# Regional soil profile data reveals the predominant role of geomorphology and geology in accurately deriving digital soil texture maps in a tropical area

Salman Khan, Isabelle Maréchaux, Ghislain Vieilledent, Stéphane Guitet, Olivier Brunaux, Bruno Ferry, Florian Soulard, Clément Stahl, Christopher Baraloto, Claire Fortunel, et al.

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## To cite this version:

Salman Khan, Isabelle Maréchaux, Ghislain Vieilledent, Stéphane Guitet, Olivier Brunaux, et al. Regional soil profile data reveals the predominant role of geomorphology and geology in accurately deriving digital soil texture maps in a tropical area. 2025. hal-04981892

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Preprint submitted on 7 Mar 2025

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Distributed under a Creative Commons Attribution 4.0 International License
1 Regional soil profile data reveals the

2 predominant role of geomorphology and

3 geology in accurately deriving digital soil

4 texture maps in a tropical area

5

6 Salman Khan, Isabelle Maréchaux, Ghislain Vieilledent,

7 Stéphane Guitet, Olivier Brunaux, Bruno Ferry, Florian Soulard,

8 Clément Stahl, Christopher Baraloto, Claire Fortunel and

9 Vincent Freycon

10 Salman Khan <salman.khan@cirad.fr>, Isabelle Maréchaux

11 <isabelle.marechaux@inrae.fr>, Ghislain Vieilledent

12 <ghislain.vieilledent@cirad.fr>, Stéphane Guitet <stephane.guitet@onf.fr>,

13 Olivier Brunaux <olivier.brunaux@onf.fr>, Bruno Ferry

14 <bruno.ferry@agroparistech.fr>, Florian Soulard

15 <florian.soulard@inrae.fr>, Clément Stahl <clement.stahl@ecofog.gf>,

16 Christopher Baraloto <cbaralot@fiu.edu>, Claire Fortunel

17 <claire.fortunel@ird.fr>, and Vincent Freycon <vincent.freycon@cirad.fr>

18 Abstract

19 Accurate information related to soil texture is essential for

20 understanding key biological, chemical, and hydrological processes.

21 However, soil data is scarce and unevenly distributed, especially in
tropical regions, and global soil information products lack regional
assessments, leading to high uncertainty. Here, we leveraged an
unprecedented dataset of soil observations in a particularly poorly
documented region, French Guiana, to identify the drivers of soil
texture in such territory, develop digital soil maps of textural
components, and provide an independent assessment of existing soil
products at a regional scale. Specifically, we employed the random
forest (RF) model to predict sand, clay and silt contents from multiple
environmental variables describing geology, climate, and
geomorphology. Results were evaluated through k-fold random and
spatial cross-validation. We used our model to derive a soil texture
map for French Guiana. We tested our map and global soil texture
maps from the Harmonized World Soil Database (HWSD) and
SoilGrids against 72 independent soil profiles collected in the region.
Geomorphology and geology were the most important predictors of
sand, clay, and silt contents in our model, yielding relatively good
predictions (random cross-validation: R² = 0.54, 0.76, and 0.10; spatial
cross-validation: R²=0.26, 0.64 and 0.05; RMSE=10.92%, 6.38%, and
6.50%, for sand, clay, and silt contents, respectively). When evaluated
on the independent dataset, both SoilGrids and HWSD exhibited poor
performance, characterised by lower R² (<0.07) and higher RMSE
values (> 13%). Furthermore, HWSD and SoilGrids failed to capture
the spatial heterogeneity of soil texture in the region, calling for
caution when using such global products at local scale. Overall, our
study emphasises the need for sustained effort in assembling
47 distributed soil information, as well as meaningful soil predictors at
48 local and regional scales.

49 **Keywords**

50 Digital soil mapping, soil texture, Jenny's Model, Tropics, Geology,

51 Geomorphology

52 **1. Introduction**

53 Soil texture constitutes a fundamental soil property that significantly
54 influences water retention and transport, air circulation, nutrient
55 retention and release, and carbon storage (Emerson, 1995; Saxton et
56 al., 1986). More specifically, soil texture is commonly used to
57 estimate porosity and bulk density (Chaudhari et al., 2013), soil
58 organic carbon(Zinn, Lal and Resck, 2005), field capacity and
59 permanent wilting point (Givi, Prasher and Patel, 2004), as well as
60 residual and saturated moisture contents (Tomasella, Hodnett and
61 Rossato, 2000) through pedotransfer functions. Information on soil
62 texture, therefore, allows the construction of water retention curves
63 (Bruand, Fernández and Duval, 2003). Overall, the accurate
64 determination of soil texture, and its variation in space, is critical to a
65 sound understanding and predictability of ecosystem dynamics from
66 local to global scales.

67 However, conventional methods of collecting soil information, such
68 as soil texture, through soil sampling in the field and physical analyses
69 in the laboratory, are labour-intensive and time-consuming (Bouma,
70 1989; Grunwald, 2009). Texture-by-feel, or tactile assessment of soil textural composition, can be a time-efficient and cost-effective alternative to determining particle size fraction (PSF) through laboratory analysis (Franzmeier and Owens, 2008; Thien, 1979).
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74 While substantial subjectivity is inherent in the process of determining texture through field observations, this method is widely recognised as valid (Hodgson et al., 1976). Nonetheless, varying degrees of accuracy in classifying soil texture through texture-by-feel method have been reported (Salley et al., 2018). As a consequence, soil texture data are usually scarce and sparsely distributed, and access to accurate estimates of soil texture for large geographic areas (e.g. national scale) is particularly difficult.
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82 Initiatives to gather and spatially extrapolate such limited data have thus been developed. In particular, the International Institute for Applied Systems Analysis (IIASA), jointly with the FAO, established the Harmonized World Soil Database (HWSD) by harmonising soil attributes derived from several sources and curated using multiple geostatistical models (Nachtergaele et al., 2023). Despite its global coverage, the concerns over the input attributes as well as the absence of uncertainty quantification have brought the accuracy of HWSD into question (Poggio et al., 2021). Moreover, the point data obtained from field experiments and used in creating spatial polygons in HWSD may not be fully representative of neighbouring sites due to the high spatial variability of soils at fine scales (Minasny and Hartemink, 2011).
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A robust modelling approach is then necessary to be able to extrapolate information on soil texture on large geographic areas from a limited number of point data, but the drivers of such soil properties remain to be ascertained in many regions. Hans Jenny proposed the state-factor model regarding soil as a dynamic system influenced by several environmental factors, including climate, organisms, relief, parent material, and time (Jenny, 1941). In agreement with this, parent material and topography were found to be predominant factors influencing soil groups and their attributes in several regions, including southern Europe (Badía et al., 2013) and Brazilian savannah (Silva et al., 2017)). Similarly, geomorphology showed a strong influence on soil texture in tropical Uganda (Brown, Clayton and McSweeney, 2004), while topography was the primary driver of variations in texture in a small watershed in southern Ethiopia (Dessalegn et al., 2014). Introducing soil and spatial position as additional factors to the original Jenny's pedogenetic model, the 'SCORPAN' model played a pivotal role in the development of digital soil mapping (Grunwald, 2009; McBratney, Mendonça Santos and Minasny, 2003). Recent advancements in remote sensing, geoinformatics, and computational power have further provided the means to generate digital maps of soil properties (Scull et al., 2003). SoilGrids, a relatively recent global soil product, draws from an extensive compilation of almost 150,000 soil profiles sourced worldwide. The product applied machine learning algorithms to spatially predict soil attributes at 250m spatial resolution using 158
remote-sensing-based covariates. However, the limited availability of
soil data in some regions such as tropical areas result in a substantial
underrepresentation of these areas in the input data (Hengl et al.,
2017), potentially introducing biases in the resulting product and
subjecting the regional accuracy of SoilGrids to scrutiny.

