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The rapid advancement of remote sensing technology has significantly enhanced the temporal resolution of remote sensing data. Multitemporal remote sensing image classification can extract richer spatiotemporal features. However, this also presents the challenge of mining massive data features. In response to this challenge, deep learning methods have become prevalent in machine learning and have been widely applied in remote sensing due to their ability to handle large datasets. The combination of remote sensing classification and deep learning has become a trend and has developed rapidly in recent years. However, there is a lack of summary and discussion on the research status and trends in multitemporal images. This review retrieved and screened 170 papers and proposed a research framework for this field. It includes retrieval statistics from existing research, preparation of multitemporal datasets, sample acquisition, an overview of typical models, and a discussion of application status. Finally, this paper discusses current problems and puts forward prospects for the future from three directions: adaptability between deep learning models and multitemporal classification, prospects for high-resolution image applications, and large-scale monitoring and model generalization. The aim is to help readers quickly understand the research process and application status of this field. [ABSTRACT FROM AUTHOR]

Land Use and Land Cover (LULC) mapping products are essential for various environmental studies, including ecological environmental assessments, resource management, urban planning, and climate change. Satellite remote sensing allows for the efficient acquisition of large-scale land use/land cover change information, enabling the creation of accurate LULC mapping products. The article summarizes the basic information, classification systems, application fields, challenges, and trends of 107 satellite LULC products (59 general and 48 thematic products). In terms of spatial scale, there are 56 global, 16 continental and 35 national products, with the spatial resolution ranging from 1 m to 100 km. China's SinoLC-1 is the only product with 1 m spatial resolution. Meanwhile, the temporal frequency of the 54 products is from 1 to 10 years, with 52 single-date products also available. Dynamic World provides a near real-time global LULC mapping service. Significant differences exist between the various LULC products, and the differences in classification schemes are one of the primary factors leading to inconsistency and uncertainty. Most LULC products based on remote sensing use the Land Cover Classification Systems (LCCS) developed by the Food and Agriculture Organization of the United Nations (FAO), but the detailed classification is often absent for categories other than forests. And distinct LULC products find applications across various study domains that span different scales. Currently, the classification system, temporal and spatial resolution, product accuracy, and validation datasets remain constraints to applying LULC products. However, LULC products have been gradually advancing towards finer classification and higher spatial and temporal resolution, with the use of deep learning, cloud computing, and multiple data sources becoming a major trend. This article can assist users in choosing the most suitable LULC mapping product for specific applications and requirements by providing comprehensive information and guidance. [ABSTRACT FROM AUTHOR]