[Defending thesis] Eva López Fornieles : Potential of multispectral satellite image time series for the characterisation and dynamic monitoring of a crop: application to vines on a regional scale

Eva López Fornieles is one of the #DigitAg co-funded PhDs

Eva will defend her thesis on 23rd September 2022 at 2.00 pm @L’Institut Agro (Amphi Philippe Lamour) – To attend the defence by visioconference

Potential of multispectral satellite image time series for the characterisation and dynamic monitoring of a crop: application to vines on a regional scale

  • Start Date: 01/10/2019
  • University: MUSE Montpellier Université d’Excellence / L’Institut Agro
  • PhD School: GAIA, Montpellier
  • Field(s): Agronomy
  • Doctoral Thesis Advisor: Bruno Tisseyre (L’Institut Agro, ITAP)
  • Co-supervisors : James Taylor (INRAE, ITAP), Nicolas Devaux (L’Institut Agro, LISAH), Jean Michel-Roger (INRAE, ITAP), Sébastien Roux (INRAE, MISTEA), Christian Gary (INRAE, SYSTEM)
  • Funding: #DigitAg – L’Institut Agro
  • #DigitAg: Axis 6 & 5, Challenges 5 & 3

Keywords: time series, remote sensing, water stress, vines

Abstract: Information sources based on remote sensing have particularly interesting characteristics for dynamic crop monitoring, from the plot scale to the regional scale. Imagery from sensing platforms is capable of being used for operational decision support (for expertise or as model input) for crop monitoring at different scales. Despite the demonstration of this capability in previous studies, the development of these new sources of information (platforms and sensors) is progressing more rapidly than the development of new information technologies adapted to the management of this vast quantity of data. Indeed, the information that characterises this type of data is not only large (multidimensional), but also very heterogeneous, which remains a challenge for data processing and agronomic interpretations.

In order to contextualise this research, this doctoral research project has focused on the potential of a time-series of Sentinel-2 satellite images for monitoring vineyards at the regional scale across the Occitanie region (France). This spatio-temporal Sentinel-2 dataset presents unique characteristics in terms of revisit time, spatial resolution, attribute information provided and cost. Moreover, the choice of spatial coverage is interesting in itself, as the Languedoc-Roussillon wine region represents a great diversity of agri-environmental conditions resulting in a large number of different grape varieties being cultivated as well as a large diversity in the management practices of the wine growers. Collectively these factors introduce additional levels of variability into the analysis of regional-scale viticulture data. This PhD work is based on the assumption that assessing the temporal variability in the satellite imagery, in addition to spectral variations, would allow a more complete analysis to derive new and relevant information about production variability of individual vineyards at the regional scale. With this in mind, the principal objective of this thesis is to integrate temporal analyses, as an additional descriptor of vineyard variability, in order to take into account, in a better and more holistic way, all the specific dimensions of remote sensing data (spectral, temporal and spatial). Different supervised and unsupervised multi-way analysis methods, derived from the field of chemometrics, were used, capable of generating information at the regional scale from time series of multispectral images. Unsupervised approaches demonstrated the possibility of extracting agronomic knowledge over time (e.g. different vegetative dynamics) without a priori knowledge. The supervised methods allowed, firstly, the spectral, temporal and spatial assessment of an extreme climatic event (e.g. a heat wave) and, secondly, the selection of multidimensional (spectral-temporal) variables to deepen the agronomic understanding of the impact of an extreme climatic event on grapevines at a regional scale.

This work demonstrates that analysis methods exploiting temporal and spectral signatures to extract information on regional-scale variations in vegetative growth offer valuable information for assessing individual crop performance. Taking into account the high dimensionality of the data, which includes the temporal dimension, the needs as well as the limitations of time series analysis are explored in the context of providing relevant information to aid large-scale knowledge of a crop, such as grapevines.

Jury compound:

Anna Maria DE JUAN CAPDEVILA, Professeure associée, Université de Barcelona, Espagne
José Antonio MARTINEZ CASASNOVAS, Professeur, Université de Lleida, Espagne
Agnès BEGUE, Directrice de recherche, Cirad, France
Jean Baptiste FERET, Chargé de recherche, Maison de la Télédétection, France
Joaquim BELLVERT, Chargé de recherche, IRTA Lleida, Espagne
Bruno TISSEYRE, Professeur, Institut Agro Montpellier, France
Harold CLENET, Professeur associé, École d’ingénieurs de Purpan, France
James TAYLOR, Directeur de Recherche, INRAE Montpellier, France


Papers in international journals

Contact:  eva.lopez-fornieles [AT] supagro.fr

Social Networks: ResearchGate – LinkedIn – Twitter