Monitoring of variables like temperature, precipitation, and air quality is performed to determine their current situation, exhibit the presence of trends and occurrence of outliers. These variables are measured at specific locations and to obtain a full estimation map, we need to predict values at unknown locations. This study focuses on making a minimum air temperature map using copula interpolation with the spline family. Minimum temperature observations for January 2017, all months of 2017, and seasonal averages are analysed over the Euphrates Basin in Turkey. The minimum temperature observations have a high variability due to the varying topography of the area, ranging between -2 C${}^{{}^{\circ }}$ and +14 C${}^{{}^{\circ }}$ in whole of 2017. The interpolation methods incorporated the above mean sea level elevation map and remotely-sensed land surface temperature. We evaluated the accuracy of the predictions using ten-fold cross-validation and compare copula interpolation with External Drift Kriging (KED). The study shows that copulas provided more accurate predictions than KED for most of the months, and for the summer and autumn seasons, whereas KED produced high accurate predictions for January 2017. Results of this study indicate that copulas are able to detect variation in minimum temperatures accurately in areas where topography and observation values are highly variable. Further work will focus on copula-based space–time interpolation techniques for the minimum temperature mapping.

进行诸如温度，降水和空气质量等变量的监视以确定它们的当前状况，显示趋势的存在和异常值的发生。这些变量在特定位置进行测量并获得完整的估算图，我们需要预测未知位置的值。这项研究的重点是通过样条族的copula插值法制作最低气温图。分析了土耳其幼发拉底海盆地2017年1月，2017年所有月份的最低温度观测值和季节性平均值。最低温度观测值由于该区域的地形变化而变化很大，范围在-2 C之间${}^{{}^{\circ }}$ 和+14 C${}^{{}^{\circ }}$整个2017年。插值方法结合了上述平均海平面高程图和遥感地表温度。我们使用十倍交叉验证评估了预测的准确性，并将copula插值法与外部漂移克里金法（KED）进行了比较。研究表明，在大多数月份以及夏季和秋季，copulas提供的预测比KED更为准确，而2017年1月，KED则提供了较高的准确预测。这项研究的结果表明，copulas能够检测最低温度的变化。在地形和观测值变化很大的区域中准确定位。进一步的工作将集中在基于copula的时空插值技术以实现最低温度映射。