Abstract: Abstract: Pine Wilt Disease (PWD), a devastating pine tree disease, has caused a serious impact on the national biosecurity, ecological security, and forestry economy. In this study, a systematic review of the research progress was made on the history of remote sensing monitoring of PWD in recent years under the object level classification of remote sensing monitoring using the literature retrieved and screened by the Web of Science (WoS) and China National Knowledge Infrastructure (CNKI). Some suggestions and outlooks were also proposed for the existing problems, which could provide reference for the technical reference and auxiliary decision-making on forestry. It was found that: 1) About 70% of the literature was published in the research field after 2017. It infers that the remote sensing monitoring of pine wood nematode has been a research hotspot in the past five years. 2) From the viewpoint of the carrier platform, the satellite, airborne, and ground datasets accounted for 17.1%, 75.6%, and 7.3% of the research data on the remote sensing monitoring of PWD, respectively. Particularly, there was the vast majority of airborne data represented by unmanned aerial vehicles (UAV). From the viewpoint of data spectral type, 44%, 34.1%, 17.1%, and 4.9% of the studies used RGB, multispectral, hyperspectral, and LIDAR data, respectively. Therefore, the RGB and multispectral datasets were dominated in the remote sensing monitoring of PWD. 3) Single plants were mainly used as the granularity of remote sensing monitoring of PWD. The diseased trees were classified into the two, three, four, five, and six categories, accounting for 53%, 23%, 15%, 6%, and 3%, respectively. There were diverse category systems with vague relationships between them. 4) Machine learning and deep learning dominated the remote sensing monitoring of PWD. Furthermore, machine learning and deep learning shared their own advantages and fail to replace each other. Furthermore, the aerospace remote sensing survey with the UAV and satellite sensors as the data sources greatly improved the efficiency of PWD epidemic increment control and stock abatement work. However, the following challenges remained: 1) A single data source cannot fully meet the harsh requirement of large-scale and fine-grained monitoring in recent years. 2) Disorganized disease classification systems led to the irregularity and specification of data for machine learning and deep learning. 3) It is still lacking in long-term series monitoring with the high-time resolution. Finally, three recommendations were proposed for the future real-time and intelligent remote sensing monitoring of PWD: 1) To explore the satellite and aerial data fusion for the large-scale and fine-grained disease monitoring; 2) To clarify the disease monitoring category system, and then to construct the relevant spectral library and sample library datasets; 3) To develop the high-frequency and long-time series remote sensing monitoring products for a general release mechanism for the PWD.