The land surface temperature (LST) and land use land cover (LULC) are the major components of climate- and environment-related studies. The objective of this study was to assess the relationship of LST with remotely sensed LULC-derived vegetation indices during 2018 at the global, latitudinal and continental scales. Moderate resolution imaging spectroradiometer (MODIS) Aqua daytime LST and eight LULC MODIS indices (NDVI, EVI, LAI, DSI, NDWI, albedo, NDSI, NDBI) were processed using Earth Engine Code Editor. The analysis was conducted using correlation coefficient and significance of the relationship of variables based on 2050 randomly selected points at the global scale. Based on the univariate and geographically weighted regression methods, the research confirmed that vegetation greenness (NDVI), leaf water content (NDWI) and snow cover (DSI) are the codominant drivers of decreasing LST at the global scale including Europe, Asia, South America and North America at the continental scale. Snow cover during winter and vegetation greenness in summer seasons control the global LST. Although albedo shows an inverse relationship, NDBI and NDSI displayed a positive relationship to LST at the global scale. In conclusion, temporal seasonal and inter-annual dynamics of LST in response to snow cover and vegetation properties (greenness, moisture) should be focused on understanding and regulating LST at varying scales.

土地表面温度（LST）和土地利用土地覆盖（LULC）是与气候和环境有关的研究的主要组成部分。这项研究的目的是评估2018年全球，纬度和大陆尺度上LST与遥感LULC衍生的植被指数之间的关系。使用Earth Engine Code Editor处理了中等分辨率成像光谱仪（MODIS）的Aqua白天LST和八个LULC MODIS指标（NDVI，EVI，LAI，DSI，NDWI，反照率，NDSI，NDBI）。使用相关系数和变量关系的显着性进行了分析，该变量基于全球规模中的2050个随机选择的点。根据单变量和地理加权回归方法，研究证实了植被的绿色度（NDVI）叶片含水量（NDWI）和积雪（DSI）是导致全球范围内LST下降的主要驱动因素，包括欧洲大陆，欧洲，亚洲，南美和北美。冬季的积雪和夏季的植被绿色控制了全球LST。尽管反照率显示出反比关系，但在全球范围内，NDBI和NDSI与LST表现出正相关。总之，响应于积雪和植被特性（绿色，湿度）的LST的季节性和年度动态，应集中于在不同尺度上理解和调节LST。尽管反照率显示出反比关系，但在全球范围内，NDBI和NDSI与LST表现出正相关。总之，响应于积雪和植被特性（绿色，湿度）的LST的季节性和年度动态，应集中于在不同尺度上理解和调节LST。尽管反照率显示出反比关系，但在全球范围内，NDBI和NDSI与LST表现出正相关。总之，响应于积雪和植被特性（绿色，湿度）的LST的季节性和年度动态，应集中于在不同尺度上理解和调节LST。