167</td><td>4</td><td>451</td><td>18</td><td>98</td><td>5</td><td>324</td><td>4</td></tr></tbody></table>
<figure><img src="image_70.png" /><figcaption>Table 1. Description of the environmental covariates selected</figcaption></figure>

Summary of environmental covariates. P: parent material; S: soil properties; R: relief; O: Organisms; C: categorical; Q: quantitative.

<table><caption>Table 3. Overall accuracies (%) obtained using various external validation approaches for the three most probable STU</caption><thead><tr><th>Environmental covariate</th><th>SCORPAN factor</th><th>Type</th><th>Unit or number of classes</th></tr></thead><tbody><tr><td colspan="4"><strong>Terrain attributes derived from the digital elevation model</strong></td></tr><tr><td>Elevation</td><td>R</td><td>Q</td><td>m</td></tr><tr><td>Slope</td><td>R</td><td>Q</td><td>%</td></tr><tr><td>Compound Topographic Index (TPI)</td><td>R</td><td>Q</td><td>Log (m³)</td></tr><tr><td>Topographic Position Index</td><td>R</td><td>C</td><td>5 classes</td></tr><tr><td colspan="4"><strong>Pedology and geology</strong></td></tr><tr><td>Soil parent material</td><td>P</td><td>C</td><td>22 classes</td></tr><tr><td>Soil Map Units</td><td>R</td><td>C</td><td>96 classes</td></tr><tr><td>Aeolian silt deposits</td><td>P</td><td>C</td><td>2 classes</td></tr><tr><td>Waterlogging index</td><td>S</td><td>C</td><td>4 classes</td></tr><tr><td colspan="4"><strong>Organism</strong></td></tr><tr><td>Landscape units</td><td>O</td><td>C</td><td>19 classes</td></tr><tr><td colspan="4"><strong>Gamma ray spectrometry from 250 m airborne geophysical survey interpolations</strong></td></tr><tr><td>K:Th ratio</td><td>P</td><td>Q</td><td></td></tr></tbody></table>

<table><caption>Pixel to pixel validation of STU</caption><thead><tr><th></th><th>DSMART approach</th><th>Most probable STU</th><th>Second most probable STU</th><th>Third most probable STU</th><th>Total</th></tr></thead><tbody><tr><td rowspan="3">Soil maps (87 150 ha)</td><td>Original DSMART</td><td>23</td><td>13</td><td>8</td><td>44</td></tr><tr><td>DSMART with expert rules</td><td>19</td><td>11</td><td>7</td><td>37</td></tr><tr><td>DSMART with extra soil observations</td><td>22</td><td>9</td><td>7</td><td>38</td></tr><tr><td rowspan="3">Independent soil profiles (n=135)</td><td>Original DSMART</td><td>11</td><td>5</td><td>3.8</td><td>18.1</td></tr><tr><td>DSMART with expert rules</td><td>10</td><td>4.4</td><td>3.7</td><td>19.8</td></tr><tr><td>DSMART with extra soil observations</td><td>8.2</td><td>6</td><td>2.7</td><td>16.9</td></tr><tr><td rowspan="3">Legacy soil profiles (n=755)</td><td>Original DSMART</td><td>14</td><td>7</td><td>6</td><td>27</td></tr><tr><td>DSMART with expert rules</td><td>18</td><td>9</td><td>7</td><td>34</td></tr><tr><td>DSMART with extra soil observations</td><td></td><td></td><td></td><td></td></tr></tbody></table>
<figure><img src="image_71.png" /></figure>

Pixel to pixel validation of STU group

<table><thead><tr><th></th><th>DSMART approach</th><th>Most probable STU</th><th>Second most probable STU</th><th>Third most probable STU</th><th>Total</th></tr></thead><tbody><tr><td rowspan="3">Soil maps (87 150 ha)</td><td>Original DSMART</td><td>26</td><td>13</td><td>9</td><td>48</td></tr><tr><td>DSMART with expert rules</td><td>22.5</td><td>13.7</td><td>9.7</td><td>45.9</td></tr><tr><td>DSMART with extra soil observations</td><td>25</td><td>10</td><td>7</td><td>42</td></tr><tr><td rowspan="3">Independent soil profiles (n=135)</td><td>Original DSMART</td><td>16</td><td>7</td><td>4.6</td><td>27.6</td></tr><tr><td>DSMART with expert rules</td><td>18</td><td>8.4</td><td>5.2</td><td>31.6</td></tr><tr><td>DSMART with extra soil observations</td><td>15</td><td>8</td><td>3.8</td><td>26.8</td></tr><tr><td rowspan="3">Legacy soil profiles (n=755)</td><td>Original DSMART</td><td>19</td><td>12</td><td>9</td><td>40</td></tr><tr><td>DSMART with expert rules</td><td>23.4</td><td>15</td><td>11.8</td><td>50.2</td></tr><tr><td>DSMART with extra soil observations</td><td></td><td></td><td></td><td></td></tr></tbody></table>

Neighbourhood of 3 x 3 validation of STU

<table><thead><tr><th></th><th>DSMART approach</th><th>Most probable STU</th><th>Second most probable STU</th><th>Third most probable STU</th><th>Total</th></tr></thead><tbody><tr><td rowspan="3">Soil maps (87 150 ha)</td><td>Original DSMART</td><td>31</td><td>16</td><td>14</td><td>61</td></tr><tr><td>DSMART with expert rules</td><td>29.6</td><td>19.4</td><td>13.1</td><td>62.1</td></tr><tr><td>DSMART with extra soil observations</td><td>28</td><td>11</td><td>9</td><td>48</td></tr><tr><td rowspan="3">Independent soil profiles (n=135)</td><td>Original DSMART</td><td>15</td><td>6</td><td>4.3</td><td>25.3</td></tr><tr><td>DSMART with expert rules</td><td>17</td><td>6.7</td><td>4.8</td><td>28.5</td></tr><tr><td>DSMART with extra soil observations</td><td>11</td><td>7</td><td>3</td><td>21</td></tr><tr><td rowspan="3">Legacy soil profiles (n=755)</td><td>Original DSMART</td><td>19</td><td>10</td><td>7</td><td>36</td></tr><tr><td>DSMART with expert rules</td><td>27.9</td><td>15</td><td>11.9</td><td>54.8</td></tr><tr><td>DSMART with extra soil observations</td><td></td><td></td><td></td><td></td></tr></tbody></table>
<figure><img src="image_72.png" /></figure>

<figure><img src="image_73.png" /><figcaption>Table 4: Comparison between the size areas covered, number of soil map units, soil type units of the original legacy soil maps and the accuracy achieved in other studies using DSMART algorithm</figcaption></figure>

<table><thead><tr><td>Study</td><td>Area (km²)</td><td>Map units</td><td>Soil type unit</td><td>Accuracy</td></tr></thead><tbody><tr><td>Odgers et al. (2014)</td><td>68,000</td><td>1,110</td><td>72</td><td>23</td></tr><tr><td>Holmes et al. (2015)</td><td>2,500,000</td><td>5,069</td><td>73</td><td>20-22</td></tr><tr><td>Chaney et al. (2016)</td><td>-</td><td>-</td><td>-</td><td>17</td></tr><tr><td>Møller et al. (2019)</td><td>43,000</td><td>11-14</td><td>18-23</td><td>12-18</td></tr></tbody></table>