Monitoring of air temperature has implications in a wide range of environmental applications. Air temperature commonly measured with meteorological stations provides a high accuracy and temporal resolution for specific monitoring sites. However, in regions with highly variable topography and scare monitoring such as the case of the southern Ecuadorian Andes, these in situ data poorly describe the spatial variability of air temperature. Thus, remote sensing data has a great potential to estimate the spatial distribution of climatological variables due to the spatial continuity of the information. This research aims to estimate the spatial distribution of the monthly air temperature in the Paute river basin for the period 2014–2017, using statistical and geostatistical methods: linear regression (LR), random forest regression (RF), and regression kriging (RK), in addition to evaluate the use of altitude and other auxiliary variables (land surface temperature, latitude, and longitude). Cross-validation showed that RF performed better than LR as well as when using auxiliary variables compared to only the altitude (LR-altitude: RMSE=1.325 °C, P bias= −0.150%, r=0.775; LR-auxiliary variables: RMSE=1.265 °C, P bias=0.000% r=0.795; RF-altitude: RMSE=1.235 °C, P bias =0.200%, r=0.810; RF-auxiliary variables RMSE=1.205 °C, P bias =0.200%, r=0.820). The application of regression kriging was limited since less than 50% of the months had spatial autocorrelation in the regression model residuals. Nevertheless, in these months, regression kriging increased the estimation performance. The outcomes of this research work increase the understanding of the spatial distribution of monthly air temperature in the Paute river basin, which will improve hydrological modeling.

空气温度的监测在广泛的环境应用中具有影响。气象站通常测量的气温为特定的监视站点提供了高精度和时间分辨率。然而，在厄瓜多尔南部安第斯山脉这样的地形变化多变且有恐慌监测的地区，这些原位数据很难描述气温的空间变化。因此，由于信息的空间连续性，遥感数据具有估算气候变量空间分布的巨大潜力。本研究旨在使用统计和地统计方法估算2014-2017年Paute河流域每月气温的空间分布：线性回归（LR），随机森林回归（RF），除了评估海拔高度和其他辅助变量（地面温度，纬度和经度）的使用之外，还可以使用回归克里金法（RK）。交叉验证表明，与仅在海拔高度（LR海拔高度：RMSE = 1.325°C，P偏倚= -0.150％，r = 0.775; LR辅助变量：RMSE = 1.265°C，P bias = 0.000％r = 0.795; 射频高度：RMSE = 1.235°C，P bias = 0.200％，r = 0.810; RF辅助变量RMSE = 1.205°C，P bias = 0.200％，r = 0.820）。回归克里金法的应用受到限制，因为少于50％的月份在回归模型残差中具有空间自相关。尽管如此，在最近几个月中，回归克里金法提高了估计性能。这项研究工作的成果使人们对Paute流域每月气温的空间分布有了更深入的了解，这将改善水文模型。