# Assessment of soil management practices in Vineyards using Sentinel-2: Challenges in enhancing soil organic carbon content prediction

Hayfa Zayani, Youssef Fouad, Nicolas Baghdadi, Didier Michot, Emmanuelle

Vaudour, Maria José Marques Perez

► To cite this version:

Hayfa Zayani, Youssef Fouad, Nicolas Baghdadi, Didier Michot, Emmanuelle Vaudour, et al.. Assessment of soil management practices in Vineyards using Sentinel-2: Challenges in enhancing soil organic carbon content prediction. TERRAenVision 4, Nature-based solutions to facilitate the transitions for living within the planetary boundaries., Jul 2024, Valencia, Spain. . [DOI: 10.5281/zenodo.1411651](https://zenodo.org/DOI/1411651)

HAL Id: hal-04811651

https://hal.science/hal-04811651v1

Submitted on 29 Nov 2024

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

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

TERRAenVision 4, 8-11 July 2024, Valencia, Spain

# Assessment of soil management practices in Vineyards using Sentinel-2:
## Challenges in enhancing soil organic carbon content prediction

Hayfa Zayani<fnref n="1" />,<fnref n="2" />, Youssef Fouad<fnref n="2" />, Nicolas Baghdadi<fnref n="3" />, Didier Michot<fnref n="2" />, Emmanuelle Vaudour<fnref n="1" />, Maria José Marques Perez<fnref n="4" />

## Introduction

* Soil organic carbon (SOC) is a key indicator of soil fertility. In the context of climate change, it is crucial to monitor its spatial and temporal evolution in relation to agricultural practices.

* Vineyard management practices influence soil surface characteristics, which in turn affect the accuracy of models used to predict SOC content via satellite remote sensing (Marques et al., 2010; Vaudour et al., 2022).

* It is crucial to identify viticultural management practices to further assess their impact on carbon storage in viticultural soils.

* This work was carried out within the framework of the SANCHOSTHIRST project, which began in November 2023. This project aims to identify soil restoration practices and management.

* **Objective:** Evaluate the ability of Sentinel-2 data time series to detect soil surface conditions associated with cover crop and tillage.

## Study areas

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

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

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

© M. J. Marques-Perez

<figure><img src="image_5.png" /><figcaption>Bergonza_Toledo plot (180 ha)</figcaption></figure>

Spain

<figure><img src="image_6.png" /><figcaption>Aranda_Burgos plot (82 ha)</figcaption></figure>

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

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

© M. J. Marques-Perez

* Two Spanish vineyard plots with 60 points sampled on each in October 2023 and January 2024.

* For both plots vineyards are trellis-trained with an inter-row spacing around 3 m.

* Two soil management practices were conducted; cover crop (CC) and tillage (Till)

## Methodology

Sentinel-2 time series

<figure><img src="image_9.png" /></figure>

January 2023 – February 2024

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

NDVI (Rouse et al. 1973)

BI (Escadafal 1989)

SI (Escadafal 1993)

NBR2 (Van Deventer et al. 1997)

NSSI (Tian et al. 2021)

Redl (Mathieu et al. 1998)

Fe2 (Van der Meer et al. 2014)

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

Principal Component Analysis (PCA) + Hierarchical Classification + Time series analysis within a 30-meter buffer around the sampling points

<figure><img src="image_12.png" /></figure>

<figure><img src="image_13.png" /></figure>

<figure><img src="image_14.png" /></figure>

## Preliminary results

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

* Toledo_Bergonza plot has greater variability in NDVI compared to the Aranda_Burgos plot.

* Clusters were identified for both plots based on the PCA of indices.

* The NSSI showed correlations with the percentage of coarse elements on the surface.

<figure><img src="image_16.png" /></figure>

<figure><img src="image_17.png" /></figure>

<figure><img src="image_18.png" /></figure>

<figure><img src="image_19.png" /></figure>

<figure><img src="image_20.png" /></figure>

<figure><img src="image_21.png" /></figure>

© M. J. Marques-Perez

<figure><img src="image_22.png" /></figure>

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

<figure><img src="image_24.png" /></figure>

<figure><img src="image_25.png" /></figure>

<figure><img src="image_26.png" /></figure>

© M. J. Marques-Perez

<figure><img src="image_27.png" /></figure>

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

<figure><img src="image_29.png" /></figure>

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

<figure><img src="image_31.png" /></figure>

© M. J. Marques-Perez

## Conclusion

* These preliminary results highlight the significant variability in soil surface condition, raising additional challenges, in addition to the presence of grapevines, for the prediction of SOC content using satellite remote sensing.

