Dear Colleagues,

Wetlands are key hubs for biological production and biodiversity, regulating both the water cycle and biogeochemical cycles. The mapping and monitoring of wetlands can be done using remote sensing imagery, terrain data, and/or hydrological modeling. On a detailed scale, high-resolution datasets (<5 m), mainly from LiDAR and optical sensors, are often used for local wetland studies, including textural and object-based image analysis which require iterative calibration and validation. Medium-resolution datasets (~5–200 m), including terrain, optical, thermal, and microwave (SAR) data, have been applied for digital wetland mapping since the 1970s. At this scale, wetlands often act as transitory ecotones and are usually classified in a pixel-wise manner. Most efforts have been directed towards estimating different wetland attributes, rather than delineating wetlands as such. A few attributes (e.g., open water and terrain forms) can be mapped using automated algorithms, even at a global scale. Mapping wetland categories across larger regions is more difficult, and the few hitherto efforts have required extensive user interactions. At a coarser scale, additional datasets from microwave brightness temperature and gravity sensors are available. Routinely monitored properties at this scale include climate, geomorphology, freeze/thaw, soil moisture, inundation, land cover, and vegetation and its phenology. These are often downscaled to support local studies, and are more seldom used for direct mapping and monitoring of wetlands over large regions. Thus, despite an explosive growth in sensors and data availability, combined with strong developments in algorithms, there are gaps in the knowledge about the extent and function of wetlands at different scales. New approaches and ideas for mapping and monitoring wetlands across space and time are needed—ideally using methods that are transparent to wetland functional traits and can be used for understanding the effects on wetlands in times of change.

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• Wetlands
• Mapping
• Monitoring
• Modeling
• Multi-source
• Hybrid
• Landform
• Hydrology
• Biophysical indicators
• Global
• Transparent