Land surface temperature (LST) is considered as one of the most significantly effective factors on the regional climate and ecology, playing an important role in connecting surface energy and water exchange. In mountainous regions, LST reveals lots of inconsistencies due to the effect of such factors as topography, vegetation, solar radiation, etc. We sought to investigate the the temporal and spatial variation LST in different years and its relationship with effective factors in 5 dimensions using Multiple statistical methods, the sepidan region in northwest Iran. The multi-factorial land use, topographic (elevation, slope, aspect), biophysical indices (normalized difference vegetation index (NDVI), normalized difference moisture index (NDMI), normalized difference built up index (NDBI), and modified of normalized difference water index (MNDWI)), socio-economic (fossil fuel CO₂ emissions (FFCOE) and road density(RD)), and climate (temperature and solar radiation) was studied in the current research. To this end, Images of July 1998 and 2017 were extracted from Thematic Mapper (TM5) and Operational Land Imager/Thermal infrared sensors (OLI/TIRS8). Moreover, ordinary least squares regression (OLS), Best subset regression, and Hierarchical Partitioning Analysis (HP) were used to investigate the relationship between LST and relevant effective factors. The results indicated that the temperature range varied from 10 to 53 °C in the time period mentioned. The highest amount of LST was observed in barren land use and the lowest one was found in garden lands. An negative correlation was found between LST and elevation. On the other hand, the highest value of the Laps rate of surface temperature was observed in the southern aspects and the lowest one was observed in the western aspects. Furthermore, the highest and lowest values of lase rate were found in slopes less than 10°, and in 50 to 60-degree slopes, respectively. The results of the OLS correlation indicated a negative correlation between LST and NDVI, NDMI, and MNDWI, and a positive correlation of LST with climatic and socio-economic indicators. LST’s highest and lowest correlations were found to be with vegetation (R² = 0.95) and road density (R² = 0.1). Finally, while in 1998 temperature and vegetation were identified as the most influential factors on LST, it was the elevation that was found to be the most effective factor on LST in 2017 with the effective rate of 82.72%. This study offers a valuable viewpoint on the temporal and spatial variations of LST, their complexity, and the environmental factors that affect them. The viewpoint could, therefore, be used for prospective studies on the analysis of the ecosystem's reaction to climate changes.

地表温度（LST）被认为是影响区域气候和生态的最显着影响因素之一，在连接地表能量和水交换方面发挥着重要作用。在山区，由于地形、植被、太阳辐射等因素的影响，LST 表现出很多不一致性。我们试图研究不同年份 LST 的时空变化及其与 5 个维度的有效因子的关系。多种统计方法，伊朗西北部的sepidan地区。多因素土地利用、地形（高程、坡度、坡向）、生物物理指标（归一化植被指数（NDVI）、归一化差异水分指数（NDMI）、归一化差异累积指数（NDBI）、归一化差异水的修正）指数（MNDWI）），₂排放（FFCOE）和道路密度（RD））和气候（温度和太阳辐射）在当前的研究中进行了研究。为此，从 Thematic Mapper (TM5) 和 Operational Land Imager/Thermal 红外传感器 (OLI/TIRS8) 中提取了 1998 年 7 月和 2017 年 7 月的图像。此外，通过普通最小二乘回归（OLS）、最佳子集回归和层次划分分析（HP）来研究LST与相关有效因素之间的关系。结果表明，在上述时间段内，温度范围在 10 至 53 °C 之间变化。在贫瘠的土地利用中观察到的 LST 量最高，在园地中发现的 LST 量最低。在 LST 和海拔之间发现负相关。另一方面，地表温度 Laps 率最高值出现在南部，最低值出现在西部。此外，激光速率的最高值和最低值分别出现在小于 10° 的斜坡和 50 至 60 度的斜坡中。OLS相关结果表明LST与NDVI、NDMI和MNDWI呈负相关，LST与气候和社会经济指标呈正相关。发现 LST 的最高和最低相关性与植被 (R LST 与气候和社会经济指标呈正相关。发现 LST 的最高和最低相关性与植被 (R LST 与气候和社会经济指标呈正相关。发现 LST 的最高和最低相关性与植被 (R²  = 0.95) 和道路密度 (R ²  = 0.1)。最后，虽然在 1998 年温度和植被被确定为 LST 的最影响因素，但在 2017 年发现海拔是对 LST 最有效的因素，有效率为 82.72%。本研究为 LST 的时空变化、复杂性以及影响它们的环境因素提供了有价值的观点。因此，该观点可用于分析生态系统对气候变化的反应的前瞻性研究。