A country scale assessment of the heat hazard-risk in urban areas

Cheval, Sorin; Dumitrescu, Alexandru; Amihăesei, Vlad-Alexandru; Irașoc, Adrian; Paraschiv, Monica-Gabriela; Ghent, Darren

doi:10.1016/j.buildenv.2022.109892

Cited by 12 publications

(1 citation statement)

References 47 publications

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“…The main objective of the LST_cci project is to provide a long-term LST data record using data from different thermal sensors meeting the Global Climate Observing System (GCOS) threshold for LST accuracy and precision, i.e., 1 K (GCOS, 2016, 2022). LST_cci data have been widely used in different studies, e.g., surface urban heat island [6][7][8], estimation of ice sheet mass [9], stability of thermal remote sensors [10] or evapotranspiration analysis [11]. Thus, in order to ensure the high quality of LST_cci products, all of them are assessed in the project by the LST_cci Validation Team.…”

Section: Introductionmentioning

confidence: 99%

Retrieval Consistency between LST CCI Satellite Data Products over Europe and Africa

et al. 2023

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The assessment of satellite-derived land surface temperature (LST) data is essential to ensure their high quality for climate applications and research. This study intercompared seven LST products (i.e., ATSR_3, MODISA, MODIST, SLSTRA, SLSTRB, SEVIR2 and SEVIR4) of the European Space Agency’s (ESA) LST Climate Change Initiative (LST_cci) project, which are retrieved for polar and geostationary orbit satellites, and three operational LST products: NASA’s MODIS MOD11/MYD11 LST and ESA’s AATSR LST. All data were re-gridded on to a common spatial grid of 0.05° and matched for concurrent overpasses within 5 min. The matched data were analysed over Europe and Africa for monthly and seasonally aggregated median differences and studied for their dependence on land cover class and satellite viewing geometry. For most of the data sets, the results showed an overall agreement within ±2 K for median differences and robust standard deviation (RSD). A seasonal variation of median differences between polar and geostationary orbit sensor data was observed over Europe, which showed higher differences in summer and lower in winter. Over all land cover classes, NASA’s operational MODIS LST products were about 2 K colder than the LST_cci data sets. No seasonal differences were observed for the different land covers, but larger median differences between data sets were seen over bare soil land cover classes. Regarding the viewing geometry, an asymmetric increase of differences with respect to nadir view was observed for day-time data, which is mainly caused by shadow effects. For night-time data, these differences were symmetric and considerably smaller. Overall, despite the differences in the LST retrieval algorithms of the intercompared data sets, a good consistency between the LST_cci data sets was determined.

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