We are thrilled to present this Research Topic highlighting groundbreaking work by emerging researchers in the field of remote sensing time series analysis. This collection celebrates the contributions of internationally recognized researchers who are in the early stages of their careers and poised to become the future leaders in remote sensing science. Their innovative work is crucial for advancing our understanding of complex temporal dynamics in environmental and Earth system processes.

By assembling a diverse array of perspectives and methodologies from the next generation of leading scientists, we aim to push forward the boundaries of remote sensing research and technology.

We invite contributions that explore topics including, but not limited to:
- Land Use and Land Cover Change Detection
- Analysis of Vegetation Dynamics and Phenology
- Monitoring Climate Change Effects
- Urbanization and its Environmental Impacts
- Ocean and Coastal Monitoring
- Natural Disaster Detection and Response
- Multi-sensor Data Fusion Techniques
- Machine Learning and Artificial Intelligence in Remote Sensing
- Spatiotemporal Pattern Recognition and Forecasting

Authors contributing to this collection are by nomination only, in recognition of their potential to influence the future directions of remote sensing research. Should you wish to nominate a colleague or have your own manuscript considered for inclusion, please either contact one of the topic editors or the journal team at remotesensing@frontiersin.org.

By contributing to this research topic authors will have the opportunity to present their work to a wide audience, gain recognition for their innovations, and foster collaborations. The topic editor team will also be presenting a best paper award , which includes an opportunity for the winner to present their results at a dedicated showcase session organized by Frontiers in Remote Sensing.

Are you interested in contributing a manuscript to this Research Topic?

Rising Stars in Remote Sensing 2025: Advancements in Time Series Analysis