Publication Details

Category Text Publication
Reference Category Journals
DOI 10.3390/atmos13071152
Licence creative commons licence
Title (Primary) Combining spatial and temporal data to create a fine-resolution daily urban air temperature product from remote sensing land surface temperature (LST) data
Author Bird, D.N.; Banzhaf, E.; Knopp, J.; Wu, W.; Jones, L.
Source Titel Atmosphere
Year 2022
Department SUSOZ
Volume 13
Issue 7
Page From art. 1152
Language englisch
Topic T5 Future Landscapes
Keywords nature based solutions (NBS); environmental services (ES); land surface temperature (LST); Landsat; MODIS; Normalized Difference Vegetation Index (NDVI); DisTrad; spatial spectral composition
Abstract Remotely sensed land surface temperature (LST) is often used as a proxy for air temperature in urban heat island studies, particularly to illustrate relative temperature differences between locations. Two sensors are used predominantly in the literature, Landsat and Moderate Resolution Imaging Spectroradiometer (MODIS). However, each has shortcomings that currently limit its utility for many urban applications. Landsat has high spatial resolution but low temporal resolution, and may miss hot days, while MODIS has high temporal resolution but low spatial resolution, which is inadequate to represent the fine grain heterogeneity in cities. In this paper, we overcome this inadequacy by combining high spatial frequency Environmental Services (ES), Landsat-driven Normalized Difference Vegetation Index (NDVI), and MODIS low spatial frequency background LST at different spatial frequency bands (spatial spectral composition). The method is able to provide fine scale LST four times daily on any day of the year. Using data from Paris in 2019 we show that (1) daytime cooling by vegetation reaches a maximum of 30 °C, above which there is no further increase in cooling. In addition, (2) the cooling is relatively local and does not extend further than 200 m beyond the boundary of the NBS. This model can be used to quantify the benefits of NBS in providing cooling in cities.
Persistent UFZ Identifier https://www.ufz.de/index.php?en=20939&ufzPublicationIdentifier=26388
Bird, D.N., Banzhaf, E., Knopp, J., Wu, W., Jones, L. (2022):
Combining spatial and temporal data to create a fine-resolution daily urban air temperature product from remote sensing land surface temperature (LST) data
Atmosphere 13 (7), art. 1152 10.3390/atmos13071152