Abstract
This review systematically analyzes 215 papers on the remote sensing monitoring of Spartina alterniflora (S. alterniflora) indexed in the Web of Science database to clarify research progress and future development directions in this field. We applied CiteSpace 6.3.R1 to conduct a bibliometric analysis of remote sensing literature on S. alterniflora, summarizing the technical methodologies across three major domains: distribution dynamics, parameter inversion, and ecosystem functions and services. We traced the technological evolution of multi-source remote sensing and artificial intelligence, and explored application prospects in addressing current challenges and supporting precision management. Our research indicates that the primary challenge lies in the complex and diverse spatiotemporal dynamics of S. alterniflora. To achieve timely monitoring of S. alterniflora changes and large-scale ecological impact assessments, it is essential to fully utilize the advantages of multi-source remote sensing big data. Harnessing artificial intelligence technologies to fully exploit the potential of remote sensing data, enhancing multi-source data fusion, and expanding sample libraries are essential to achieve comprehensive monitoring spanning spatial patterns, ecological processes, and ecosystem functions and services. These efforts will provide a scientific basis and decision-making support for the sustainable management of coastal wetlands.