Bandeau MELICERTES
Agroécologie et Numérique
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MELICERTES

MELICERTES

Modélisation des états et dynamiques des écosystèmes – applications aux flux et stocks de carbone des écosystèmes modifiés par les activités agricoles.

L'un des objectifs clés de l’agroécologie et de l’agroforesterie est de promouvoir le stockage du carbone dans les sols et de développer les pratiques qui le permettent. Le projet MELICERTES s’intéresse à la modélisation, à l’évaluation et au développement d’approches pour quantifier et surveiller les teneurs stocks de carbone organique dans les écosystèmes et les sols cultivés, en particulier par télé- et proxi-détection. Le projet propose une nouvelle méthode pour analyser les flux de carbone en partant de ces données ainsi que leur dynamique à grande échelle. Les principaux facteurs caractérisant ces flux, et notamment la teneur en carbone organique, seront ainsi identifiés et leur distribution spatiale sera analysée et prédite.

En particulier, MELICERTES vise à estimer des distributions spatiales de carbone organique dans les sols et l'incertitude associée. Pour ce faire, le programme de recherche suivant va être appliqué :

  • intégrer des données provenant de sources multiples et hétérogènes afin de développer des modèles spectraux et spatiaux de la teneur en carbone organique du sol, pilotés par satellite et par des données mesurées au sol ;
  • développer des diagnostics par satellite des pratiques agricoles qui maximisent le stockage du carbone dans le sol (stockantes) et exploiter les résultats satellitaires, spectraux et spatiaux pour construire et adapter des modèles de contenu et de stocks de carbone organique dans le sol ;
  • développer une nouvelle classe de modèles basés sur des données pour en déduire la dynamique des écosystèmes ;
  • combiner les modèles de carbone organique du sol et les modèles systémiques globaux pour fournir un nouveau diagnostic des pratiques agricoles stockantes.

Le caractère innovant de ce projet est le développement de méthodes et d’outils pour établir des prédictions des écosystèmes directement à grande échelle, en partant des données mesurées. Ces modèles, en rupture avec l’existant en machine learning, ont pour objectif de caractériser les dynamiques spatiales et temporelles liées à la productivité, la santé et la résilience des agro-écosystèmes.

 

MELICERTES

 

MELICERTES

Valentine Sollier (INRAE, UMR 1391 – ISPA) : Résilience des agroécosystèmes forestiers sur le versant Pacifique de l’Équateur : effet du changement climatique et des évènements extrêmes ENSO, approche couplée télédétection et modélisation.

PUBLICATIONS

HAL : Dernières publications

  • [hal-04189398] Sentinel-2 satellite images for monitoring cattle slurry and digestate spreading on emerging wheat crop: a field spectroscopy experiment

    This study is aimed to evaluate the utility of Sentinel-2 imagery for monitoring exogenous organic matter (EOM) applied on winter wheat crop, using two spatial scales: proximal and satellite. From proximal sensing, multi-temporal spectral field measurements were taken on experimental fields consisting of three treatments (cattle slurry, liquid and raw digestates) and a control throughout 46 days. From Sentinel-2 satellites, images were analysed before and after EOM application. For both sensing scales, EOM and vegetation indices were used. On any scale of observation, the digestates spread on emerging wheat were easily detectable in late winter, in contrast to spring spreading events which were hindered by the developed vegetation. The agglomerative hierarchical clustering from the EOM indices divided by EVI achieved to discriminate digestates at early and medium stages of vegetation growth. Our findings did not apply for cattle slurry, presumably because of both lower organic and dry matter contents. HIGHLIGHTS • Digestates spread on emerging wheat are detectable in late winter. • Developed vegetation constrains the detection of spring spreading events. • Spectral measurements did not separate the field with cattle slurry and the control. • The visible to near infrared bands are the most impacted after digestate spreading.

    ano.nymous@ccsd.cnrs.fr.invalid (Maxence Dodin) 28 Aug 2023

    https://hal.inrae.fr/hal-04189398
  • [hal-04508449] Sentinel-2/1 Bare Soil Temporal Mosaics of 6-year Periods for Soil Organic Carbon Content Mapping in La Beauce, Central France

    [...]

