Objective

WP2 aims to explore Earth Observation data capabilities and potentialities in assessing physical plant traits and risk
indicators. Features of vineyard environments are strongly dependent on external conditions and, therefore, a proper dataset will be detected and tested on the two selected Area Of Interest (AOI). This leads to the development of the best practices to be
implemented in all analyzed areas and, mainly, in the evaluation of the benefits introduced by the adoption of the most recent
satellite images (Landsat 9, Planet, etc.) and by their integration with UAV-based data. 

Methodology

EO big data quality and potentialities will be checked step-by-step by applying both conventional methods, like index-based
technique, and the most advanced ones, such machine learning, deep learning and multivariate statistical approaches, which go
beyond the univariate statistical procedure limits. WP2 will integrate all the existing (background) data, models and algorithms that are necessary for vineyard contexts.

Tasks

Task 2.1. Scientific background acquisition aims at providing the scientific base (literature overview, models, and key indicators)
tailored to project needs.

Task 2.2. EO cloud processing platform review aimed at reviewing the existing EO cloud processing technologies in order to select the optimal one to meet the project purpose.

Task 2.3. Best practice definition devoted to detecting the ideal input dataset to gather and define the optimal EO data processing strategies.