High-resolution greenspace dynamic data cube from Sentinel-2 satellites over 1028 global major cities

Area of Science:

• Environmental Science
• Urban Ecology
• Remote Sensing

Background:

• Greenspace provides essential ecological and socioeconomic benefits, aligning with Sustainable Development Goal 11.
• Accurate urban greenspace mapping is vital for assessing supply and demand, but seasonal variations are often overlooked.
• Existing urban greenspace studies lack detailed spatiotemporal dynamics, particularly concerning seasonal fluctuations.

Purpose of the Study:

• To develop a high-resolution, dynamic greenspace data cube for global major cities.
• To capture seasonal greenspace fluctuations and their spatiotemporal dynamics.
• To investigate the cooling effect of urban greenspace using satellite data.

Main Methods:

• Utilized Sentinel-2 satellite imagery (10-m resolution, ~5-day revisit) from 2019-2022.
• Employed a spectral unmixing approach and time-series phenology modeling.
• Integrated Landsat land surface temperature data to analyze greenspace cooling effects.

Main Results:

• Generated a four-year (2019-2022), 10-m resolution, 10-day interval greenspace dynamic data cube for 1028 global cities.
• Successfully captured seasonal greenspace dynamics across diverse types, cities, and climate zones.
• Demonstrated the capability to reflect spatiotemporal patterns of greenspace's cooling effect.

Conclusions:

• The developed data cube offers unprecedented insights into urban greenspace spatiotemporal dynamics.
• This resource supports investigations into the complex interactions between urban greenspace and human society.
• Enhances understanding for sustainable urban development and climate change adaptation strategies.