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.

In recent decades, space-borne hyperspectral sensors have demonstrated significant potential for agricultural monitoring by providing rich spectral information, improved feasibility, and cost-effectiveness compared to multispectral satellite imagery. In this study, we investigated the consistency of two hyperspectral satellite sensors, PRISMA and EnMAP, for agricultural mapping during the 2025 growing season in the Meknes region: one of the most fertile and productive areas for cereals and vegetables at the national level of Morocco. The primary objective was to conduct a comparative analysis of the two datasets and perform a binary classification (crop vs. no-crop) to support land use monitoring, inform decision-making, and enable advanced crop type mapping.Our methodology included a correlation analysis of reflectance values across the visible to near-infrared (VNIR) and shortwave infrared (SWIR) ranges, as well as the evaluation of NDVI indices using two methods: band averaging and hyperspectral NDVI (hNDVI). Classification was performed using three machine learning algorithms—Support Vector Machine (SVM), Random Forest (RF), and CatBoost—based on 16 optimal hyperspectral narrow-bands (i.e., 427, 535, 567, 714, 775, 805, 839, 913, 977, 1175, 1246, 1295, 1717, 2077, 2191, 2343 nm) from PRISMA and EnMAP that best capture the variability of vegetation biophysical and biochemical characteristics.Results demonstrated high Pearson correlation coefficients between the two sensors, with r=0.93 in the VNIR and r=0.91 in the SWIR ranges. NDVI comparison also showed strong consistency results, with correlations of r=0.84 using the hNDVI method and r=0.85 using band averaging. The utilization of optimal hyperspectral narrow-bands achieved superior classification accuracies of 99.95% with PRISMA and 99.65% with EnMAP, with SVM outperforming other algorithms, followed by RF and CatBoost. Moreover, an Explainable Artificial Intelligence (XAI) based analysis indicated that bands in the NIR and SWIR regions were the most critical features driving these high classification performances.These findings highlight the consistency and complementarity of PRISMA and EnMAP for agricultural monitoring. They also demonstrate the potential of these sensors to produce hyperspectral time-series essential for tracking crop phenology and enhancing crop type mapping, thereby overcoming the constraints posed by limited revisit intervals in current imaging spectroscopy missions.

As global food demand increases, farming systems experience heightened pressure to enhance productivity on limited arable land. In Africa, including Morocco, smallholder farms are particularly susceptible to climate variability, soil degradation, and suboptimal farming practices, resulting in yield gaps—the disparity between actual and potential yields under optimal conditions. In Morocco, yield variability is significantly influenced by soil fertility, irrigation, and climate. Consequently, quantitative assessment and mapping of key soil fertility indicators at the field scale are essential for improving yields. Remote sensing data, particularly hyperspectral imagery, presents a cost-effective and time-efficient alternative to traditional soil mapping methods. However, its potential for providing detailed local-scale soil information in Africa remains underexplored. This study utilizes high-resolution PRISMA (PRecursore IperSpettrale della Missione Applicativa) hyperspectral imagery and laboratory-analyzed soil samples to map four key soil properties—cation exchange capacity (CEC), soil organic matter (SOM), available phosphorus (P₂O₅), and exchangeable potassium (K₂O)—in the Ain el Orma agricultural area on the Saïss plateau, Morocco. Despite the advantages of hyperspectral sensors, their high processing complexity, due to redundant or correlated spectral bands, can impede machine learning model accuracy. This study compares the performance of traditional and advanced machine learning algorithms combined with dimensionality reduction techniques—PCA, UMAP, and RFE. Six well-established algorithms (XGBoost, Gradient Boosting, PLSR, SVR, and Random Forest) were evaluated as an initial step in the artificial intelligence workflow, yielding weak to moderate results. For SOM (%), the utilization of RFE resulted in the optimal performance with a substantial improvement in R² from 0.30 (PCA) to 0.36, while the Root Mean Squared Error (RMSE) decreased from 0.52 to 0.39%. Furthermore, the Ratio of Performance to Interquartile Range (RPIQ) for SOM (%) also increased from 1.58 (PCA) to 2.10. In the case of P₂O₅ (mg/kg), PCA emerged as the superior method, yielding an R² of 0.38 compared to 0.37 for RFE and -0.01 for UMAP. The RMSE decreased from 11.92 (RFE) to 11.82. For K₂O (mg/kg), PCA again proved to be the optimal method, with an R² improving to 0.13 from -0.29 with RFE and remaining superior to UMAP's 0.19. The RMSE decreased from 107.33 (RFE) to 88.51%, and the RPIQ increased from 1.50 to 1.82. Lastly, for CEC (meq/100g), PCA delivered the most accurate predictions, improving the R² to 0.68 from 0.60 (RFE) and 0.21 (UMAP). The RMSE was reduced significantly from 2.08 (RFE) to 1.88%, while the RPIQ increased from 2.47 to 2.73. These initial findings underscore the importance of feature selection and dimensionality reduction for developing robust models for soil property estimation using hyperspectral data. Additionally, this study aims to propose advanced innovative AI models capable of enhancing the accuracy of soil maps. In conclusion, the anticipated results are expected to support the creation of accurate soil maps, necessary for spatialized analysis of wheat yield variability using hyperspectral remote sensing imagery, thus contributing to food security and sustainable agricultural practices.