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

Abstract

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. HIGHLIGHTSThe components and fundamentals of multivariate modelling of agricultural drought were discussed.The importance of hybrid artificial intelligence models is widely discussed in improving the performance of traditional machine learning models.Quantum machine learning algorithms are weakly explored in multivariate drought modelling. Therefore, future studies should explore this approach.The major challenge of multivariate modelling of drought frequency is mainly related to the difference in the return periods of the different variables (time-shifted and spatially effects).

Keywords

• Multi-sensor modelling
• agricultural drought indices
• machine learning
• drought frequency
• multivariate drought modelling
• artificial intelligence