Here, we leveraged an extensive dataset of soil observations in a
particularly poorly documented region, French Guiana, to (i) test the
relevance of texture-by-feel in situ analyses as a mean to
quantitatively estimate PSF at lower cost, (ii) identify the drivers of
soil texture in this region, (iii) develop digital soil maps of sand, silt
and clay contents, and (iv) provide an independent assessment of
existing global soil texture maps at a regional scale. More specifically,
drawing on Jenny's model of pedogenesis processes, we gathered
multiple environmental variables related to climate, relief, and
parent material to predict sand, clay, and silt contents at a spatial
resolution of 1 km x 1 km using a Random Forest model. We then
assessed our soil map and the ones provided by HWSD 2.0 and
SoilGrids 2.0 using an independent dataset.

## 2. Materials and Methods

### 2.1 Study Area

French Guiana, covering a land area of approximately 82,220 square
kilometres, is situated on the Guiana Shield in the northeastern part
of South America (Figure 1). The region lies within the tropical zone,
spanning latitudes 2°10′ to 5°45′N and longitudes 51°40′ to 54°30′W.
144 The area includes an inhabited coastal region in the northeast and a
dense rainforest that covers 97% of its total land area (Chave, Dubois
145
and RiÉRa, 2001).
146

147 Experiencing an equatorial climate, French Guiana receives an
148 average annual rainfall ranging from 1900 mm to 3900 mm, with high
149 intra-annual variation. Meanwhile, the mean air temperature
150 remains relatively stable across the region, fluctuating between 23°C
151 and 27°C annually (Beaufort, Gibier and Palany, 2019). The elevation
152 primarily ranges from 0 to 200 metres above mean sea level,
153 occasionally reaching over 800 metres in mountain peaks located in
154 the inner central part of the region. The geological makeup of the
155 region is predominantly composed of the Guiana Shield, a nearly 2
156 giga-annum old crystalline geological landform (Delor et al., 2003).
157

2.2 Soil Data

158 The soil data used in this study comprises 421 soil cores, which were
159 extracted using a soil auger from 29 different sites in the region
160 (HABITAT project led by Office National des Forêts in French Guiana).
161 The 29 sites cover the six main soil types that can be found in French
162 Guiana based on expert knowledge (Guitet et al., 2015). For each of
163 the 421 cores, multiple soil samples were extracted at various depths
164 (10, 20, 40, 60, 80, 100, and 120 cm), resulting in a total of 2810
165 individual soil samples. In the field, the extracted soil samples at each
166 depth were described based on their texture-by-feel, coarse
167 fragment features, and colour characteristics. More specifically, for
each sample, the quantities of sand, silt and clay were ranked between 0 to 5, while the presence and amount of coarse fragment features such as duricrust and lithorelics were ranked between 0 and 3. Duricrust consists of hardened soil formation enriched with iron oxide and aluminium oxide (Nash, 2009), whereas lithorelics are rock fragments originating from weathered parent material (Lecomte, 1988). Soil colour was characterised according to the Munsell colour system (Hue, Chroma, and Value). In the Munsell colour system, the Hue describes the basic colour (red, yellow, green, blue, or purple), the Chroma describes the colour intensity (ranging from 0 to 12), and the Value describes the lightness (from 0 to 10). Hue has been transformed from a qualitative variable to a continuous variable with values ranging from 1 to 360 degrees (Blavet, Mathe and Leprun, 2000). In our dataset, the Value varied between 4 and 8, the Chroma between 1 and 8, and the transformed Hue varied between 36 and 270 degrees. Some soil samples were subsequently combined to obtain composite soil samples representative of soil types and depths in French Guiana. Specifically, 198 soil samples from 49 cores in 13 sites, with two to five samples of the same site, same soil type, and same depth, were mixed together to obtain a total of 52 composite soil samples. These 52 composite samples were taken to the laboratory to determine the soil PSF, specifically the sand, silt, and clay contents. The techniques followed for soil extraction, sample collection and composite sample formation are further detailed in (Guitet et al., 2015).
193 We also gathered data for 415 soil samples at various depths from 67
194 complete soil profiles collected as part of the GuyaFor project. These
195 soil samples were also analysed in the laboratory for their sand, silt
196 and clay contents at each depth. Eleven additional soil profiles from
197 the WOSIS dataset were considered to be added to the dataset
198 (Batjes, 2012). We only retained 8 of those as 3 profiles were situated
199 in swamp areas while our model only considers land areas excluding
200 water bodies. Overall, the final dataset consists of 496 (421 + 67 + 8)
201 soil cores or profiles spatially distributed across French Guiana (Fig.
202 1).

203 An additional soil dataset consisting of 72 soil profiles of the top 30
204 cm depth was independently collected and subjected to laboratory
205 analyses as part of the AMALIN and DIADEMA projects (Baraloto et
206 al., 2011; Vleminckx et al., 2021). These 72 profiles were exclusively
207 used for the independent assessment of the Random Forest model's
208 predictions, as well as for assessing the global soil maps from HWSD
209 and SoilGrids. Across these 72 soil samples, sand content ranged from
210 6.7% to 99%, clay from 0.5% to 89%, and silt from 1% to 48%.

211 2.3 Environmental Variables

212 Based on Jenny's pedogenic model (Jenny, 1941), we considered the
213 following types of variables to model the soil texture in French
214 Guiana: climate, relief, and parent material. We have considered
215 neither organisms nor time as factors explaining the spatial variability
216 in soil texture, as the region has remained predominantly covered by
217 the rainforest (Ballère et al., 2021).

218 2.3.1 Climate

219 Climatic data, namely mean annual rainfall (rfc), were obtained from
220 WorldClim 2.1 (Fick and Hijmans, 2017). The datasets are provided at
221 a 30 arc second spatial resolution (approximately 1 km at the
222 Equator) and averaged over the period 1970--2000. Due to
223 insignificant variation in mean annual temperature across the region,
224 and due to marginal impact on the model output, temperature was
225 ultimately not used as a predictor.

226 2.3.2 Relief

227 The "relief" component in Jenny's model is here documented by the
228 topography, geomorphology and landform features. Digital elevation
229 model (DEM) at 30 m spatial resolution was acquired from the Shuttle
230 Radar Topography Mission (SRTM) and used directly as a quantitative
231 variable *alt* (Farr et al., 2007). The DEM was also used to derive
232 several topographic and geomorphological variables. To begin with,
233 the relative elevation (*rel*) was calculated by taking the difference
234 between the elevation of a pixel and the mean elevation within a
235 circular radius of 1200 m (McKenzie and Ryan, 1999). Similarly, the
236 height above the nearest drainage (or HAND index abbreviated as
237 *hdi*) and the slope (*slo*, in percent) were calculated from the DEM
238 (Nobre et al., 2011; Rennó et al., 2008).
239 Geomorphology was then qualified at both local and macrorelief scales. First, at local scale, the DEM was used to categorise the area into ten frequently recognizable geomorphons. A search radius of 100 m was used to derive a 10-class qualitative variable, called geomorphon (gmp). These classes include Flat, Peak, Ridge, Shoulder, Spur, Slope, Hollow, Footslope, Valley, and Pit (Jasiewicz and Stepinski, 2013). Second, at macrorelief scale, the entire study area was divided into ten distinct landscape classes (lsc) based on several descriptors, including altitude range, altitude base, uphill flattening, hillside slope and hydromorphic index. The lsc classes used were High hill and mountain, Regular pattern hilly area, Hilly plateau with inselbergs, Peneplain with moderate hill, Moderate plateau with inselberg, Inland plain, Complex hilly area, High dissected plateau, Low hilly area and large valley, and Coastal plain (Guitet et al., 2013).

