Introduction: The accelerated pace of urbanization across the globe has resulted in a marked increase in impervious surface coverage, population density, intensified construction activities, and elevated energy consumption. These factors collectively contribute to the amplification of the Urban Heat Island (UHI) phenomenon. Specifically, the substitution of natural vegetation with artificial, impermeable surfaces, the spatial concentration of anthropogenic activities, rising energy demands, and the disruption of natural cooling processesare among the primary drivers intensifying this effect. Given the necessity for comprehensive spatial and temporal datasets in estimating Land Surface Temperature (LST), recent advancements in remote sensing technologies have significantly enhanced our understanding of the spatiotemporal dynamics of urban heat islands. Numerous studies to date have investigated the relationships between land use/land cover changes, construction density, urban biophysical characteristics, and vegetation cover with surface temperature patterns and the intensity of heat islands. Additionally, the broader socio-environmental implications of elevated surface temperatures—such as increased energy consumption, heightened heat stress, and adverse effects on human health—have been extensively explored. Despite the valuable insights provided by previous research, the majority of studies have primarily concentrated on the urban scale. 
Materials and Methods: In the present study, seasonal variations in heat island intensity across Malayer County were analyzed using Land Surface Temperature (LST) data derived from the MODIS sensor aboard the Terra satellite and the TIRS sensor on Landsat 8, covering the period from 2019 to 2024. Selected surface biophysical parameters were used to guide the analysis. To facilitate meaningful comparison across different images, LST values were normalized between their respective minimum and maximum values. For the purpose of comparing LST across various land use types, land cover was categorized into five classes—orchards and croplands, wastelands, rangelands, water bodies, and urban areas—through the Maximum Likelihood Classification (MLC) method implemented in Google Earth Engine. This classification was based on a composite of all available Landsat OLI images from the year 2023, representing the broader study period. Furthermore, to assess the relationships between surface characteristics and LST, key biophysical indicators including the Normalized Difference Vegetation Index (NDVI), Fractional Vegetation Cover (FVC), and surface albedo were calculated within the Google Earth Engine platform. 
Results: The results indicated that the highest land surface temperatures, both during the day and at night, occurred in August, while the lowest values for both temporal intervals were recorded in January. A statistically significant negative correlation (r = -0.603) was observed between mean surface albedo and monthly NDVI, suggesting an inverse relationship between vegetation greenness and surface reflectivity. Moreover, the Fractional Vegetation Cover (FVC) demonstrated a negative correlation with nighttime land surface temperature (NLST), particularly during months characterized by greater vegetation abundance. Notably, in April, May, and January—especially within rangelands—the correlation coefficient between FVC and NLST reached as low as -0.65, underscoring the cooling effect of vegetative cover on surface temperatures. 
Discussion: The intensity of surface heat island formation is influenced by land use type, such as agricultural practices or irrigation, and exhibits distinct spatial and temporal variations between daytime and nighttime periods. During the daytime, areas characterized by orchards, relatively dense vegetation, and higher elevations exhibit lower surface temperatures, functioning as local cooling zones in contrast to their surrounding environments. Conversely, nighttime observations revealed that the urban core represents the warmest region, whereas rural settlements, adjacent agricultural lands, and orchards maintain comparatively lower temperatures. Additionally, elevated terrains—particularly those located in the northwestern part of the region—consistently emerged as the coldest zones across most seasons. These findings have practical implications for regional planning and management, offering valuable insights for sectors such as agriculture, environmental conservation, climatology, urban and rural development, public health, and social welfare.