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Accurate seasonal forecasting of cereal yields is an important decision support tool for countries, such as Morocco, that are not self-sufficient in order to predict, as early as possible, importation needs. This study aims to develop an early forecasting model of cereal yields (soft wheat, barley and durum wheat) at the scale of the agricultural province considering the 15 most productive over 2000–2017 (i.e., 15 × 18 = 270 yields values). To this objective, we built on previous works that showed a tight linkage between cereal yields and various datasets including weather data (rainfall and air temperature), regional climate indices (North Atlantic Oscillation in particular), and drought indices derived from satellite observations in different wavelengths. The combination of the latter three data sets is assessed to predict cereal yields using linear (Multiple Linear Regression, MLR) and non-linear (Support Vector Machine, SVM; Random Forest, RF, and eXtreme Gradient Boost, XGBoost) machine learning algorithms. The calibration of the algorithmic parameters of the different approaches are carried out using a 5-fold cross validation technique and a leave-one-out method is implemented for model validation. The statistical metrics of the models are first analyzed as a function of the input datasets that are used, and as a function of the lead times, from 4 months to 2 months before harvest. The results show that combining data from multiple sources outperformed models based on one dataset only. In addition, the satellite drought indices are a major source of information for cereal prediction when the forecasting is carried out close to harvest (2 months before), while weather data and, to a lesser extent, climate indices, are key variables for earlier predictions. The best models can accurately predict yield in January (4 months before harvest) with an R2 = 0.88 and RMSE around 0.22 t. ha−1. The XGBoost method exhibited the best metrics. Finally, training a specific model separately for each group of provinces, instead of one global model, improved the prediction performance by reducing the RMSE by 10% to 35% depending on the provinces. In conclusion, the results of this study pointed out that combining remote sensing drought indices with climate and weather variables using a machine learning technique is a promising approach for cereal yield forecasting.

Accurate and efficient crop maps are essential for decision-makers to improve agricultural monitoring and management, thereby ensuring food security. The integration of advanced artificial intelligence (AI) models with hyperspectral remote sensing data, which provide richer spectral information than multispectral imaging, has proven highly effective in the precise discrimination of crop types. This systematic review examines the evolution of hyperspectral platforms, from Unmanned Aerial Vehicle (UAV)-mounted sensors to space-borne satellites (e.g., EnMAP, PRISMA), and explores recent scientific advances in AI methodologies for crop mapping. A review protocol was applied to identify 47 studies from databases of peer-reviewed scientific publications, focusing on hyperspectral sensors, input features, and classification architectures. The analysis highlights the significant contributions of Deep Learning (DL) models, particularly Vision Transformers (ViTs) and hybrid architectures, in improving classification accuracy. However, the review also identifies critical gaps, including the under-utilization of hyperspectral space-borne imaging, the limited integration of multi-sensor data, and the need for advanced modeling approaches such as Graph Neural Networks (GNNs)-based methods and geospatial foundation models (GFMs) for large-scale crop type mapping. Furthermore, the findings highlight the importance of developing scalable, interpretable, and transparent models to maximize the potential of hyperspectral imaging (HSI), particularly in underrepresented regions such as Africa, where research remains limited. This review provides valuable insights to guide future researchers in adopting HSI and advanced AI models for reliable large-scale crop mapping, contributing to sustainable agriculture and global food security.

Africa’s rapidly growing population is driving unprecedented demands on agricultural production systems. However, agricultural yields in Africa are far below their potential. One of the challenges leading to low productivity is Africa‘s poor soil quality. Effective soil fertility management is an essential key factor for optimizing agricultural productivity while ensuring environmental sustainability. Key soil fertility properties—such as soil organic carbon (SOC), nutrient levels (i.e., nitrogen (N), phosphorus (P), potassium (K), moisture retention (MR) or moisture content (MC), and soil texture (clay, sand, and loam fractions)—are critical factors influencing crop yield. In this context, this study conducts an extensive literature review on the use of hyperspectral remote sensing technologies, with a particular focus on freely accessible hyperspectral remote sensing data (e.g., PRISMA, EnMAP), as well as an evaluation of advanced Artificial Intelligence (AI) models for analyzing and processing spectral data to map soil attributes. More specifically, the study examined progress in applying hyperspectral remote sensing technologies for monitoring and mapping soil properties in Africa over the last 15 years (2008–2024). Our results demonstrated that (i) only very few studies have explored high-resolution remote sensing sensors (i.e., hyperspectral satellite sensors) for soil property mapping in Africa; (ii) there is a considerable value in AI approaches for estimating and mapping soil attributes, with a strong recommendation to further explore the potential of deep learning techniques; (iii) despite advancements in AI-based methodologies and the availability of hyperspectral sensors, their combined application remains underexplored in the African context. To our knowledge, no studies have yet integrated these technologies for soil property mapping in Africa. This review also highlights the potential of adopting hyperspectral data (i.e., encompassing both imaging and spectroscopy) integrated with advanced AI models to enhance the accurate mapping of soil fertility properties in Africa, thereby constituting a base for addressing the question of yield gap.

