Wetland ecosystems are crucial for sustaining life on Earth, yet they are increasingly threatened by external pressures that disrupt their natural processes and dynamics. Despite their importance, there remain significant knowledge gaps regarding their components, processes, and interactions.

This session seeks studies that leverage multitemporal earth observation data, using passive and active sensors, to better understand wetland processes and components such as:

• Water dynamics
• Vegetation changes
• Disturbances
• Soil moisture
• Biodiversity

Submissions are encouraged on studies of wetland ecosystems (e.g., marshes, swamps, fens, bogs, peatlands, lakes, ponds) with various hydrological regimes (e.g., permanent, temporary). Special interest will be given to contributions that explore multi-sensor approaches or apply machine learning technologies for wetland monitoring, conservation, and restoration.

Themes of interest include but are not limited to:

• Advances in multitemporal remote sensing for wetland studies
• Multi-sensor approaches for wetland dynamics
• Machine learning applications in wetland ecosystem monitoring
• Solutions for wetland conservation and restoration

The session is convened by SEI LA researchers Sebastián Palomino-Ángel and Tania Santos, with co-convener Fernando Jaramillo.