A review of drought monitoring with big data: Issues, methods, challenges and research directions

Over recent years, the frequency and intensity of droughts have increased and there has been a large drying trend over many parts of the world. Consequently, drought monitoring using big data analytic has gained an explosive interest. Droughts stand among the most damaging natural disasters. It threatens agricultural production, ecological environment, and socio-economic development. For this reason, early warning, accurate evaluation, and efficient prediction are an emergency especially for the nations that are the most menaced by this danger. There are numerous emerging studies addressing big data and its applications in drought monitoring. In fact, big data handle data heterogeneity which is an additive value for the prediction of drought, it offers a view of the different dimensions such as the spatial distribution, the temporal distribution and the severity detection of this phenomenon. Big data analytic and drought are introduced and reviewed in this paper. Besides, this review includes different studies, researches and applications of big data to drought monitoring. Challenges related to data life cycle such as data challenges, data processing challenges and data infrastructure management challenges are also discussed. Finally, we conclude that big data analytic can be beneficial in drought monitoring but there is a need for statistical and artificial intelligence-based approaches.