### UHI Studies with Satellite Data  
Numerous methods are available for analyzing the thermal characteristics of a location, including approaches such as remote sensing, information gathered from stationary meteorological stations, data from reputable organizations, and on-site surveys utilizing portable thermal cameras. Remote sensing observations offer significant advantages in understanding UHI intensity and hotspots, serving as a substitute for on-site surface measurements. The use of satellite sensors allows more uniform and larger sampling than in situ data from meteorological stations or mobile traverses. The emergence of high-resolution Earth-monitoring satellites has enabled the study of the thermal environment and UHI effects from local to global scale. Furthermore, utilizing remote sensing data is more appropriate for observations in densely populated urban areas with a positive thermal balance due to the significant impact of local urban factors. In contrast, standard fixed stations are primarily employed in urban zones. Recently, satellite data have been used extensively to study the thermal environment in urban and rural regions [71; 72; 73; 74; 75]. Multi-temporal data from MODIS (aboard Terra and Aqua) have been extensively used to explore the spatial and temporal characteristics of SUHI along with other factors such as land surface characteristics, and forest and vegetation cover [72; 74; 75; 76; 21; 77; 78]. MODIS LST data have an accuracy of about 1 \({}^{\circ}\)C when compared with stationary on-site measurements in homogeneous rural regions with known emissivity [79; 80; 81; 82]. MODIS-derived LST have been used widely since 2000 to study UHI effect [72; 78; 83]. Validation of satellite data from MODIS (-1 km grid size), NOAA-AVHRR (14, 15, and 16) (-1 km grid size) and Landsat-ETM (60 m grid size) in the Basel urban boundary layer experiment (BUBBLE) demonstrated good correlation of the longwave emissions measured by the sensor with on-site measurements, even in urban environments [84]. The temporal evolution of LST and temperature anomaly over Athens, Greece during a heat wave event was studied using LST datasets from MODIS and SEVIRI [78]. The spatial variations of AVHRR and SPOT HRV-derived LST in Paris during a heat wave showed that the mortality ratio matched the spatial distribution of the highest nighttime LSTs [71]. Relatively higher resolution Landsat TM/ETM+ data and field observations were used to study the urban thermal environment (UHI effect) in Vancouver, Canada [73]. MODIS air temperature is highly useful in UHI research as its moderate resolution is good enough to capture heterogeneity in an urban thermal environment, and its relationship between air temperature and urban fraction can be quantified as urban heat island curve (UHIC), to quantify UHI city by city [74]. Monitoring heat wave events through application of satellite data (LST and EVI) has been found to provide important information for characterizing agriculture vulnerability over Brazil [76]. LST within UHI varies with land surface characteristics at higher resolution [72]. UHI exhibits a non-homogeneous nature in high resolution satellite images due to mixed biophysical landscape characteristics. Regression relationships between LST- and satellite-derived indices such as normalized differential vegetation index (NDVI), normalized differential build-up index (NDBI), and normalized difference bareness index (NDBaI) demonstrated that both NDVI and NDBI have high correlation with the LST variances while NDBaI has a weaker correlation with LST [72]. Relatively smaller cities also exhibit significant differences in mean air temperature between urban and rural areas [85]. The urban thermal environment in Beijing was explored using daily satellite-derived LST products (MODIS LST) from 2000 to 2012, which showed considerable variation of the intensity and distribution of the UHI with season (summer and winter season) and time (daytime and nighttime) [75].