# Combined use of Sentinel-2 images and Sentinel-1-derived moisture maps for soil organic carbon content mapping in croplands, South-western France

Diego Urbina Salazar, Emmanuelle Vaudour, Nicolas Baghdadi, Eric Ceschia, Dominique Arrouays

<figure><img src="image_1.png" /></figure>

## To cite this version:

Diego Urbina Salazar, Emmanuelle Vaudour, Nicolas Baghdadi, Eric Ceschia, Dominique Arrouays. Combined use of Sentinel-2 images and Sentinel-1-derived moisture maps for soil organic carbon content mapping in croplands, South-western France. EGU 2021, Apr 2021, Vienna (AUSTRIA), Austria. 2021, 10.5194/egusphere-egu21-8836. https://hal-04220921

HAL Id: hal-04220921

https://hal.inrae.fr/hal-04220921

Submitted on 28 Sep 2023

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<figure><img src="image_2.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
# COMBINED USE OF SENTINEL-2 IMAGES AND SENTINEL-1/2 DERIVED MOISTURE MAPS FOR SOIL ORGANIC CARBON CONTENT MAPPING IN CROPLANDS, SOUTH-WESTERN FRANCE

université
PARIS-SACLAY

INRAO

AGROPARISTECH

D. URBINA-SALAZAR¹,⁴, E. VAUDOUR¹, N. BAGHDADI², E. CESCHIA³, D. ARROUAYS⁴

¹ Université Paris-Saclay, INRAE, AgroParisTech, UMR ECOSYS, 78850, Thiverval-Grignon, France

² INRAE, UMR TETIS, 34093, Montpellier CEDEX 5, France

³ INRAE, USC 1439 CESBIO, Toulouse, France

⁴ INRAE, InfoSol Unit, US 1106, 45075 Orléans, France

ECOSYS

<figure><img src="image_3.png" /></figure>

Theia
Pôle Thématique
Surfaces Continentales

## Study area / objective

## Material & methods

<figure><img src="image_4.png" /><figcaption>Soil moisture products (SMP)</figcaption></figure>

<figure><img src="image_5.png" /><figcaption>Classes of texture</figcaption></figure>

<figure><img src="image_6.png" /></figure>

<figure><img src="image_7.png" /></figure>

<figure><img src="image_8.png" /></figure>

<figure><img src="image_9.png" /><figcaption>Relationship between texture classes and SOC prediction residuals</figcaption></figure>

<figure><img src="image_10.png" /></figure>

## Results & discussion

S2 prediction performance using surface soil moisture (SM) from (SMP) as a covariate (in bold characters, models using SM as a covariate)

<table><thead><tr><th rowspan="2">Imaging Date S2</th><th rowspan="2">SMP</th><th rowspan="2">NS</th><th rowspan="2">MD</th><th rowspan="2">R<sup>2</sup>cv</th><th rowspan="2">RMSEcv (g.kg<sup>-1</sup>)</th><th rowspan="2">RP Dcv</th><th rowspan="2">NC</th><th colspan="4">Soil Moisture (mV Vol%)</th></tr><tr><th>Min</th><th>Me</th><th>x</th><th>Max</th></tr></thead><tbody><tr><td rowspan="2">6 April 2017</td><td>7 April 2017</td><td>59</td><td>No</td><td>0.08</td><td>1.69</td><td>1.05</td><td>1</td><td>6.8</td><td>17.8</td><td>17.38</td><td>24.6</td></tr><tr><td>19 May 2017</td><td>50</td><td>No</td><td>0.47</td><td>2.06</td><td>1.39</td><td>4</td><td>9</td><td>20.6</td><td>20.4</td><td>28</td></tr><tr><td rowspan="2">16 May 2017</td><td>25 May 2017</td><td>58</td><td>No</td><td>0.46</td><td>1.33</td><td>1.38</td><td>4</td><td>5.4</td><td>13.2</td><td>13.02</td><td>18.8</td></tr><tr><td>21 March 2018</td><td>55</td><td>No</td><td>0.1</td><td>2.42</td><td>1.07</td><td>2</td><td>22</td><td>26.4</td><td>26.24</td><td>31.4</td></tr><tr><td rowspan="2">26 May 2017</td><td>19 April 2018</td><td>47</td><td>No</td><td>0.09</td><td>3.58</td><td>1.06</td><td>2</td><td>11.2</td><td>19.8</td><td>19.88</td><td>26.4</td></tr><tr><td>13 May 2018</td><td>58</td><td>No</td><td>0.44</td><td>3.06</td><td>1.35</td><td>3</td><td>21.4</td><td>28.4</td><td>28.21</td><td>33.4</td></tr><tr><td rowspan="2">22 March 2018</td><td>20 May 2018</td><td>73</td><td>No</td><td>0.48</td><td>2.91</td><td>1.4</td><td>7</td><td>20.8</td><td>22.6</td><td>23.66</td><td>29</td></tr><tr><td>21 May 2018</td><td>73</td><td>No</td><td>0.47</td><td>2.91</td><td>1.38</td><td>8</td><td>20.8</td><td>22.6</td><td>23.66</td><td>29</td></tr></tbody></table>

