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

作者：Jianxi Huang, Guilong Xiao, Xuecao Li, Juepeng Zheng, Yelu Zeng, Wei Su, Shuangxi Miao, Anne Gobin · 发表于：IEEE Geoscience and Remote Sensing Magazine · 年份：2026 · DOI：10.1109/mgrs.2025.3648119 · 被引用次数：6 · 研究领域：Remote Sensing in Agriculture、Smart Agriculture and AI、Remote-Sensing Image Classification

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 guid...