253 2.3.3 Parent Material

254 A detailed geology map of French Guiana, originally consisting of 41 classes, was obtained from the French Geological Survey (Delor et al., 2001). The map had been simplified to reach eight classes, each representing distinct geological features (Gond et al., 2011). Following an expert assessment, the geological class 'White sand' was reintroduced resulting in the following nine geological classes (abbreviated as *gos*) in the entire region: Recent sediments, Dikes, Granite, Gneiss, Gabbro, Quartzite & sandstone, Volcanic sediments, Metapelite, White sand.
263 **2.4 Quantitatively estimating soil texture from texture-by-feel**
264 analysis.

265 We first estimated the silt, sand and clay contents of the 2810 soil
266 samples coming from the HABITAT project. To do so, we built multiple
267 linear regression models with soil texture as response variables and
268 soil characteristics documented in the field as predictors using the 52
269 composite samples for which both types of information were
270 available. We then used these fitted models to predict silt, sand, and
271 clay contents from soil characteristics documented in the field for the
272 2810 samples. More specifically, the particle size distribution of the
273 composite samples was re-attributed to the constituent samples,
274 assuming that the composite samples accurately represented the soil
275 texture of their constituents. This led to a dataset comprising 198
276 observations, each encompassing measured sand, silt, and clay
277 contents, as well as field-analysed characteristics such as texture
278 ranks, coarse fragment features, and colour properties, all
279 corresponding to their respective samples.

280 Two multiple linear regressions (MLR) were employed to ascertain
281 the relationships between the sand and clay contents (in %) and the
282 soil characteristics documented in the field. As explanatory
283 covariates, the two MLR models included the three textural ranks for
284 sand, silt, and clay, the value, chroma, and hue describing the soil
285 colour based on the Munsell colour system, and the quantity of
286 duricrust and lithorelic. To take into account the strong correlation
287 between sand and clay contents observed for our soil samples, we
288 also used the percentage of observed sand as an additional covariate
289 for predicting clay. For predicting silt content for the 198 soil samples,
290 we used the following relationship: silt content = 100 - (sand content
291 + clay content). All the covariates used as predictors in the MLR
292 models were first normalised on the interval [0, 1] using the min-max
293 normalisation technique. We chose the Akaike information criterion
294 (AIC) to reduce the number of covariates and simplify the model
295 without significantly impacting its performance (Akaike, 1998). More
296 specifically, we employed the stepAIC function in the MASS R package
297 to select the best set of covariates that explain the response variable
298 and balance the model fit with complexity (Venables and Ripley,
299 2002). Following this method, the MLR models with the lowest AIC
300 values were selected as parsimonious models for sand and clay
301 predictions. Additionally, we examined the normality of the model
302 residuals.

303 Considering the relatively small number of samples, i.e. 198, the
304 models were trained and cross-validated using a 5-fold cross-
305 validation procedure, where the dataset was divided into five
306 subsets. Each model iteration involved training on four-fifths of the
307 data and validating on the remaining one-fifth. The results of MLR
308 models developed for sand and clay contents were evaluated based
309 on their coefficient of determination (R²) to examine the goodness of
310 fit between the observed and predicted values. Additionally, the root
311 mean square error (RMSE) was used to evaluate the magnitude of
312 error in the predicted sand, silt and clay contents. Using the fitted
313 MLR models, PSF (in %) were then computed for the 2810 soil
314 samples at different locations and depths based on soil characteristics
315 (textural ranks, soil colour, and quantity of lithorelic and duricrust).
316 We then used a weighted average across depths to estimate the PSF
317 for each of the 421 soil cores. Similarly, the texture data from 67 soil
318 profiles and 8 WOSIS profiles were also averaged across depth to
319 determine sand, clay, and silt fractions representative of the entire
320 profile. The sand, silt, and clay contents derived using the MLR
321 approach (421 observations), in combination with the data from 67
322 soil profiles and 8 WOSIS profiles, resulted in a total of 496 PSF point
323 data that were subsequently used in training the spatial Random
324 Forest models as described in the followed section.

325 **2.5 Spatial Random Forest model of soil texture**

326 We used a Random Forest (RF) model (Breiman, 2001) to estimate
327 sand, silt, and clay contents using climatic, relief and parent material
328 variables described in Section 2.3 as the predictors. The ‘train’
329 function available in the ‘caret’ package was employed to perform the
330 training of the RF models (Kuhn, 2008). The model was trained using
331 496 observations of sand content as the response variable and eight
332 environmental variables as predictors (climate: rfc; relief: alt, rel, hdi,
333 slo, gmp, lsc; parent material: gos). The parameter ‘mtry’ was set to
334 zero, and the model was tuned using the ‘expand.grid’ function to
335 allow the algorithm to determine the optimum number of variables
336 to consider at each split. The 'ntree' parameter was set at 500 and
337 the 'RSquared' was defined as the performance metric in the model
338 training.

339 The performance of the RF model was then assessed through both
340 random and spatial cross-validations, the latter aiming at addressing
341 potential bias arising from spatial autocorrelation in the data (Ploton
342 et al., 2020). For the k-fold random cross validation, the trainControl
343 function was set to 'cv' with the number of folds set to 10. The values
344 of R² and RMSE acquired at each validation fold were averaged across
345 folds to assess the overall performance of the model when randomly
346 cross validated. For spatial cross-validation, the entire region of
347 French Guiana was divided into 10 geographic zones, each consisting
348 of a 1-degree x 1-degree cell. The model was trained using nine zones.
349 During the evaluation process, the model was tested on the 10th
350 zone. This iterative model development and evaluation procedure
351 was repeated 10 times, with a different zone held out for evaluation
352 each time. The overall model's performance, measured in terms of R²
353 and RMSE, was estimated by averaging the R² and RMSE values
354 obtained at each validation step.

355 Another RF model was developed using 496 observations of clay
356 content as the response variable and nine covariates as model
357 predictors, including the eight environmental variables and the
358 observed sand content. Random and spatial cross validation
359 methods, as detailed in the preceding paragraph, were repeated to
360 test the prediction accuracy of the clay model. Silt content was
361 computed as 100 minus the sum of predicted sand and predicted clay.
362 Subsequently, the models' ability to predict silt was assessed by
363 computing its R² and RMSE against silt contents measured in 496
364 profiles. Predictions of silt contents were also cross evaluated using
365 both random k-fold and spatial cross validations.

366 Alternatively, we explored the additive log-ratio (ALR) transform
367 approach (Lark and Bishop, 2007). This approach entailed training
368 two Random Forest (RF) models to predict the log ratios of sand/clay
369 (ALRsand) and silt/clay (ALRsilt), utilising the same set of eight
370 environmental variables. The predicted ALRs were subsequently
371 transformed into PSF using the additive generalised logistic transform
372 (Aitchison, 1982), a method commonly applied to compositional data
373 analysis (Ballabio, Panagos and Monatanarella, 2016; Ward and
374 Mueller, 2012). However, the ALR approach was not considered
375 further due to its underperformance in the evaluation phase (Table
376 S1).

