Time-series dataset on land surface temperature, vegetation, built up areas and other climatic factors in top 20 global cities (2000-2018)

Jamei, Y, Rajagopalan, P and Sun, Q 2019, 'Time-series dataset on land surface temperature, vegetation, built up areas and other climatic factors in top 20 global cities (2000-2018)', Data in Brief, vol. 23, pp. 1-4.


Document type: Journal Article
Collection: Journal Articles

Title Time-series dataset on land surface temperature, vegetation, built up areas and other climatic factors in top 20 global cities (2000-2018)
Author(s) Jamei, Y
Rajagopalan, P
Sun, Q
Year 2019
Journal name Data in Brief
Volume number 23
Start page 1
End page 4
Total pages 4
Publisher Elsevier BV
Abstract Time-series datasets of Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI), Normalized Difference Built Index (NDBI) and other climatic factors are of significance due to their application in tracking climate change in cities. In this paper, new data processing methods are presented using the application of Google Earth Engine (GEE) and GIS. Different variables including LST (both daytime and nighttime), NDVI, NDBI, rainfall, wind speed, evapotranspiration, and surface soil moisture were computed for 18 years from 2000 to 2018 with of use of GEE platform. The study areas cover 20 top global cities which were mentioned in the global cities index report in 2018 [1]. The data sources used on GEE are: MODIS Terra LST and Emissivity 8-Day Global 1km; MODIS Terra Vegetation Indices 16-Day Global 1km; MODIS Terra Surface Reflectance 8-Day Global 500 m; TRMM Monthly Precipitation Estimate data; Terra Monthly Climate; MODIS Terra Net Evapotranspiration 8-Day Global 500 m; and NASA-USDA SMAP Global Soil Moisture Data. Also, to gather information regarding the global cities, United Nations (UN) population dataset, cities elevation and the A.T.Kerney report [1] was used. A short description of GEE functions to retrieve variables is provided. The dataset can be used to investigate the spatial-temporal relationships between LST, vegetation and built-up areas, as well as to provide the global perspective of climate and population change in various cities around the world.
Subject Environmental Monitoring
Keyword(s) Google Earth Engine (GEE)
LST
NDBI
NDVI
Time-series analysis
Top global cities
DOI - identifier 10.1016/j.dib.2019.103803
Copyright notice © 2019 The Author(s)
ISSN 2352-3409
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