    ano.nymous@ccsd.cnrs.fr.invalid (Diego Urbina-Salazar) 18 Mar 2024

    https://hal.inrae.fr/hal-04508449
  • [hal-04553210] Uncertainty in Digital Soil Mapping at broad-scale: A review

    With the needs of efficient acquisition of soil information, Digital Soil Mapping (DSM) has been greatly developed and widely applied for over the past two decades. The spatial estimates of soil properties produced with diverse methods over various study areas, have been often seen as the main output of DSM, as they play an important role in environmental modelling and policy. However, compared with the soil property maps, their prediction uncertainty is still less emphasized, which may potentially lead to mis-uses of results and inappropriate decisions if the uncertainty is not assessed, reported, and taken into account by end-users.In this communication, we present a preliminary review of the sources of prediction uncertainties in DSM coming from learning soil data (data source, sampling in space and time, measurements), covariates, and models. We also summarize the methods used to estimate the uncertainty, and to assess the reliability of the uncertainty estimates. We also consider the propagation of uncertainties when several soil attributes are combined to derive information and/or used as inputs for modelling. Furthermore, we discuss some strategies for mitigating the uncertainty, challenges, and future perspectives. This review aims to consolidate the understanding of DSM uncertainties and to contribute to reliable DSM practices, facilitating more informed decision-making in soil-related research and management. 

    ano.nymous@ccsd.cnrs.fr.invalid (Qianqian Chen) 19 Apr 2024

    https://hal.inrae.fr/hal-04553210
  • [hal-04507597] Influence of percentile reflectance thresholding in Sentinel-2 temporal mosaicking on regional SOC and clay prediction performances: case of the Veskra-Skaraborg region (Sweden)

    [...]

    ano.nymous@ccsd.cnrs.fr.invalid (Diego Urbina-Salazar) 16 Mar 2024

    https://hal.inrae.fr/hal-04507597
  • [hal-04507584] Review of spectral indices in remote sensing: definition, popularity, and issues

    [...]

    ano.nymous@ccsd.cnrs.fr.invalid (Qianqian Chen) 16 Mar 2024

    https://hal.inrae.fr/hal-04507584
  • [hal-04507590] Review of spectral indices in remote sensing: definition, popularity, and issues

    [...]

    ano.nymous@ccsd.cnrs.fr.invalid (Qianqian Chen) 16 Mar 2024

    https://hal.inrae.fr/hal-04507590
  • [hal-04350813] Sentinel-2 Imagery for Monitoring Exogenous Organic Matter Fertilizers on Winter Wheat Crop: Proximal and Satellite Approaches

    The use of exogenous organic matter (EOM) fertilizers, such as digestate and cattle slurry, has gained increasing attention due to their potential to reduce reliance on synthetic fertilizers in agriculture. This study evaluated the utility of Sentinel-2 imagery for monitoring different liquid EOM fertilizers applied on winter wheat crop, using both proximal and satellite scales. At the proximal scale, spectral field measurements were taken of experimental fields consisting of three treatments (cattle slurry, liquid and raw digestates) and a control over 46 days. Field reflectance spectra were simulated into the MSI spectral bands of Sentinel-2. At the satellite scale, Sentinel-2 images were analyzed before and after EOM application for each experimental field. EOM and vegetation indices were used to monitor EOM application at both scales. The main findings of this study refer to digestates. Firstly, the spread of digestates on emerging wheat can be easily detected in late winter, up to 15 days after application. Secondly, the visible to near infrared bands are the most impacted the first days after spreading and the visible to red-edge bands are persistently impacted 15 days after spreading. Finally, the detection of spring spreading events is constrained or even hindered by developed vegetation. These findings did not apply to cattle slurry, which was hardly visible in the field and in Sentinel-2 images. This Sentinel-2-based approach can serve as a primer for further implementation over larger fields.

    ano.nymous@ccsd.cnrs.fr.invalid (Maxence Dodin) 18 Dec 2023

    https://hal.inrae.fr/hal-04350813

Documents à télécharger

MELICERTES pdf - 1.83 mo

Voir aussi

Projets ciblés

Date de modification : 09 avril 2024 | Date de création : 06 septembre 2023 | Rédaction : AgroEcoNum