377 Mean decrease in model accuracy was used as the criterion to rank
378 the predictive power of each variable used in the development of
379 each RF model. The indicator measures the decrease in model's
380 predictive performance when a specific variable is omitted (Breiman,
381 2001). Moreover, Moran's I was employed as a statistic to assess
382 spatial autocorrelation in the residuals of the sand and clay models
383 (Moran, 1950). Semivariograms were also generated to determine
384 the magnitude and direction of spatial autocorrelation in the sand
385 and clay residuals using the 'moran.randtest' function from the
386 'adespatial' package in R (Dray et al., 2023). Density plots of residuals
387 were used to assess their normality, providing an additional criterion
388 to evaluate the robustness of the RF models.

389 **2.6 Mapping soil texture in French Guiana based on**
390 **environmental variables**

391 The fitted spatial Random Forest model for sand was used to spatially
392 predict the values of sand content across French Guiana using the
393 raster maps of the eight environmental variables resampled at 1km
394 resolution. Then, the predicted sand content at 1km resolution was
395 used together with the eight environmental variables to predict clay
396 content at 1km resolution using the second spatial Random Forest
397 model for clay. These spatial predictions were carried out using the
398 'predict' function from the R package 'terra' (Hijmans, 2023). The silt
399 content was computed over French Guiana at 1km resolution from
400 the predicted sand and clay contents given that the silt, sand, and clay
401 contents sum to 100.

402 **2.7 Evaluating the regional and global soil texture maps using**
403 **independent soil data**

404 We used the 72 independent soil profiles, extracted from the top 30
405 cm depth, to evaluate our regional soil texture map and two global
406 soil texture maps provided by SoilGrids v2.0 and HWSD v2.0. SoilGrids
407 2.0 (https://www.isric.org/explore/soilgrids) produces maps of soil
properties for the entire globe at 250m spatial resolution using
machine learning models. It takes as inputs soil observations from
about 240,000 locations worldwide, among which only 11 are located
in French Guiana, and over 400 global environmental covariates
describing vegetation, terrain morphology, climate, geology and
hydrology (Poggio et al., 2021). The Harmonized World Soil Database
([https://gaez.fao.org/pages/hwsd](https://gaez.fao.org/pages/hwsd)) is a global soil inventory providing
information on the morphological, chemical and physical properties
of soils at a resolution of 30 arc seconds (Nachtergaele et al., 2023).
The 72 independent soil profiles were collected in 9 sites across
French Guiana. Out of the 72 profiles, 21 were taken from 2 sites
(namely SL and MIT) that were not included in the training dataset
(Figure 1). The other 51 soil profiles came from 7 sites that were also
sampled to obtain training data for the Random Forest models.
However, for these sites, it is important to note that the soil profiles
used for evaluation were taken at different locations from the soil
profiles used for the Random Forest model calibration. The inset
maps in Figure 1 zoom in on two sites with soil profiles used for
training and independent soil profiles used for evaluation to highlight
their distances. The SoilGrids 2.0 and HWSD 2.0 maps for sand, silt,
and clay contents were averaged at a 0 - 100 cm depth to match our
training dataset's 120 cm average depth, using a weighted arithmetic
mean. Ultimately, two indices namely R² and RMSE were employed
to assess the accuracy of the three products (our regional maps, the
432 SoilGrids 2.0 and HWSD 2.0 global maps) against the observed
textural data from the 72 independent soil profiles.

434 **3. Results**

435 **3.1 Particle size fraction estimated from in-situ observations**

436 The laboratory-analysed data exhibited a strong negative correlation
437 between sand and clay contents, with an R² value of 0.67. However,
438 observations with high silt contents, which were all located in the
439 same site (Crique Plomb), tended to deviate from the correlation line
440 (Figure 2a). The sand and clay models (Table 1) had R² values of 0.62
441 ± 0.13 and 0.86 ± 0.04, and RMSE values of 12.43 ± 1.98 % and 7.28 ±
442 1.07 % respectively, when using k-fold random cross-validation
443 (Figure 2b). The predicted silt content, calculated as 100 minus the
444 sum of predicted clay and sand contents, had R² values of 0.08 ± 0.05,
445 and RMSE values of 10.84 ± 3.03 % when randomly cross-validated
446 against the observed values.

447 **3.2 Particle size fraction predicted from environmental covariates**
448

449 The sand, clay, and silt contents in the 496 observations used to train
450 the Random Forest model varied from 0% to 96% (39 ± 16%), 2% to
451 81% (41 ± 14%) and 1% to 51% (21 ± 8%), respectively. The predicted
452 values of sand, clay and silt contents for the maps of textural
453 components (Figure 3) ranged from 18% to 65% (39 ± 9%), 14% to
454 70% (42 ± 8%), and 8% to 34% (19 ± 5%). Overall, the RF models
455 struggled to accurately predict both low and high values of each textural component (Figure S1). We found similar results while 456 assessing the accuracy of the predicted textural classes (Table S2, 457 Figure S2). 458

459 **3.2.1 Random and Spatial Cross Validation**

460 The sand RF model had R² of 0.54 and RMSE of 10.92% when 461 evaluated using random cross-validation (CV), and R² of 0.26 and 462 RMSE of 14.44% when evaluated using spatial CV. The clay RF model 463 had R² of 0.76 and RMSE of 6.38% when evaluated using random CV, 464 and R² of 0.64 and RMSE of 6.57% when evaluated using spatial CV. 465 Silt predictions that were computed with predicted sand and clay, had 466 R² of 0.10 and RMSE of 6.50% when evaluated using random CV, and 467 R² of 0.05 and RMSE of 6.85% when evaluated using spatial CV (Table 468 3). Both model's residuals presented no significant spatial 469 autocorrelation (Figure S3).

470 **3.2.2 Variable Importance**

471 The variables lsc (gemorphological landscape) and gos (geological 472 substrate) were the most important factors to predict sand content, 473 with an overall importance of 100 and 47%, respectively (Table 4, 474 Figures S4a and S4b). The gemorphological landscape alone explained 475 30-50 % of the variation in sand content. High sand content was 476 associated with inland plains, peneplains and coastal plains, whereas 477 relatively lower sand content was associated with mountains and
478 plateaus. Similarly, the geological substrate alone explained 35-45%
479 of the variation in sand contents. Specifically, high sand content was
480 associated with quartzite, sandstone, dikes and white sand, while the
481 lowest sand content was associated with metapelites, gabbros and
482 recent sediments.

483 The other environmental variables (i.e. rfc, slo, gmp, rel, alt, hdi)
484 weakly predicted sand content, with overall importance of 7.9, 2.7,
485 1.5, 1.4, 1.3, and 0% respectively (Table 4). Sand content showed a
486 slight downward trend, with around a 5% variation, as mean annual
487 precipitation (rfc) increased from 2000 to 3000 mm (Figure S4c).
488 Similarly, sand content showed a slight downward trend, with around
489 a 3% variation, as slope (slo) increased from 0 to 20% (Figure S4d).

490 In the clay model, the significance of all environmental variables was
491 overshadowed by the sole influence of predicted sand. With an
492 overall importance of 100, predicted sand exhibited a strong negative
493 correlation with predicted clay (Figure S4e). This is consistent with a
494 similarly strong negative correlation detected between the observed
495 values of sand and clay content in the laboratory-measured data
496 (Figure 2a).

