Accurate estimations of daily mean land surface temperature (LST) are important for investigating the urban heat island effect, land-atmosphere energy exchanges, and global climate change. Moderate Resolution Imaging Spectroradiometer (MODIS) sensors can provide up to four instantaneous LSTs of a single day across the world. However, numerous studies, such as those on climate change and hydrology, require the input of daily mean LSTs rather than instantaneous value. In this paper, we propose a practical method to estimate the daily mean LST using instantaneous LST products derived from MODIS. Based on the in situ LST measurements collected from 235 sites distributed globally, multiple linear regressions of two to four valid instantaneous LSTs at different MODIS observations moments (at least one daytime and one nighttime observations) can provide reliable estimates of daily mean LSTs under all-weather conditions with a root mean square error (RMSE) of less than 1.60 K. In addition, the conditions of clouds would affect the estimation accuracy of daily mean LST to a certain extent. Subsequently, an algorithm is proposed to produce the most complete coverage of daily mean LSTs from instantaneous LST products derived from MODIS. Validation results with in situ measurements show that the daily mean LSTs estimated from the MOD11A1 and MYD11A1 products are similar to the daily mean of the in situ LST, with an RMSE of 2.17 K. Furthermore, the daily mean LST derived from MODIS data is successfully applied to calculate the global annual cycle parameters (ACPs) in the annual temperature cycle (ATC) model. The results of this study show that the daily mean LST can be retrieved accurately from combinations of daytime and nighttime LSTs derived from MODIS. We expect that our findings will be useful for various applications involving global LST trend analysis and climate change.

每日平均地表温度 (LST) 的准确估计对于研究城市热岛效应、陆地-大气能量交换和全球气候变化非常重要。中等分辨率成像光谱仪 (MODIS) 传感器可以在全球范围内提供多达一天的四个瞬时 LST。然而，许多研究，例如气候变化和水文研究，需要输入每日平均 LST 而不是瞬时值。在本文中，我们提出了一种使用从 MODIS 导出的瞬时 LST 产品来估计每日平均 LST 的实用方法。基于从分布于全球的 235 个站点收集的原位 LST 测量结果，在不同 MODIS 观测时刻（至少一个白天和一个夜间观测）的两到四个有效瞬时 LST 的多元线性回归可以提供全天候条件下每日平均 LST 的可靠估计，均方根误差 (RMSE) 小于1.60 K。另外，云的情况也会在一定程度上影响日平均LST的估计精度。随后，提出了一种算法，以从 MODIS 派生的瞬时 LST 产品中生成最完整的日平均 LST 覆盖范围。原位测量的验证结果表明，从 MOD11A1 和 MYD11A1 产品估计的每日平均 LST 与原位 LST 的每日平均值相似，RMSE 为 2.17 K。此外，从 MODIS 数据导出的日平均 LST 成功应用于计算年温度循环 (ATC) 模型中的全球年循环参数 (ACP)。这项研究的结果表明，可以从 MODIS 导出的白天和夜间 LST 的组合中准确地检索出每日平均 LST。我们希望我们的研究结果对涉及全球 LST 趋势分析和气候变化的各种应用有用。