There is growing demand for increased accuracy in sub–seasonal weather forecast. This calls for understanding of characteristics of precipitation variability in association with global teleconnections. This study analyzes the influence of major global teleconnections on spatial and temporal variability of precipitation in Morocco in the wet season over the period 1980–2015. We consider a suite of climate indices (CIs), including the North Atlantic Oscillation (NAO), various forms of the El Niño–Southern Oscillation (ENSO), the East Atlantic Oscillation (EA), the Mediterranean Oscillation (MO), and the Western Mediterranean Oscillation (WeMO). In addition, we analyze the Pacific Decadal Oscillation (PDO), the Atlantic Multidecadal Oscillation (AMO), the Scandinavian (SCAND) pattern, and the East Atlantic/Western Russia (EATL/WRUS) pattern. We identify the dominant teleconnections and examine their seasonal and regional impacts across Morocco using regression analyses, empirical orthogonal function (EOF) analysis, and wavelet coherence. Linear regression of sea surface temperature (SST) and geopotential height fields onto three identified precipitation zones reveals varying patterns of oceanic and atmospheric variability, with significant differences between winter and spring. Additionally, regressing precipitation against natural climate variability modes (CIs) indicates a complex interplay of global teleconnections. The NAO and MO emerge as the primary drivers of winter precipitation, whereas spring precipitation is more strongly modulated by the EA pattern and the WeMO. Secondary patterns such as SCAND and EATL/WRUS also exert region–specific influences. Notably, the influence of ENSO on precipitation evolved over the study period. ENSO–related impacts have strengthened post 2000, coinciding with enhanced Pacific–Atlantic coupling. Wavelet coherence analysis reveals that since 2000, Pacific signals (ENSO/PDO) have become more in phase with Atlantic variability (NAO/AMO), amplifying their effect on precipitation. These findings clarify the seasonal and regional teleconnection dynamics governing precipitation in Morocco and highlight an emerging influence of Pacific climate variability in the twenty-first century. This improved understanding can inform seasonal forecasting to support climate adaptation efforts in North Africa.

Water stress is one of the factors controlling agricultural land salinization and is also a major problem worldwide. According to FAO and the most recent estimates, it already affects more than 400 million hectares. The Tafilalet plain in Southeastern Morocco suffers from soil salinization. In this regard, the GIS tools and remote sensing were used in the processing of 19 satellite images acquired from Landsat 4–5, (Landsat 7), (Landsat 8), and (Sentinel 2) sensors. The most used indices in the literature were (16 indices) tested and correlated with the results obtained from 25 samples taken from the first soil horizon at a constant depth of 0.20 m from the 2018 campaign. The linear model, at first, allows the selection of five better indices of the soil salinity discrimination (SI-Khan, VSSI, BI, S3, and SI-Dehni). These last indices were the subject of the application of a logarithmic model and polynomial models of degree two and four to increase the prediction of saline soil.. After studies and analysis, we concluded that the second-degree polynomial model of the salinity index (SI-KHAN) is the most efficient one for detecting and mapping soil salinity in the Tafilalet oasis, with a coefficient of determination (R2) and the Nash–Sutcliffe efficiency (NSE) equal to 0.93 and 0.86, respectively. Percent bias (PBIAS) calculated for this model equal was 1.868% < 10%, and the low value of the root mean square error (RMSE) confirms its very good performance. The drought cyclicity led to the intensification of the soil salinization process and accelerated soil degradation. The standardized precipitation anomaly index (SPAI) is strongly correlated to soil salinity. The hydroclimate condition is the factor that further controls this phenomenon. An increase in salinized surfaces is observed during the periods of 1984–1996 and 2000–2005, which cover a surface of 11.50 and 24.20 km2, respectively, while a decrease of about 50% is observed during the periods of 1996–2000 and 2005–2018.