497 **3.3 Independent Validation**

498 The RF models were able to predict sand, clay and silt contents at the
499 72 sampling points of the independent evaluation dataset with an R²
500 of 0.35, 0.20 and 0.18, and an RMSE of 26.7%, 23.6%, and 12.6%,
501 respectively. While the RF model's predictions exhibited a narrower
502 range as compared to the observed values, our predictions show a
503 narrower range of bias across all textural components compared to
504 both HWSD 2.0 and SoilGrids 2.0, indicating greater consistency and
505 accuracy in estimating soil textural composition (Figure S5b).

506 Specifically, HWSD 2.0 estimated sand, clay, and silt contents with R²
507 values of -0.06, 0.03, and -0.49, and RMSE of 30.8%, 26%, and 13 %
508 for sand, clay, and silt, respectively. SoilGrids, while showing a slight
509 improvement over HWSD, still demonstrated poor agreement with
510 the textural data recorded at the 72 sampling points, with R² values
511 of -0.01, 0.07, and -0.21, and RMSE values of 26.5 %, 37.5 % and
512 18.6% for clay, sand and silt, respectively.

513 Furthermore, the SoilGrids 2.0 map of soil texture shows a much
514 lower spatial variability of soil texture and predicts a higher clay
515 content on the coastal area than our regional map (Figure 3, Figure
516 S6). The HWSD map shows large areas (10-50 km size) with
517 homogeneous soil texture while our regional map shows a soil
518 texture variability at a finer spatial resolution (< 10 km, Figure 3,
519 Figure S6).

520 **4. Discussion**

521 **4.1 Texture-by-feel and field observations provide reliable**

522 **estimates of particle size fractions**
In our study, the *in-situ* observations of soil texture, colour and coarse fragment effectively predicted the percentages of sand and clay. This provides new evidence coming from a tropical region that texture-by-feel analysis, when conducted by a well-trained soil surveyor, can serve as an effective alternative to the restrictive and time-consuming laboratory measurements. These findings are consistent with prior studies examining the relationships between in-situ observations and laboratory analysis of PSF (Vos et al., 2016) and soil textural classifications (Foss, Wright and Coles, 1975; Richer-de-Forges et al., 2022). This is especially advantageous for large and difficult to access regions where comprehensive soil texture data may be limited. Furthermore, our results emphasise the positive relationship between the high clay content and the presence of duricrust, as shown in previous studies (Laurent et al., 2017). Nonetheless, the estimations of silt content through the texture-by-feel method were relatively poorer than for sand and clay. Distinguishing between fine particles and accurately assessing their ranges using only field methods can be inherently challenging. The challenge of differentiating between the stickiness of clay and the floury feel of silt in a moist sample is well-documented in soil texture analysis literature (Hodgson et al., 1976; Salley et al., 2018). Similarly, silt content can frequently be confused with very fine sand in the tactile analysis of soil samples (Richer-de-Forges et al., 2022). On the other hand, the relatively poorer performance in silt predictions can also be attributed to the significant underrepresentation of silty
548 textured soils and the relatively narrow range of silt content in our
549 study (Figure S1). The low prevalence of silty textures and the strong
550 negative correlation between clay and sand align with the definition
551 of bimodal soils commonly found in tropical and subtropical regions
552 (Botula et al., 2013; de Condappa et al., 2008). A similar but opposite
553 phenomenon was observed with clay predictions exhibiting the
554 lowest accuracy in a clay-deficient, silt-dominated area in Germany
555 (Dunkl and Ließ, 2022).

556 **4.2 Landscape-scale Geomorphology and Geology Explain**
557 **Spatial Variability in Soil Texture**

558 Geomorphological landscape type was the most important driver of
559 soil texture in our study. More specifically, geomorphological
560 characterisation at macrorelief scale was a much stronger
561 determinant of soil texture than local geomorphological and
562 topographic variables whose influence were insignificant in our case.
563 This is consistent with a previous study linking soil types in French
564 Guiana with the geomorphological characterisation at regional scale
565 (Guitet et al., 2016). Our result is also consistent with those of
566 (Quesada et al., 2010) who showed that soil physical properties
567 across the Amazon Basin varied with a gradient of pedogenetic
568 development linked with the main macroreliefs. Globally, landscapes
569 at macro scale play a crucial role in shaping soils, as they are a product
570 of geology, climate and other environmental processes including
571 weathering and erosion that have occurred over millions of years
572 (Summerfield, 2014). We found that high mountain landscapes had
573 the lowest sand content (i.e. the highest clay content) while coastal
574 plains, inland plains, and peneplains exhibited the highest levels.
575 These observations align well with studies examining the gradient of
576 clayey ferralsols at high hills to acrisols along the slopes and sandy
577 podzols at the plains, highlighting the relationship between landscape
578 and soils in French Guiana as well as wider Amazonia (Boulet, 1978;
579 Do Nascimento et al., 2004; Fritsch et al., 2011; Lucas et al., 1987;
580 Montes et al., 2007). This is also consistent with the presence of
581 duricrust at high hills in the Guiana Shield that results in clayey
582 plinthosols upon weathering (Théveniaut and Freyssinet, 1999;
583 Théveniaut and Freyssinet, 2002). The association between hilly
584 landscapes and high clay content, and conversely, plains and sandy
585 soils, has also been observed in the Venezuelan Amazon (Gavaud et
586 al., 1986).

587 In our study, we found that geology is the second most important
588 driver of texture: quartzite, sandstones, and white sand had higher
589 sand contents in the predicted soils, while metapelites and gabbros
590 had relatively lower sand contents. This is consistent with the
591 composition of these two rocks, metapelites being fine-grained
592 sedimentary rocks, and gabbro being low in quartz (Garzanti et al.,
593 2016; Rudnick and Fountain, 1995). This is also in agreement with soil
594 textural mapping in Sao Paulo, Brazil, where sandstones and alluvial
595 deposits were linked with high sand, whereas gabbro was linked with
596 high clay contents (Bonfatti et al., 2020). Our findings are also
consistent with the observed association between low clay in soils and high silica content in parent rock in New South Wales, Australia (Gray, Bishop and Wilford, 2016). Given the abundance of Precambrian rocks in the Guiana Shield, geology may have become a less influential factor over time, attenuating its impact on soil texture, but this impact is still visible up to now.

We observed that other environmental variables, such as mean annual precipitation and slope, demonstrated weak correlations with sand content. Despite the significant variation in annual rainfall across French Guiana, ranging from 2000 to 4000 mm, it only accounted for a 5% variation in sand content (Figure S4c). The negative correlation between rainfall and sand content may suggest limited erosion in French Guiana, as finer particles like clay and silt are more easily detached and transported by runoff, leading to sandy soils in erosion-prone areas (Wu et al., 2017). This limited erosion could be explained by two causes: (i) the dominance of a dense vegetation leads to soils that were always covered and therefore not very prone to erosion (Ramos et al., 2019) (Ramos et al., 2019); (ii) extensive weathering over prolonged periods in the Guiana Shield could have removed the less stable surface mineral assemblage, leading to significantly lower dissolved or solid sediment loads as reported in rivers in the area (Stallard, 1988). Alternatively, the negative correlation between rainfall and sand contents could also signify a stronger impact of parent material, which might outweigh the effects of precipitation on prediction outcomes. In particular, the
parent material linked with sandy textures are found primarily in relatively drier northwestern regions of French Guiana.

