Progress and Perspectives of Crop Type Mapping With Remote Sensing: A review

Crop type mapping is a core topic in agricultural remote sensing, playing a strategic role in global food security and resource management. Advances in remote sensing and artificial intelligence have shifted crop type mapping from traditional expert-driven approaches toward data- and knowledge-driven paradigms. However, a systematic synthesis that links key components of crop identification across data sources, methods, and application contexts remains limited. In this review, we analyze the evolution of crop type mapping over the past five decades through a large-scale meta-analysis of more than 19,000 publications retrieved from the Web of Science Core Collection. The literature was examined using a large language model-assisted workflow applied to titles, abstracts, keywords, and publication metadata, enabling scalable identification of thematic patterns and methodological trends. Building on this analysis, we organize existing studies within an analytical framework that connects crop sampling strategies, feature engineering, algorithm architectures, and validation practices. The review critically assesses empirical approaches, machine learning and deep learning algorithm, transfer learning strategies, and hybrid modeling frameworks, highlighting recent progress in deep feature extraction, learning under limited data conditions, and modeling in complex agricultural environments. Rather than proposing a universal solution, this review provides structured methodological guidance by clarifying the applicability, strengths, and limitations of different approaches under varying environmental conditions, data availability, and mapping objectives. Key challenges are also identified, including early-season and large-scale crop mapping, cross-regional generalization, data gaps and feature drift caused by cloud cover and precipitation in mountainous regions, and the interpretability of deep learning models. Finally, we outline promising directions for all-weather, multimodal, and scalable crop type mapping, emphasizing the emerging role of large remote sensing models enabled by pre-training, adaptive fine-tuning, and data fusion. By synthesizing methodological trends and empirical evidence, this review offers a coherent reference framework and practical insights to support future research in remote sensing-based crop type mapping.