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.

近年来，干旱的频率和强度增加了，并且在世界许多地方都有很大的干旱趋势。因此，使用大数据分析进行干旱监测已经引起了爆炸性的兴趣。干旱是最具破坏性的自然灾害之一。它威胁着农业生产，生态环境和社会经济发展。因此，预警，准确评估和有效预测是紧急情况，特别是对于那些受到这种危险最大威胁的国家。有大量针对大数据及其在干旱监测中的应用的新兴研究。实际上，大数据处理的数据异质性是干旱预测的附加值，它提供了不同维度的视图，例如空间分布，这种现象的时间分布和严重性检测。本文介绍并回顾了大数据分析和干旱。此外，本文还对大数据在干旱监测中的不同研究，研究和应用进行了综述。还讨论了与数据生命周期相关的挑战，例如数据挑战，数据处理挑战和数据基础架构管理挑战。最后，我们得出结论，大数据分析在干旱监测中可能是有益的，但是需要基于统计和人工智能的方法。还讨论了与数据生命周期相关的挑战，例如数据挑战，数据处理挑战和数据基础架构管理挑战。最后，我们得出结论，大数据分析在干旱监测中可能是有益的，但是需要基于统计和人工智能的方法。还讨论了与数据生命周期相关的挑战，例如数据挑战，数据处理挑战和数据基础架构管理挑战。最后，我们得出结论，大数据分析在干旱监测中可能是有益的，但是需要基于统计和人工智能的方法。