Air temperatures in urban environments are usually obtained from sparse weather stations that provide limited information with regard to spatial patterns. Effective methods that predict air temperatures (T_air) in urban areas are based on statistical models which utilize remotely sensed and geographic data. This work aims to compute T_air predictions for diurnal and nocturnal time intervals using predictive models that do not exploit information on Land Surface Temperatures. The models are developed based on explanatory variables that describe the urban morphology, land cover and terrain, aggregated at 100 m × 100 m resolution, combined with in situ T_air measurements from urban meteorological stations. The case study is the urban and per-urban area of Heraklion, Greece, where a dense meteorological station network is available since 2016. Moran’s eigenvector filtering and an autoregressive moving average residual specification are implemented to account for spatial and temporal correlations. The statistical models display satisfactory predictive performance, with mean annual Mean Absolute Error (MAE) equal to 0.36 °C, 0.34 °C, 0.42 °C and 0.54 °C, for 11:00–12:00, 14:00–15:00, 22:00–23:00 and 02:00–03:00 (UTC + 2), respectively. The minimum (maximum) MAE for the estimated datasets is 0.22 °C (0.81 °C). The mean annual MAE for all T_air interpolations is 0.42 °C, the mean annual Root Mean Square Error (RMSE) is 0.49 °C and the mean annual bias < 0.01 °C. The time intervals of the analysed measurements coincide with the acquisition times of MODIS and Copernicus Sentinel 3 over Heraklion; hence, the derived estimates can be used in future spaceborne calculations of urban energy budget parameters.

城市环境中的气温通常是从稀疏的气象站获得的，这些气象站提供的空间模式信息有限。预测城市地区气温（T _air）的有效方法是基于利用遥感和地理数据的统计模型。这项工作旨在使用不利用陆地表面温度信息的预测模型来计算昼夜间隔的T_空气预测。这些模型是基于解释性变量开发的，这些变量描述了城市形态，土地覆盖和地形，以100 m×100 m的分辨率汇总，并结合了原位T _air来自城市气象站的测量结果。案例研究是希腊的伊拉克利翁市区和整个城市区域，自2016年以来便有了密集的气象站网络。实施了Moran的特征向量滤波和自回归移动平均残差规范以说明时空相关性。统计模型显示出令人满意的预测性能，在11：00–12：00、14：00–15期间，年平均平均绝对误差（MAE）等于0.36°C，0.34°C，0.42°C和0.54°C： 00、22：00–23：00和02：00–03：00（UTC + 2）。估计数据集的最小（最大）MAE为0.22°C（0.81°C）。所有T_空气的平均年度MAE插值法为0.42°C，年均均方根误差（RMSE）为0.49°C，年均偏差<0.01°C。分析测量的时间间隔与在伊拉克利翁上的MODIS和哥白尼前哨3的采集时间一致；因此，得出的估算值可用于未来的城市能源预算参数的星载计算。