Background Climate change has devastating impacts on agriculture, increasing the yield gap for most crops, especially in developing nations. This is likely to worsen food insecurity in some countries, calling for efforts to close the yield gap as much as possible. Estimating the yield gap and its drivers is essential for devising strategies to increase yields. This study quantifies the wheat yield gap in Morocco's five major wheat production regions. It analyzes the historical sensitivity of wheat yield to temperature, precipitation, and soil moisture, which are important factors affecting agricultural productivity. Furthermore, it evaluates how these yield gaps impact the revenue of producers in these regions. This analysis was conducted using datasets, including the Global Dataset of Historical Yield (GDHY) for yield gap assessment, soil moisture data, ERA5 reanalysis data, and CHIRPS datasets for climate assessment from 1982 to 2016. Pearson correlation and multiple linear regression analyses were employed to reflect the variation characteristics of wheat yield and to identify the impacts of precipitation, temperature, and soil moisture on wheat yield. Results High regional differences in wheat yield gaps were observed, with values ranging from 1.64 t/ha in Casablanca Settat to 4.12 t/ha in Marrakech Safi, and temporal variability ranging from 9 to 18%. Wheat yields were found to be strongly correlated with rainfall, particularly from December to March. Temperature fluctuations had a significant negative impact on wheat yield across the regions. Soil moisture was positively correlated with wheat yields throughout all growing periods, showing the strongest impacts during the early vegetative development phase. Additionally, losses due to wheat yield gaps were considerable, ranging between $ 194 and 891 per hectare. The revenue loss due to Yield Gap I ranged from 49 to 71%, while the loss due to Yield Gap II ranged from 240 to 444%, depending on the method used to calculate the wheat yield gap. Conclusions Results reveal gaps in wheat yield, forming a basis for process-based modeling to understand crop yield gap drivers. Understanding yield gap drivers will play a pivotal role in evidence-based intervention strategies to enhance yields. By applying such strategies, it is possible to not only manage and reduce the variability in wheat production, but also ensure sustainable agricultural practices and achieve food security in Morocco and beyond.

This study evaluates the use of remote sensing data to improve the understanding of groundwater resources in climate-sensitive regions with limited data availability and increasing agricultural water demands. The research focuses on estimating groundwater reserve dynamics in two major river basins in Morocco, characterized by significant local variability. The study employs data from Gravity Recovery and Climate Experiment satellite (GRACE) and ERA5-Land reanalysis. Two GRACE terrestrial water storage (TWS) products, CSR Mascon and JPL Mascon (RL06), were analyzed, along with auxiliary datasets generated from ERA5-Land, including precipitation, evapotranspiration, and surface runoff. The results show that both GRACE TWS products exhibit strong correlations with groundwater reserves, with correlation coefficients reaching up to 0.96 in the Oum Er-rbia River Basin and 0.95 in the Tensift River Basin (TRB). The root mean square errors (RMSE) were 0.99 cm and 0.88 cm, respectively. GRACE-derived groundwater storage (GWS) demonstrated a moderate correlation with observed groundwater levels in OERRB (R = 0.59, RMSE = 0.82), but a weaker correlation in TRB (R = 0.30, RMSE = 1.01). On the other hand, ERA5-Land-derived GWS showed a stronger correlation with groundwater levels in OERRB (R = 0.72, RMSE = 0.51) and a moderate correlation in TRB (R = 0.63, RMSE = 0.59). The findings suggest that ERA5-Land may provide more accurate assessments of groundwater storage anomalies, particularly in regions with significant local-scale variability in land and water use. High-resolution datasets like ERA5-land are, therefore, more recommended for addressing local-scale heterogeneity in regions with contrasted complexities in groundwater storage characteristics.

