The ever-increasing global population presents a looming threat to food production. To meet growing food demands while minimizing negative impacts on water and soil, agricultural practices must be altered. To make informed decisions, decision-makers require timely, accurate, and efficient crop maps. Remote sensing-based crop mapping faces numerous challenges. However, recent years have seen substantial advances in crop mapping through the use of big data, multi-sensor imagery, the democratization of remote sensing data, and the success of deep learning algorithms. This systematic literature review provides an overview of the history and evolution of crop mapping using remote sensing techniques. It also discusses the latest scientific advances in the field of crop mapping, which involve the use of machine and deep learning models. The review protocol involved the analysis of 386 peer-reviewed publications. The results of the analysis show that areas such as crop rotation mapping, double cropping, and early crop mapping require further exploration. The use of LiDAR as a tool for crop mapping also needs more attention, and hierarchical crop mapping is recommended. This review provides a comprehensive framework for future researchers interested in accurate large-scale crop mapping from multi-source image data and machine and deep learning techniques.

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.

Several drought indices are commonly used to quantify the severity and magnitude of agricultural drought. A comprehensive assessment of temporal and spatial convergences is important. This study evaluates the spatio-temporal concordances of eight drought indices, between October and April, over the period 2003–2021 under the semi-arid conditions of two watersheds in Morocco. These include three monovariate, two bivariate and three composite indices. Taylor diagram, linear regression and Random Forest (RF) clustering were used to evaluate and compare the overall performance of the indices. The results indicate that spatio-temporal variability and spatial heterogeneity of the different agrosystems have little influence on the concordances of composite indices while TCI and VCI have a variable spatio-temporal concordance depending on the spatial extent considered. Nevertheless, depending on the spatial extent considered and the agroclimatic characteristics of each agrosystem, the seasonal correlations between the composite indices and precipitation anomalies, net primary productivity and cereal yield anomalies vary from 0.52 to 0.79, 0.23 to 0.78 and from 0.33 to 0.73, respectively. The correlations between the simple indices and precipitation anomalies, net primary productivity, and cereal yield anomalies range, respectively, from 0.33 to 68 0.13 to 0.72, and from 0 to 0.84. In general, the composite indices showed the best agreement between them and reference indicators but the VCI seems better suited for the assessment of the seasonal profitability of studied agrosystems.

Analysis of the temporal relationship between meteorological drought and hydrological drought is crucial in monitoring water resource availability. This study examined the linear and lagged relationships of the spread of meteorological drought to hydrological drought and their joint effects on low-flow drought variability in the Oum Er-Rbia (OER) watershed. To this end, random forest (RF) model and statistical methods were used to study the characteristics of the temporal relationships between meteorological and hydrological drought indices at monthly, seasonal, and annual scales. The various analyses revealed that the relationship between hydrological and meteorological drought is mainly a function of the time scale considered, the choice of indices to describe each type of drought and the season considered. The hydrological drought of surface water and snow cover is synchronized with the meteorological drought at the monthly, seasonal, and annual scales. In contrast, the transition from meteorological drought to groundwater drought has a lag time of 1 month and is statistically significant up to t − 5 and t + 5, i.e., 6 months. The linear correlation between the annual rainfall deficit and the monthly groundwater storage index was the lowest (0.15) in December and the highest (0.83) in March. This suggests a seasonal response of groundwater drought to the cumulative effects of precipitation deficits. The RF analysis highlighted the importance of the cumulative characteristics of meteorological drought regarding the severity of low-flow drought. The meteorological drought indices at longer time scales have a greater impact on the severity of low-flow drought, with a contribution of approximately 10% per index. However, the relative contributions of meteorological factors and hydrological indices rarely exceed 5%. Thus, by exploring for the first time the complex interactions among the severity of low-flow regimes, meteorological and hydrological drought indices and meteorological factors, this study provides a new perspective for understanding the characteristics of propagation from meteorological to severe hydrological drought.