Modelling agricultural drought: a review of latest advances in big data technologies

Ismaguil Hanadé Houmma (1, 2, 3) , Loubna El Mansouri (4) , Sébastien Gadal (2, 1, 5) , Maman Garba (6) , Rachid Hadria (7, 8)

1 ESPACE - Étude des Structures, des Processus d’Adaptation et des Changements de l’Espace
2 AMU - Aix Marseille Université
3 IAV Hassan II - Institut Agronomique et Vétérinaire Hassan II
4 Institute of Agronomy and Veterinary Hassan II
5 North-Eastern Federal University
6 Institut National de la Recherche Agronomique du Niger (INRAN)
7 Faculté des Sciences Semlalia [Marrakech]
8 INRA Maroc - Institut national de la recherche agronomique [Maroc]

This article reviews the main recent applications of multi-sensor remote sensing and Artificial Intelligence techniques in multivariate modelling of agricultural drought. The study focused mainly on three fundamental aspects, namely descriptive modelling, predictive modelling, and spatial modelling of expected risks and vulnerability to drought. Thus, out of 417 articles across all studies on drought, 226 articles published from 2010 to 2022 were analyzed to provide a global overview of the current state of knowledge on multivariate drought modelling using the inclusion criteria. The main objective is to review the recent available scientific evidence regarding multivariate drought modelling based on the joint use of geospatial technologies and artificial intelligence. The analysis focused on the different methods used, the choice of algorithms and the most relevant variables depending on whether they are descriptive or predictive models. Criteria such as the skill score, the given game complexity used, and the nature of validation data were considered to draw the main conclusions. The results highlight the very heterogeneous nature of studies on multivariate modelling of agricultural drought, and the very original nature of studies on multivariate modelling of agricultural drought in the recent literature. For future studies, in addition to scientific advances in prospects, case studies and comparative studies appear necessary for an in-depth analysis of the reproducibility and operational applicability of the different approaches proposed for spatial and temporal modelling of agricultural drought.

Mots clés: Multi-sensors modelling; Agricultural drought; Indices; Drought frequency; Machine learning; Multivariate drought modelling; Artificial intelligence AI; Big data

Domaines: Modélisation et simulation; Traitement des images [eess.IV]; Environnement et Société; Géographie; Méthodes et statistiques

Ismaguil Hanadé Houmma, Loubna El Mansouri, Sébastien Gadal, Maman Garba, Rachid Hadria. Modelling agricultural drought: a review of latest advances in big data technologies. Geomatics, Natural Hazards and Risk, 2022, 13, pp.2737 - 2776. ⟨10.1080/19475705.2022.2131471⟩. ⟨hal-03818444⟩