重力卫星数据反演地表质量变化的点质量 模型法研究进展.

The successful implementation of multi-generation gravity satellite missions has made significant progress in using gravity satellite observation data to monitor global mass changes. This has improved our understanding of large-scale material migration, global environmental changes in the Earth system, and facilitated research on critical environmental issues such as global sea level change monitoring, glacier melting, and groundwater extraction. Limitations in the accuracy and resolution of satellite instruments, along with factors such as modeling errors, result in global mass changes calculated using gravity satellite data being affected by noise patterns in the form of south-north stripes and signal leakage. While commonly used filtering and smoothing methods effectively mitigate the impact of south-north stripes noise, they exacerbate signal leakage problems. To address these issues and enhance the spatiotemporal resolution and accuracy of surface mass changes in the calculation of gravity satellite data, numerous scholars have developed a point-mass modeling approach based on Newton's law of universal gravitation. These methods establish a direct relationship between surface mass changes and perturbed motion of satellites, employing constraint matrices to tackle strip errors and ill-posed problems arising from downward continuation. This article examines the research progress of the point-mass modeling approach, provides a comprehensive overview of the fundamental theories and various methods employed, analyzes different strategies and characteristics of constraint matrices, and provides a concise summary of the post-processing methods associated with this approach. The comprehensive analysis presented in this article is intended to serve as a valuable reference for the future development and research of the point-mass modeling approach. [ABSTRACT FROM AUTHOR]