Soil moisture (SM) data are required at high spatio-temporal resolution—typically the crop field scale every 3–6 days—for agricultural and hydrological purposes. To provide such high-resolution SM data, many remote sensing methods have been developed from passive microwave, active microwave and thermal data. Despite the pros and cons of each technique in terms of spatio-temporal resolution and their sensitivity to perturbing factors such as vegetation cover, soil roughness and meteorological conditions, there is currently no synergistic approach that takes advantage of all relevant (passive, active microwave and thermal) remote sensing data. In this context, the objective of the paper is to develop a new algorithm that combines SMAP L-band passive microwave, MODIS/Landsat optical/thermal and Sentinel-1 C-band radar data to provide SM data at the field scale at the observation frequency of Sentinel-1. In practice, it is a three-step procedure in which: (1) the 36 km resolution SMAP SM data are disaggregated at 100 m resolution using MODIS/Landsat optical/thermal data on clear sky days, (2) the 100 m resolution disaggregated SM data set is used to calibrate a radar-based SM retrieval model and (3) the so-calibrated radar model is run at field scale on each Sentinel-1 overpass. The calibration approach also uses a vegetation descriptor as ancillary data that is derived either from optical (Sentinel-2) or radar (Sentinel-1) data. Two radar models (an empirical linear regression model and a non-linear semi-empirical formulation derived from the water cloud model) are tested using three vegetation descriptors (NDVI, polarization ratio (PR) and radar coherence (CO)) separately. Both models are applied over three experimental irrigated and rainfed wheat crop sites in central Morocco. The field-scale temporal correlation between predicted and in situ SM is in the range of 0.66–0.81 depending on the retrieval configuration. Based on this data set, the linear radar model using PR as a vegetation descriptor offers a relatively good compromise between precision and robustness all throughout the agricultural season with only three parameters to set. The proposed synergistical approach combining multi-resolution/multi-sensor SM-relevant data offers the advantage of not requiring in situ measurements for calibration.

Due to its important spatiotemporal variability, accurate rainfall monitoring is one of the most difficult issues in semi-arid mountainous environments. Moreover, due to the inconsistent distribution of gauge measurement, the availability of precipitation data is not always secured and totally reliable at the instantaneous time step. As a result, earth observation of precipitation estimations could be an alternative for overcoming this restriction. The current study presents a framework for either the hydro-statistical evaluation and bias correction of the Global Precipitation Measurement (GPM) Integrated Multi-SatellitE Retrievals version 06 Early (IMERG-E), Late (IMERG-L), and Final (IMERG-F) products. On a sub-daily duration, from the Taferiat rain gauge-based station, which was used as a benchmark from 1 September 2014 to 31 August 2018. Statistical analysis was performed to examine each precipitation product’s performance. The results showed that all Post_Real_Time and Real_Time IMERG had a high level of awareness accuracy. The IMERG-L results were statistically similar to the gauge data, succeeded by the IMERG-F and IMERG-E. The Cumulative Distribution Function (CDF) has been employed to adjust the precipitation values of the three IMERG products in order to decrease bias estimation. The three products were then integrated into the “HEC-HMS” hydrological model to assess their dependability in flow modeling. Six flood occurrences were calibrated and validated for each product at 30-minute time steps. With a mean Nash-Sutcliffe coefficient of NSE 0.82, the calibration findings demonstrate that IMERG-F provides satisfactory hydrological performance. With an NSE = 0.80, IMERG-L displayed good hydrological utility, slightly better than IMERG-E with an NSE = 0.77. However, when the flood events were validated using the initial soil conditions, IMERG F and IMERG E overestimated the discharge by 13% and 10%, respectively. While IMERG L passed the validation phase with an average score of NSE = 0.69.

The main goal of this work was to evaluate the potential of the Shuttleworth–Wallace (SW) model for mapping actual crop evapotranspiration (ET) over complex surface located in the foothill of the Atlas Mountain (Morocco). This model needs many input variables to compute soil (rss) and vegetation (rsv) resistances, which are often difficult to estimate at large scale particularly soil moisture. In this study, a new approach to spatialize rss and rsv based on two thermal-based proxy variables was proposed. Land Surface Temperature (LST) and Normalized Difference Vegetation Index (NDVI) derived from Landsat data were combined with the endmember temperatures for soil (Tsmin and Tsmax) and vegetation (Tvmin and Tvmax), which were simulated by a surface energy balance model, to compute the soil (Ts) and the vegetation (Tv) temperatures. Based on these temperatures, two thermal proxies (SIss for soil and SIsv for vegetation) were calculated and related to rss and rsv, with an empirical exponential relationship [with a correlation coefficient (R) of about 0.6 and 0.5 for soil and vegetation, respectively]. The proposed approach was initially evaluated at a local scale, by comparing the results to observations by an eddy covariance system installed over an area planted with olive trees intercropped with wheat. In a second step, the new approach was applied over a large area which contains a mixed vegetation (tall and short) crossed by a river to derive rss and rsv, and thereafter to estimate ET. A Large aperture scintillometer (LAS) installed over a line transect of 1.4 km and spanning the total area was used to validate the obtained ET. The comparison confirmed the ability of the proposed approach to provide satisfactory ET maps with an RMSE, bias and R2 equal to 0.08 mm/h, 0.06 mm/h and 0.80, respectively.