# Disaggregation of legacy soil maps at regional scale in France. Comparison of DSMART and Random Forest

Manon Caubet, Alexis Messant, Ghislain Girot, Blandine Lemercier, Guillaume Martelet, Christian Walter, Dominique Arrouays, Nicolas Saby, Anne C Richer-De-Forges

► To cite this version:

Manon Caubet, Alexis Messant, Ghislain Girot, Blandine Lemercier, Guillaume Martelet, et al. Disaggregation of legacy soil maps at regional scale in France. Comparison of DSMART and Random Forest. workshop for Digital Soil Mapping and GlobalSoilMap, Mar 2019, Santiago, Chile. hal-02789531

HAL Id: hal-02789531

https://hal.inrae.fr/hal-02789531v1

Submitted on 5 Jun 2020

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Distributed under a Creative Commons CC0 1.0 - Universal - International License
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# MAPPING OF SOILS AND THEIR PROPERTIES USING LEGACY SOIL MAPS

## *Comparison of DSMART and Random Forest*

Joint workshop for Digital Soil Mapping and GlobalSoilMap
12-15 March 2019
Santiago
Soil polygon maps

* Cover major part of french territory

* Different scales 1:250 000 ; 1:50 000

* Rich source of information about soil

But ..

* Spatial support and extent is not always adapted to demand

» **Objective** : Increase spatial resolution of soil maps and to map soil properties

» **How** ? We tested and compared two methods :

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Data available

Legacy soil map

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60 polygons
196 soil types

435 soil profiles
for calibration

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1:50 000 soil maps

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30 soil profiles for validation
using probability sampling

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+ 24 Covariates

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# Disaggregation with DSMART

## Input

<figure><img src="image_9.png" /><figcaption>Legacy soil map</figcaption></figure>

<table><caption>Composition of Soil Map Units</caption><thead><tr><th>SMU</th><th>STU</th><th>%</th></tr></thead><tbody><tr><td>1</td><td>1</td><td>10</td></tr><tr><td>1</td><td>2</td><td>50</td></tr><tr><td>1</td><td>3</td><td>10</td></tr><tr><td>1</td><td>4</td><td>30</td></tr></tbody></table>

<figure><img src="image_10.png" /><figcaption>435 point observations</figcaption></figure>

<figure><img src="image_11.png" /><figcaption>24 covariates</figcaption></figure>

## DSMART

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Odgers et al., 2014

## Output

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## Validation

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30 points
scorpan regression with Random Forest

Input

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435 point
data

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1:50 000
soil maps

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24 covariates

Output

Validation

Precise soil
map

Systematic
virtual sampling

Add real
observations

Model building
with covariates

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~ 4500 points

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435 points

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Random
Forest

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Prediction

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30 points
<table><thead><tr><th rowspan="2"></th><th colspan="2">Map of soil types</th></tr><tr><th>Purity (%)</th><th>Conf.Int - 90%</th></tr></thead><tbody><tr><th>Dominant soil type map</th><td>33,33</td><td>[19,9 ; 46,8]</td></tr><tr><th>DSMART</th><td>33,33</td><td>[17,8 ; 48,8]</td></tr><tr><th>R.F.</th><td>26,67</td><td>[13,2 ; 40,1]</td></tr></tbody></table>

Validation with 30 points based on probability sampling
Conclusion : Take home message

<table><thead><tr><th>DSMART</th><th>scorpan regression</th></tr></thead><tbody><tr><td colspan="2">Same performance than the original map</td></tr><tr><td colspan="2">The covariates did not capture soil spatial variability</td></tr><tr><td>Intense computational load</td><td>Efficient computational load</td></tr><tr><td colspan="2">Pedological context is a DSM challenge<br><em>new sensors ?</em></td></tr><tr><td>Implementation of soil / landscapes rules</td><td>New 50 000 map</td></tr></tbody></table>
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Thank you for your attention

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