Remote Sensing of Coastal Saline-Alkali Land in China: Progress in Identification, Ecological Restoration, and Sustainable Management

Coastal saline-alkali land represents an important reserve land resource and a highly fragile ecosystem in China. Accurate identification, dynamic monitoring, ecological restoration, and sustainable management of these lands have become major research priorities in the field land-resource management, ecological conservation, and environmental governance. Owing to its broad spatial coverage, high temporal resolution, efficient data acquisition, continuous monitoring capability, and non-destructive observation, remote sensing has become a vital tool for the investigation, monitoring and management of coastal saline-alkali land, providing essential technical support for both scientific research and practical decision-making. This review systematically summarizes recent advances in the application of remote sensing to coastal saline-alkali land in China. First, we examine the development of remote sensing data sources, soil salinity inversion algorithms, and information extraction methods for saline-alkali land identification, with particular emphasis on the contributions of multi-source data fusion, machine learning, and deep learning techniques to improving mapping accuracy and reliability. Second, we review recent applications of remote sensing in ecological restoration, focusing on vegetation recovery monitoring and landscape pattern analysis in restored coastal ecosystems. Third, we summarize the use of remote sensing products in sustainable land management, including regional land-use planning, ecological risk assessment, salinization early warning, and precision management. Finally, we discuss the major challenges that remain, including the limited transferability of salinity inversion models, the insufficient adaptability of remote sensing approaches under complex environmental conditions, and the weak integration of remote sensing technologies with field-scale management practices. Future research should emphasize physics-informed artificial intelligence, multi-source data fusion, and intelligent decision-support systems to enhance the operational application of remote sensing for the sustainable utilization and ecological restoration of coastal saline-alkali land.