Cities worldwide face escalating climate change risks, underscoring the need for spatially and temporally resolved urban air temperature (Ta) data. While satellite-derived land surface temperature (LST) data have been widely used to estimate Ta, high-resolution hourly Ta estimation in urban areas remains underexplored. Traditional methods typically rely on LST data from geostationary satellites and continuous 24-h Ta observations from weather stations. To address these limitations, we introduce a method that combines a diurnal temperature cycle (DTC) model with a random forest model to estimate monthly mean hourly urban Ta at 1-km resolution. This approach leverages a limited number of diurnal Ta observations from weather stations, MODIS LST data, and ancillary information. The core idea of the proposed method is to transform the estimation of monthly mean hourly 1-km Ta into estimating 1-km DTC model parameters, primarily daily maximum and minimum Ta values. This method capitalizes on MODIS LST's ability to estimate daily Ta extremes and requires only four diurnal Ta observations within a daily cycle to estimate monthly mean hourly 1-km Ta. Station-based five-fold cross-validation yields overall RMSE values consistently below 1.0 °C across nine cities with diverse geographic and climatic contexts. The accuracy achieved with only four diurnal Ta observations rivals that obtained using continuous 24-h Ta observations. Even with a limited training set of ten stations, the overall RMSE remains below 1.0 °C for most cities. The proposed method proves effective for both single-city and multi-city modeling and can estimate daily hourly 1-km Ta under clear-sky conditions. In conclusion, this study offers a feasible, efficient, and versatile method for accurately estimating monthly mean hourly 1-km Ta, which can be readily applied to other cities and holds potential for various applications.

中文翻译：

---

使用昼夜温度循环模型对月平均每小时 1 km 城市气温进行卫星估计


世界各地的城市都面临着不断升级的气候变化风险，这凸显了对空间和时间分辨率城市气温 （Ta） 数据的需求。虽然卫星衍生的地表温度 （LST） 数据已被广泛用于估计 Ta，但城市地区的高分辨率每小时 Ta 估计仍未得到充分开发。传统方法通常依赖于来自对地静止卫星的 LST 数据和来自气象站的连续 24 小时 Ta 观测。为了解决这些限制，我们引入了一种方法，该方法将昼夜温度循环 （DTC） 模型与随机森林模型相结合，以 1 公里的分辨率估计月平均每小时城市 Ta。这种方法利用了来自气象站的有限数量的昼夜 Ta 观测、MODIS LST 数据和辅助信息。所提出的方法的核心思想是将月平均每小时 1 km Ta 的估计转化为估计 1 km DTC 模型参数，主要是日最大值和最小值 Ta 值。这种方法利用了 MODIS LST 估计每日 Ta 极值的能力，并且只需要在日周期内进行四次昼夜 Ta 观测即可估计每月平均每小时 1 公里的 Ta。基于站点的五重交叉验证在具有不同地理和气候背景的 9 个城市中得出的总体 RMSE 值始终低于 1.0 °C。仅用四次昼夜 Ta 观测就达到了准确度，与使用 24 小时连续 Ta 观测所获得的准确性相当。即使使用有限的 10 个站点训练集，大多数城市的整体 RMSE 仍低于 1.0 °C。所提出的方法被证明对单城市和多城市建模都有效，并且可以在晴空条件下估计每天每小时 1 km Ta。 综上所述，本研究为准确估算月平均每小时 1 km Ta 提供了一种可行、高效、通用的方法，可应用于其他城市，具有多种应用潜力。