[PhD’s Corner] Eva López Fornieles : Potential for combining time series of multispectral and radar satellite data to develop spatialized variables of vine development at the regional scale: application to the estimation of growth and its cessation and integration into a spatialized water status model

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

Potential for combining time series of multispectral and radar satellite data to develop spatialized variables of vine development at the regional scale: application to the estimation of growth and its cessation and integration into a spatialized water status model

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

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

Abstract: This thesis will investigate the potential of new sources of satellite-based earth observations to be used in operational decision support in viticulture (either directly by experts or as a model input). It will primarily focus on imagery acquired from the ESA Sentinel satellites (Sentinel 1 and 2). In effect, these new sources of earth observation present unique characteristics in terms of the improved revisit time (5 days), spatial resolution (10 m), types of imagery available (multispectral and radar), area covered (regional and greater) and the cost (free imagery). As such, they are extremely interesting for spatial viticulture applications at multiple scales ranging from sub-field to regional.
The originality of the thesis will be in the adoption and adaptation of image analysis techniques to extract relevant and pertinent information from the various image types that :  are multivariate and heterogenous with the attribute space (optical and radar based imagery)  have a temporal dimension (defined by the revisit time and free access)  exist in a geographical context, i.e. the data are spatial. The thesis proposes to explore the data within the images using methods based originally in chemometrics and spectral analysis but integrated with a spatial (geostatistical) and/or temporal (time-series) functionality. The objective will be to extract spatial descriptors (metrics, indices etc…) from these new sources of information that provide information relating to water stress in grapevines. These descriptors could be used to validate zoning and management, as a covariate in the extrapolation and mapping of point data or as an input into a predictive crop model. These potential applications have been identified because ;  water stress and its management is an important and historical field of research for the research group and the project will benefit from access to existing databases that will enable and enhance the research.  There is a strong social and industry demand for operational dignostics and tools to follow the evolution of water stress in vines at different scales with the intent to support enhanced management of grape quality and vine health.

 

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

Social Networks: ResearchGate – LinkedIn – Twitter