This article provides the first country-scale climatology of the Surface Urban Heat Island (SUHI) investigated across the cities with more than 30,000 inhabitants from Romania using using the MODIS Land Surface Temperature (LST) data set developed within the Land Surface Temperature Climate Change Initiative (CCI) project (LST_cci), funded by European Space Agency. The main findings refer to the relationship between air temperature and LST, temporal variability of the LST over selected urban areas, and the country-scale variations of the SUHI intensity (SUHII), defined as the thermal contrast between the city and its rural proximity. Night- and day-time conditions introduce major differences, the seasonality is an important bias, and the results are presented accordingly. The LST is very well correlated with air temperature (T2m) under clear-sky conditions. At nighttime, the average SUHII is around 0.9 °C in all summer months, while at daytime the average SUHII equals 2.5 °C, in June, 2.3 °C in July, and 1.9 °C in August. The results are relevant for clear-sky conditions and show that the city size (mainly the log transformed area size), climate type (i.e., Cfa, and Cfb, by nightime, and Cfb, and Dfb, by daytime), and land use-land cover (LULC) conditions (i.e., Urban fabric) amplify the magnitude of the SUHII across Romania. 

MODIS-based climatology of the Surface Urban Heat Island at country scale (Romania)

Rapid urbanization significantly affects the near-surface temperature for major cities worldwide. A greater understanding of spatial-temporal changes of the near-surface temperature has given better insights into global issues such as climate change, thermal discomfort, and energy consumption. This remote sensing-based study aims to investigate the spatial-temporal variations of land surface temperature before and after the lockdown, implemented in response to the spread of COVID-19, for three major Indian cities located in different climate zones viz., i) New Delhi (inland), ii) Hyderabad (inland), and iii) Mumbai (coastal). The land surface temperature variation is assessed for 2015-2020 on different land cover types, i.e., buildings, barren land, roads, vegetation, and water. Landsat 8 OLI/TIRS images are employed to classify land use/cover types using Normalized Difference Vegetation Index, Normalized Difference Built-up Index, and Land Surface Temperature maps. Results show that for New Delhi, Hyderabad, and Mumbai, the mean land surface temperature decreases by 5°C, 1.9°C, and 0.26°C, respectively, in April 2020 (after lockdown) in comparison with April 2019. Results for Mumbai do not vary significantly compared to the other two cities. Overall, the performed analysis presents evidence of the impact produced by the COVID-19 lockdown on the surface urban heat island for different land use/cover types. 

Assessing the Effect of COVID-19 Lockdown on Surface Urban Heat Island for Different Land Use /Cover Types Using Remote Sensing

Investigating the relationship between land alteration and the urban heat island of Seville city using multi-temporal Landsat data

Leveraging cloud-based computing and spatial modeling approaches for land surface temperature disparities in response to land cover change: Evidence from Pakistan

Urbanization induced degradation of urban green space and its association to the land surface temperature in a medium-class city in India

Analyzing spatial relationship between land use/land cover (LULC) and land surface temperature (LST) of three urban agglomerations (UAs) of Eastern India

Anamika Saha

Papers

Analyzing spatial relationship between land use/land cover (LULC) and land surface temperature (LST) of three urban agglomerations (UAs) of Eastern India