INTEGRATING REMOTE SENSING AND GIS-BASED MACHINE LEARNING FOR CROP CLASSIFICATION: GLOBAL TRENDS AND EMERGING RESEARCH DIRECTIONS (2018–2026)
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Abstract
Over the past decade, crop classification has become a critical research domain at the intersection of Remote Sensing (RS), Geographic Information Systems (GIS), and Machine-Learning (ML) techniques. This bibliometric and thematic analysis examines 3921 peer-reviewed publications indexed in Scopus (2018–2026) selected through a targeted Boolean query combining RS, GIS, and ML concepts while excluding non-agricultural subject areas. Results reveal a consistent rise in publication activity since 2018, dominated by Earth-observation datasets such as Sentinel-1/2, Landsat-8/9, MODIS, and PlanetScope. Random Forest (RF) and Support Vector Machine (SVM) remain the most frequently applied supervised classifiers, whereas deep-learning frameworks (CNNs, U-Net, ResNet) show rapid adoption for multisensor and time-series analysis. Thematic mapping indicates a transition from traditional spectral-index-based methods toward hybrid approaches integrating SAR-optical fusion, object-based classification, and GeoAI-driven decision systems. Keyword-co-occurrence networks highlight four primary clusters: (1) machine-learning and algorithmic optimization, (2) multi-sensor data fusion and spectral indices, (3) spatial modeling and GIS-based validation, and (4) applications in crop monitoring, yield estimation, and climate-impact assessment. The United States, China, India, and European countries lead in research output, while Central Asia and Africa exhibit emerging contributions. Overall, this study outlines global research patterns, methodological evolution, and future priorities for RS-GIS-based crop classification, emphasizing reproducibility, explainable AI, and integration of ground-truth datasets to enhance agricultural decision-support systems.
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