Image Time Series Analysis
Submission deadline: 2023-12-31
Section Collection Editors

Section Collection Information

Dear Colleagues,

The research of remote sensing time series for Earth observation technology has received widespread attention. The continuous accumulation of remote sensing data provides a strong data guarantee for the research of remote sensing time series image change detection. The corresponding research has also become one of the most popular research directions in remote sensing science, technology and application.

With the continuous launch and operation of new high temporal resolution sensors in the future, researchers will obtain more abundant remote sensing time series data. Therefore, on the one hand, we should continue to introduce new methods and technologies in the field of video processing and machine learning to develop more advanced and effective methods for remote sensing time series image change detection. On the other hand, we should pay attention to the application needs of different industries, give full play to the advantages of time series data, and provide better data and information services for specific application fields, so as to promote the extensive and in-depth application of remote sensing time series data.

Remote sensing time series have the characteristics of seasonal, unstable, regional, multi-scale, spatio-temporal autocorrelation, high dimension and huge data volume. Therefore, how to comprehensively use massive historical data and newly acquired high-frequency remote sensing images to extract and detect change information and apply it to dynamic monitoring of natural phenology, urban expansion, disaster and land use/cover, climate change and carbon neutrality, ecological status assessment and system diagnosis has become the research focus of remote sensing information science. We welcome research articles and reviews in this field.

We look forward to receiving your contributions.


Image Time Series Analysis; Remote Sensing; Image Change Detection; Machine Learning; Detection Methods; Dynamic Monitoring; Natural Phenology; Urban Expansion; Disaster and Land Use/Cover; Climate Change; Carbon Neutrality

Published Paper