Siberian Tundra Land Surface Temperature and Vegetation Maps from Drone Imagery, 2020-2021

Data from 2020 and 2021 supports a study on how summer drought impacts land surface cooling by tundra vegetation in North-Eastern Siberia. The dataset includes drone-derived multispectral, RGB, and thermal imagery mosaics, land cover training polygons, and meteorological observations. It was created by Nils Rietze and colleagues from the University of Zurich for the associated research paper.

Use Cases

• Analyzing differences in land surface cooling among tundra plant communities based on high-resolution thermal imagery.
• Training land cover classification models for Arctic tundra using the provided training polygons.
• Studying the relationship between meteorological conditions and surface energy fluxes using the included observational data.
• Investigating the impact of the 2020 Siberian heat wave and drought on surface temperature regimes.

Strengths

• Includes multiple data modalities: drone imagery (multispectral, RGB, thermal), land cover polygons, and meteorological observations.
• Data is directly linked to a peer-reviewed scientific study and the associated open-source code repository.
• Focuses on a specific, extreme climatic event (the 2020 Siberian drought) providing a case study for climate impact research.

Limitations

• Row count and exact file sizes are unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred from the associated code or paper.
• Last update date is unknown; freshness unverified.

Provenance

Source

University of Zurich, associated with the research paper 'Summer drought weakens land surface cooling of tundra vegetation'.

Collection Method

Drone data acquisition and meteorological observations collected in North-Eastern Siberian lowland tundra.

Time Range

Covers the drought year 2020 and reference year 2021.

Geography

North-Eastern Siberian lowland tundra.