## 4.3 Global Soil Maps Fail to Capture Textural Heterogeneity of Tropical Soils in French Guiana

Both HWSD 2.0 and SoilGrids 2.0 failed to capture the spatial variability of soil texture in French Guiana. This poor performance of global soil products is consistent with similar studies investigating soil texture variation in other areas. Particularly larger errors were observed in regions with fewer soil profiles used in the derivation of global soil maps (Stoorvogel and Mulder, 2021). For instance, SoilGrids exhibited poor performance in accurately predicting soil texture in several geoclimatic regions, including Tropical Colombia (Varón-Ramírez, Araujo-Carrillo and Guevara Santamaría, 2022), the Thar Desert in India (Dandabathula et al., 2022), temperate Croatia (Radočaj et al., 2023), and boreal forests in Norway (Huang et al., 2022). Similarly, HWSD resulted in high errors and low correlation with respect to point observations in southern Africa (Bodenstein et al., 2022; Buenemann et al., 2023). Global soil products also exhibited low overall accuracies in textural classifications in Ghana (Maynard et al., 2022). The disparity from ground reality observed in the global soil products might lead to significant errors in estimating other soil parameters that rely on accurate PSF through pedotransfer functions.

## 4.4 Perspective, Limitations and Recommendations
We here used an unprecedented dataset of field observations analysed throughout a tropical region to accurately predict soil textural components. Relying on Jenny's pedogenetic model, this provided the means to establish reliable digital soil maps of sand, silt, and clay contents for the region, which should be preferentially used to derive soil information and estimate soil-related parameters in comparison to global soil products such as HSWD and SoilGrids. Landscape type and parent material were identified as the strongest determinants of soil texture with reasonable accuracy in French Guiana. With the widespread availability of geological maps and digital elevation models used in deriving landscape maps, our approach has the potential to predict soil texture in other areas worldwide.

Nonetheless, the study presented in this article has certain limitations that require careful consideration. Firstly, a significant portion of the response variables utilised in the RF model is derived from in-situ observations of soil texture, which could be susceptible to biases due to potential human errors. However, these limitations were overcome by the high predictive performance of the MLR models used for the prediction of PSF. Secondly, for the sake of modelling simplicity, the study assumed a uniform texture across the entire soil profile by averaging textural component values observed at various depths within the same sampling point. Thirdly, the depth of the independent dataset used for evaluation was not the same as the three predicted soil maps (30 cm vs 120cm), which could potentially
670 underestimate the performance of these maps. Lastly, our dataset
671 only includes observations from forest habitats, which covers 96% of
672 French Guiana. As a consequence, our map should only be valid for
673 that habitat type but might not be valid for other habitat types such
674 as swamp areas, where soils are different from terrestrial soils and
675 characterised by anaerobic conditions. To mitigate the limitations
676 discussed above, we recommend a more comprehensive approach in
677 future studies. This could involve supplementing the dataset with
678 additional laboratory-derived soil profiles and constructing multilayer
679 models for more precise textural predictions. Nevertheless, the study
680 represents a significant step forward in generating detailed textural
681 maps for an area that was previously underrepresented. The resulting
682 soil texture maps have the potential to be used to estimate other soil
683 properties, such as organic carbon, water holding capacity, and
684 nutrient availability, through the application of pedotransfer
685 functions. These soil properties play a crucial role in explaining forest
686 dynamics, particularly in a region like the tropical Amazon.

687 **Declaration of competing interests**

688 The authors declare that they have no conflict of interest.

689 **Acknowledgements**

690 This study benefited from the financial support from the CEBA
691 strategic project METRADICA, Part of an "Investissement d'Avenir"
692 grant managed by the Agence Nationale de la Recherche and the
693 Center for the Study of Biodiversity in Amazonia (CEBA, ref. ANR-10-
694 LABX-25-01). SK received a 17-month postdoctoral fellowship by
695 CEBA as part of the METRADICA project. The authors thank all the
696 participants (researchers, technicians, students) who participated in
697 collecting soil data as part of the METRADICA, GuyaFor, HABITAT,
698 DIADEMA, and AMALIN projects. The combination of these data
699 contributed to obtaining the large soil database over French Guiana
700 used in this study.

701 **Data availability**

702 Data and code used for this study are available on GitHub at the
703 following address: https://github.com/ghislainv/soil-texture-map-
704 french-guiana ,and will be permanently archived on the Cirad
705 Dataverse at https://dataverse.cirad.fr under a CC BY-SA 4.0 (for
706 data) and GPLv3 (for code) licences as soon as the article is accepted
707 for publication.

708 Author contributions

709 SK, IM, GV, and VF designed and performed the study. CS and GV
710 secured the funding for the METRADICA project. VF, BF, SG, and OB
711 collected soil data as part of the GuyaFor and HABITAT projects. VF,
712 SK, CS, OB and GV collected additional soil data as part of the
713 METRADICA project. CB and CF collected the soil data that were used
714 for validation as part of the AMALIN and DIADEMA projects. VF
715 coordinated the lab analysis of soil samples. FS initiated the statistical
716 analysis during his Master 2 training as part of the METRADICA
717 project under the supervision of IM and VF. SK performed all the
718 subsequent statistical analysis presented in this study with the help
719 of IM, GV, and VF. SK led the writing of the initial draft with the help
720 of IM, GV, and VF. All authors discussed the results and participated
721 in the writing of the final version of the manuscript.

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934 Fischer, G., Jones, A., Montanarella, L., Shi, X., Tramberend,
935 S., 2023. Harmonized World Soil Database. Food and Agriculture Organization of the United Nations and International Institute for Applied Systems Analysis, Rome and Laxenburg.

939 Nash, D.J., 2009. Duricrusts. In: V. Gornitz (Ed.), Encyclopedia of Paleoclimatology and Ancient Environments. Springer Netherlands, Dordrecht, pp. 284-285.

942 Nobre, A.D., Cuartas, L.A., Hodnett, M., Rennó, C.D., Rodrigues, G., Silveira, A., Waterloo, M., Saleska, S., 2011. Height Above the Nearest Drainage – a hydrologically relevant new terrain model. Journal of Hydrology 404(1), 13-29.

946 Ploton, P., Mortier, F., Réjou-Méchain, M., Barbier, N., Picard, N., Rossi, V., Dormann, C., Cornu, G., Viennois, G., Bayol, N., Lyapustin, A., Gourlet-Fleury, S., Pélissier, R., 2020. Spatial validation reveals poor predictive performance of large-scale ecological mapping models. Nature Communications 11(1), 4540.

952 Poggio, L., de Sousa, L.M., Batjes, N.H., Heuvelink, G.B.M., Kempen, B., Ribeiro, E., Rossiter, D., 2021. SoilGrids 2.0: producing soil information for the globe with quantified spatial uncertainty. SOIL 7(1), 217-240.

