Data from earth observing satellites provide a wealth of information to examine global changes. With the availability of high temporal resolution data such as MODIS, Sentinel, remote sensing images can support tracking of environmental change from global to local level, as well as determining human-environment interactions and drivers of change.
Though time series data from multiple sensors acquired with varying spatiotemporal details can be challenging, advancement in technology, cloud computing platform and machine learning algorithms provide opportunities to apply in a wide range of disciplines such as climate science, oceanography, ecology, agriculture, urban planning and many more.
Primarily, the aim of this Article Collection is to utilize satellite-based time series observations in terms of new approaches and innovative applications of real-world issues. Conventional practice using the existing approaches over a local area should be avoided, therefore, applications to showcase wider range is encouraged. We welcome original contribution to the following topics:
- Time series image algorithms and analysis
- Feature extraction and development
- Applications of time series data in land, water, soil, and atmosphere
- Data fusion for time series analysis
- Comprehensive validation of time series products
All manuscripts submitted to this Article Collection will undergo a full peer-review; the Guest Advisors for this collection will not be handling the manuscripts. Please review the journal scope and author submission instructions prior to submitting a manuscript.
The deadline for submitting manuscripts is 1st March 2024.
Please contact Alex Johnson at [email protected] with any queries regarding this Article Collection.
Article Collection Guest Advisors
Quazi Hassan’s research interest include the application of remote sensing in forecasting and monitoring of natural hazards/disasters, such as forest fire, drought, and flooding; use of remote sensing and geographic information system (GIS) techniques in understanding the dynamics of natural resources, such as forestry, agriculture, and water; and integration of remote sensing, GIS, and modelling techniques in addressing issues related to energy and environment.
Ashraf Dewan, an environmental geographer, is an academic in the School of Earth & Planetary Science at Curtin University, Australia. He utilises geospatial intelligence extensively to examine a wide range of environmental issues, both at local and global scales, including climate risk management.
Article Collection key words:
- Time series applications
- Multi-sensor data development
- Soil, air and water
- Machine learning
- Data fusion