*Moisture data in drier periods improved performance

*S1 data in the models did not improve performance

*Higher residues were associated with loamy textures in 2018

S2 prediction performance using raw S1

<table><thead><tr><th>Imaging Date S1</th><th>NS</th><th>P</th><th>R<sup>2</sup>cv</th><th>RMSEcv (g.kg<sup>-1</sup>)</th><th>RP Dcv</th><th>NC</th></tr></thead><tbody><tr><td rowspan="2">7 April 2017</td><td>59</td><td>VII</td><td>0.13</td><td>1.64</td><td>1.08</td><td>3</td></tr><tr><td>50</td><td>VII</td><td>0.09</td><td>1.68</td><td>1.06</td><td>1</td></tr><tr><td rowspan="2">19 May 2017</td><td>58</td><td>VII</td><td>0.44</td><td>2.12</td><td>1.35</td><td>5</td></tr><tr><td>59</td><td>VII</td><td>0.45</td><td>1.31</td><td>1.36</td><td>5</td></tr><tr><td rowspan="2">25 May 2017</td><td>58</td><td>VII</td><td>0.45</td><td>1.31</td><td>1.36</td><td>5</td></tr><tr><td>55</td><td>VII</td><td>0.09</td><td>2.44</td><td>1.05</td><td>2</td></tr><tr><td rowspan="2">21 March 2018</td><td>55</td><td>VII</td><td>0.3</td><td>2.08</td><td>1.24</td><td>2</td></tr><tr><td>47</td><td>VII</td><td>0.65</td><td>3.64</td><td>1.04</td><td>2</td></tr><tr><td rowspan="2">19 April 2018</td><td>58</td><td>VII</td><td>0.46</td><td>3.01</td><td>1.37</td><td>2</td></tr><tr><td>59</td><td>VII</td><td>0.46</td><td>2.9</td><td>1.37</td><td>2</td></tr><tr><td rowspan="2">20 May 2018</td><td>73</td><td>VII</td><td>0.46</td><td>2.97</td><td>1.37</td><td>9</td></tr><tr><td>73</td><td>VII</td><td>0.45</td><td>3</td><td>1.36</td><td>9</td></tr></tbody></table>

## Conclusion

*The date of acquisition of the S2 images and other disturbing factors affect SOC prediction

*Perspectives to study the effect of Soil moisture (SM) on more dates and under different conditions (Possibility of determining optimal thresholds above which the SM can be included in prediction models).

<figure><img src="image_11.png" /></figure>

**Study Aim:** Predict topsoil organic carbon (SOC) content over croplands, addressing: (i) the influence of the sentinel image date, and (ii) the use of soil moisture products derived from the S1 & S2 satellites in the spectral models.

Sentinel-2 (S2A) images and soil moisture products were acquired from the Theia platform (https://www.theia-land.fr/) and Sentinel-1 images from the European Space Agency's Copernicus platform.

Bare soil reflectance spectra were extracted then centered and PLSR with leave-one-out cross-validation was used on the bands to construct prediction models for SOC content.