956 Quesada, C.A., Lloyd, J., Schwarz, M., Patiño, S., Baker, T.R., Czimczik, C., Fyllas, N.M., Martinelli, L., Nardoto, G.B., Schmerler, J., Santos, A.J.B., Hodnett, M.G., Herrera, R., Luizão, F.J., Arneth, A., Lloyd, G., Dezzeo, N., Hilke, I., Kuhlmann, I., Raessler, M.
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991 Saxton, K.E., Rawls, W.J., Romberger, J.S., Papendick, R.I., 1986.
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998 Silva, M.B., Anjos, L.H.C.d., Pereira, M.G., Schiavo, J.A., Cooper, M.,
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1048

1049

1050
1051 **Figures**

1052

<figure><img src="image_3.png" /><figcaption>1054 **Figure 1: Spatial distribution of soil texture observations in French**</figcaption></figure>

1055 **Guiana.** Left panel: This figure shows the locations of soil profiles used for training the model (496 sample points, blue circles) and for validation (72 sample points, pink crosses). Right panels: The validation dataset includes independent soil texture profiles either in the same sites as the training dataset but with different exact locations (top right figure at the LIM site) or in different sites (bottom right panel at the MIT site). The inset maps (highlighted within red rectangles) offer a closer view of specific sites within the study area.
1056
1057
1058
1059
1060
1061
1062
1063 Each inset map features a 1 X 1 kilometre grid (dashed lines) to aid in spatial reference and scale.
1064
1065

<figure><img src="image_4.png" /></figure>

$R^2 = 0.67$

1066

<figure><img src="image_5.png" /></figure>

1067 Figure 2 Correlations between sand and clay contents and

1068 performance of the model predicting soil granulometry measured

1069 at the laboratory from soil texture and characteristics estimated in

1070 the field. Left: Correlation between sand and clay contents measured

1071 in the laboratory (n=52). Right: Relationship between the particle size

1072 fractions predicted by the multiple regression models and measured

1073 in the laboratory (n=198).

1074

1075

1076

1077

1078
<figure><img src="image_6.png" /><figcaption>Figure 3: Composite soil texture maps of French Guiana predicted by our RF models (left panel), SoilGrids v2.0 (middle), and the Harmonized World Soil Database (HWSD) v2.0 (right). The SoilGrids map shows a much lower spatial variability of soil texture and predicts a higher clay content on the coastal area than our regional map. The HWSD map shows large areas (10-50 km size) with homogeneous soil texture while our regional map shows a soil texture variability at a finer spatial resolution (< 10 km).</figcaption></figure>

1088

1089

1090
<figure><img src="image_7.png" /><figcaption>Figure 4. Evaluation of our model (top left), SoilGrids 2.0 (top right) and HWSD 2.0 (bottom left). Our model results demonstrate strong agreement with independently collected data, whereas both SoilGrids 2.0 and HWSD 2.0 exhibit poor correspondence with the measured data. The grey shaded area on each side of the trend line represents the 95% confidence interval (n = 72).</figcaption></figure>
1099

1100 Tables

1101 Table 1: Multiple Linear Regression models intercept and coefficients. Initially, sand, silt and clay contents are in percent, ranks vary between 0 and 5 and rock features between 0 and 3. Value and chroma range from 4 to 8 and 1 to 8, respectively. These predictors were rescaled between 0 and 1 so that their coefficients can be directly compared to determine relative variable importance.

<table><thead><tr><th rowspan="2">COMPON<br>ENT</th><th rowspan="2">INTERC<br>EPT</th><th rowspan="2">TEXTURA<br>L<br>COMPON<br>ENT</th><th colspan="3">RANKS</th><th colspan="2">COLOUR</th><th colspan="2">ROCK FEATURES</th></tr><tr><th>Sand</th><th>Clay</th><th>Sand</th><th>Silt</th><th>Value</th><th>Chro<br>ma</th><th>Lithoreli<br>c</th><th>Duricrus<br>t</th></tr></thead><tbody><tr><td>SAND</td><td>48.449</td><td>-</td><td>-8.342</td><td>35.90</td><td>13.2</td><td>-</td><td>-</td><td>-16.173</td><td>-22.666</td></tr><tr><td></td><td></td><td></td><td></td><td>0</td><td>63</td><td>18.696</td><td>11.2</td><td></td><td></td></tr><tr><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>78</td><td></td><td></td></tr><tr><td>CLAY</td><td>50.028</td><td>-43.233</td><td>13.02</td><td>-</td><td>-</td><td>-</td><td>14.2</td><td>-13.480</td><td>-</td></tr><tr><td></td><td></td><td></td><td>49</td><td></td><td>12.3</td><td>5.7620</td><td>12</td><td></td><td></td></tr></tbody></table>

1107

1108
1109 Table 2: Variables used as covariates in the RF Models. These

1110 variables were derived based on Jenny's pedogenesis model.

<table><thead><tr><th>Categ<br>ory</th><th>Variable Name</th><th>Variable<br>Abbreviation</th><th>Source/<br>Resolution</th><th>Type/unit</th><th>Range/<br>Classes</th><th>Reference</th></tr></thead><tbody><tr><td>Paren<br>t<br>Mate<br>rial</td><td>Geological<br>substrate</td><td>gos</td><td>BRGM<br>1/50<br>000</td><td>Qualitative</td><td>9</td><td>Delor et al<br>2001, Gond<br>et al. 2011</td></tr><tr><td>Clima<br>te</td><td>Mean annual<br>rainfall</td><td>rfc</td><td>WorldC<br>lim 2<br>1-km</td><td>Quantitative<br>mm</td><td>1880 -<br>3850</td><td>Fick &<br>Hijmans<br>2017</td></tr><tr><td rowspan="6">Relief</td><td>Altitude</td><td>alt</td><td>DEM<br>30-m</td><td>Quantitative<br>m</td><td>0 - 830</td><td>SRTM 2013</td></tr><tr><td>Landscape</td><td>isc</td><td>DEM<br>30-m</td><td>Qualitative</td><td>10</td><td>Guitet et al<br>2013</td></tr><tr><td>Geomorphon</td><td>gmp</td><td>DEM<br>30-m</td><td>Qualitative</td><td>10</td><td>Jasiewicz &<br>Stepinski<br>2012</td></tr><tr><td>Regional<br>elevation</td><td>rel</td><td>DEM<br>30-m</td><td>Quantitative<br>m</td><td>0 - 514</td><td>McKenzie &<br>Ryan 1999</td></tr><tr><td>Slope</td><td>slo</td><td>DEM<br>30-m</td><td>Quantitative<br>%</td><td>0 - 228</td><td>Gallant &<br>Wilson 1996</td></tr><tr><td>HAND Index</td><td>hdi</td><td>DEM<br>30-m</td><td>Quantitative<br>m</td><td>0 - 187</td><td>Rennó et al.<br>(2008)</td></tr></tbody></table>
1112 Table 3: RF models results summary, performance, evaluated based
1113 on the amount of variance explained (R²) and the root mean square
1114 error (RMSE)

1115 $$R^2 = 1 - \left( \frac{\text{var(residual)}}{\text{var(observed)}} \right)$$

1116 $$RMSE = \sqrt{\frac{1}{n} * \sum(y_i - \hat{y}_i)^2}$$

<table><thead><tr><th>COMPONENT</th><th>VARIABLES</th><th>CV METHOD</th><th>R<sup>2</sup> (SD)</th><th>RMSE % (SD)</th></tr></thead><tbody><tr><td rowspan="2">SAND</td><td rowspan="2">8</td><td>10-fold Random</td><td>0.54 (0.09)</td><td>10.92 (1.51)</td></tr><tr><td>10-Zones Spatial</td><td>0.26 (0.30)</td><td>14.44 (2.61)</td></tr><tr><td rowspan="2">CLAY</td><td rowspan="2">9</td><td>10-fold Random</td><td>0.76 (0.06)</td><td>6.38 (0.62)</td></tr><tr><td>10-Zones Spatial</td><td>0.64 (0.25)</td><td>6.57 (6.62)</td></tr><tr><td rowspan="2">SILT</td><td rowspan="2">10 (model is 100 - predicted clay - predicted sand)</td><td>10-fold Random</td><td>0.10 (0.03)</td><td>6.50 (0.81)</td></tr><tr><td>10-Zone Spatial</td><td>0.05 (0.11)</td><td>6.85 (1.91)</td></tr></tbody></table>

1117

1118
1119 Table 4: RF Models Variables Ranked by their significance, calculated

1120 using the mean decrease in accuracy, in the predictions.

