Modeling of land surface dynamics driven by time series of remote sensing satellite images

This lecture focuses on how to construct dynamic evolution models using time series of remote sensing satellite images. The dynamic evolution process of land surface is a complex process affected by many factors, and it is difficult to express by the physical model. How can this process be modeled from a data-driven perspective? This lecture explores three types of modeling methods, including spatiotemporal decoupling methods, the control equation discovery methods, and the methods of state variable discovery networks.

 

Ping TANG, Dr., Professor

National Engineering Research Center for Remote Sensing Application

Aerospace Information Research Institute (AIR), CAS

Ping Tang earned Ph.D. degree in mathematics from Beijing Normal University in 1996, followed by a postdoc position in solid state geophysics in Institute of Geophysics, Chinese Academy of Sciences (CAS) until 1998. After that, she was engaged in the research of remote sensing digital image processing in the Institute of Remote Sensing Application, CAS. Currently, she is a professor of National Engineering Research Center for Remote Sensing Application in the Aerospace Information Research Institute (AIR), CAS. Her primary research interests lie in the development of remote sensing data processing tools for environmental analysis and big data analysis techniques.