## Next steps

* Multivariable analysis for the 7 indices.

* Analyzing indices of only bare soil periods

* Relation analysis of NSSI and coarse elements

* Conduct a deeper investigation of agricultural practices with farmers.

### References:

* Escadafal, R. et al. (1993) 'Le spectroradiomètre de terrain: un outil de la télédétection et de la pédologie', Cahiers - ORSTOM, Serie Pedologie, 28(1), pp. 15-29.

* Escadafal, R., Girard, M. and Courault, D. (1989) 'Munsell Soil Color and Soil Reflectance in the Visible Spectral Bands of Landsat MSS and TM Data', 46(September 1988), pp. 37-46.

* Marques, M.J. et al. (2010) 'Soil conservation beneath grass cover in hillside vineyards under Mediterranean Climatic conditions (Madrid, Spain)', Land Degradation & Development, 21(2), pp. 122-131. Available at: https://doi.org/10.1002/ldr.915.

* Mathieu, R. et al. (1998) 'Relationships between satellite-based radiometric indices simulated using laboratory reflectance data and typic soil color of an arid environment', Remote Sensing of Environment, 66, pp. 17-28. Available at: https://doi.org/10.1016/S0034-4257(98)00030-3.

* Rouse, J.W. et al. (1973) 'Monitoring vegetation systems in the great plains with ERTS', in 3rd ERTS Symposium, NASA SP-351. Washington DC: NASA, pp. 309-317.

* Tian, J. et al. (2021) 'A novel spectral index for estimating fractional cover of non-photosynthetic vegetation using near-infrared bands of Sentinel satellite', International Journal of Applied Earth Observation and Geoinformation, 101(102361), pp. 1-9. Available at: https://doi.org/10.1016/j.jag.2021.102361.

* Van der Meer, F.D., van der Werff, H.M.A. and van Ruitenbeek, F.J.A. (2014) 'Potential of ESA's Sentinel-2 for geological applications', Remote Sensing of Environment, 148, pp. 124-133. Available at: https://doi.org/10.1016/j.rse.2014.03.022.

* Van Deventer, A.P. et al. (1997) 'Using Thematic Mapper data to identify contrasting soil plains and tillage practices', Photogrammetric Engineering and Remote Sensing, 63(1), pp. 87-93.

* Vaudour, E. et al. (2022) 'Satellite Imagery to Map Topsoil Organic Carbon Content over Cultivated Areas: An Overview', Remote Sensing, 14(2917), pp. 1-22. Available at: https://doi.org/10.3390/rs14122917.

### Acknowledgements:

The authors gratefully acknowledge financial support from the EJP Soil SanchosThirst project, the MELICERTES project (PEPR Agroécologie et Numérique), CES THEIA Digital Soil Mapping and the POLYPHEME project of CNES-TOSCA.

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

<figure><img src="image_33.png" /></figure>

Universidad Autónoma de Madrid

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

AgroParisTech

Talents d'une planète soutenable

<figure><img src="image_35.png" /></figure>

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

<figure><img src="image_37.png" /></figure>

L'INSTITUT
agro

Rennes
Angers

<footnote n="1">¹ Université Paris-Saclay, INRAE, AgroParisTech, UMR EcoSys, 91120 Palaiseau, France ; hayfa.zayani@inrae.fr, emmanuelle.vaudour@inrae.fr</footnote>

<footnote n="2">² SAS, Institut Agro, INRAE, 65 Rue de St Brieuc, 35000 Rennes, France ; youssef.fouad@agrocampus-ouest.fr, didier.michot@agrocampus-ouest.fr</footnote>

<footnote n="3">³ CIRAD, CNRS, INRAE, TETIS, Université de Montpellier, AgroParisTech, CEDEX 5, 34093 Montpellier, France ; nicolas.baghdadi@teledetection.fr</footnote>

<footnote n="4">⁴ Department of Geology and Geochemistry, Universidad Autónoma de Madrid (UAM), 28049 Madrid, Spain ; mariajose.marques@uam.es</footnote>