<table><thead><tr><th rowspan="2">S. No</th><th rowspan="2">Variable Name</th><th rowspan="2">Type/ unit</th><th rowspan="2">Range/ Classes</th><th rowspan="2">Variable Abbreviations</th><th colspan="2">Overall Importance</th></tr><tr><th>Sand</th><th>Clay</th></tr></thead><tbody><tr><td></td><td>Sand</td><td>%</td><td>0 - 80</td><td>—</td><td>—</td><td>100</td></tr><tr><td>1</td><td>Landscape</td><td>QL</td><td>10</td><td>lsc</td><td>100</td><td>5.29</td></tr><tr><td>2</td><td>Geological Substrate</td><td>QL</td><td>9</td><td>gos</td><td>47.26</td><td>1.98</td></tr><tr><td>3</td><td>Rainfall</td><td>QN mm</td><td>1880 - 3850</td><td>rfc</td><td>7.93</td><td>1.81</td></tr><tr><td>4</td><td>Slope</td><td>QN %</td><td>0 - 228</td><td>slo</td><td>2.70</td><td>0</td></tr><tr><td>5</td><td>Geomorphon</td><td>QL</td><td>10</td><td>gmp</td><td>1.52</td><td>0</td></tr><tr><td>6</td><td>Regional Elevation</td><td>QN m</td><td>0 - 514</td><td>rel</td><td>1.44</td><td>0</td></tr><tr><td>7</td><td>Altitude</td><td>QN m</td><td>0 - 830</td><td>alt</td><td>1.29</td><td>0</td></tr><tr><td>8</td><td>HAND Index</td><td>QN m</td><td>0 - 187</td><td>hdi</td><td>0</td><td>0</td></tr></tbody></table>

1121

1122

1123

1124
1125 Supplementary Material

1126 Table S1: RF models for ALR predictions and independent

1127 validation of transformed data

<table><thead><tr><th rowspan="2"></th><th rowspan="2">CV Method</th><th colspan="2">RF Model (n = 496)</th><th colspan="3">Independent Validation<br>Transformed Data (n = 72)</th></tr><tr><th>ALRsand</th><th>ALRsilt</th><th>Clay</th><th>Sand</th><th>Silt</th></tr></thead><tbody><tr><td rowspan="2">R<sup>2</sup></td><td>Random</td><td>0.53 (0.09)</td><td>0.42 (0.12)</td><td>0.27</td><td>0.36</td><td>-0.04</td></tr><tr><td>Spatial</td><td>0.23 (0.27)</td><td>0.17 (0.23)</td><td></td><td></td><td></td></tr><tr><td rowspan="2">RMS<br>E</td><td>Random</td><td>0.56 (0.09)</td><td>0.47 (0.09)</td><td>25.1 %</td><td>26 %</td><td>10.5 %</td></tr><tr><td>Spatial</td><td>0.75 (0.19)</td><td>0.64 (0.17)</td><td></td><td></td><td></td></tr></tbody></table>

1128 Table S2: Model performance at correctly predicting the USDA soil textural classes using three indicators.

1129

1130 The sensitivity and specificity indicators were used to calculate true positive and true negative rates, respectively, while kappa was applied to measure the inter-rater reliability of the soil textural classes.

<table><thead><tr><th>INDICATOR</th><th>VALUE</th></tr></thead><tbody><tr><td>Sensitivity</td><td>0.96</td></tr><tr><td>Specificity</td><td>0.57</td></tr><tr><td>Kappa</td><td>0.53</td></tr></tbody></table>

1134
<figure><img src="image_8.png" /><figcaption>(a)</figcaption></figure>

<figure><img src="image_9.png" /><figcaption>(b)</figcaption></figure>

<figure><img src="image_10.png" /><figcaption>(c)</figcaption></figure>

<figure><img src="image_11.png" /><figcaption>(d)</figcaption></figure>

1137 **Figure S1: Density plots for observed and predicted values of a) Sand**

1138 **b) Clay and c) Silt, along with d) Model Residuals.** Bell-shaped plots

1139 indicate an approximately normal distribution of the residuals (n =

1140 496)
<figure><img src="image_12.png" /><figcaption>Figure S2: USDA soil texture triangle of observed soils and soils predicted using Random Forest models (n = 496). Cl: clay, SaCl: sandy clay, SaClLo: sandy clay loam, ClLo: clay loam, SiCl: silty clay, SiClLo: silty clay loam, Lo: loam, SiLo: silty loam, Si: silt, SaLo: sandy loam, LoSa: loamy sand, and Sa: sand. The observed and predicted sand, silt, and clay contents at 496 sampling points were categorised into twelve textural classes using United States Department of Agriculture (USDA) classifications. It is noticeable that the predicted points (depicted in red) tend to cluster in the sandy clay, sandy clay loam, and clay classes, whereas the observed data points appear more dispersed. The figure also highlights an underestimation of high clay and sand contents, resulting in an underrepresentation of sandy loam, loam, loamy sand, and clay loam classes.</figcaption></figure>
<figure><img src="image_13.png" /></figure>

<figure><img src="image_14.png" /><figcaption>(i)</figcaption></figure>

<figure><img src="image_15.png" /></figure>

<figure><img src="image_16.png" /><figcaption>(ii)</figcaption></figure>

Figure S3: Spatial autocorrelation of model residuals. Spatial

distribution and semi-variances plots for i) sand and ii) clay residuals

(n = 496). Spatial autocorrelation in the residuals remains

insignificant with low values of Moran's I and p-values higher than

0.05
<figure><img src="image_17.png" /><figcaption>(a)</figcaption></figure>

<figure><img src="image_18.png" /><figcaption>(b)</figcaption></figure>

<figure><img src="image_19.png" /><figcaption>(c)</figcaption></figure>

<figure><img src="image_20.png" /><figcaption>(d)</figcaption></figure>

<figure><img src="image_21.png" /></figure>
1163 **Figure S4: Partial dependence plots between the predicted sand and clay, and the explanatory variables** of a) landscape, b) Geological Substrate, c) Precipitation, d) Slope against sand predicted by the Random Forest model; and e) Partial Dependence Plot of predicted sand against clay predicted by the Random Forest model. Partial dependence was calculated to show how the predicted value changes as a single variable is varied while holding all other variables constant.
<figure><img src="image_22.png" /></figure>

<figure><img src="image_23.png" /></figure>

Figure S5: a) Relative bias in RF predictions plotted against the observed values of soil textural components. Relative bias was calculated as (predicted - observed)/observed. In the plot, blue represents relative bias in sand, red represents relative bias in clay, and green represents relative bias in silt (n = 496).

b) Bias in soil textural components predicted by our model, SoilGrids and HWSD using the validation points. Bias was calculated as predicted - observed (n = 72)
<figure><img src="image_24.png" /></figure>

1180

<caption>(i) (a)</caption>

<caption>(b)</caption>

<figure><img src="image_25.png" /><figcaption>(c)</figcaption></figure>

1181

<caption>(ii) (a)</caption>

<caption>(b)</caption>

<caption>(c)</caption>
<figure><img src="image_26.png" /><figcaption>Figure S6: Individual particle size maps of i) Sand ii) Clay and iii) Silt predicted by a) Our RF model b) SoilGrids 2.0 and c) HWSD 2.0</figcaption></figure>

1185