Precipitation data are useful for the management of water resources as well as flood and drought events. However, precipitation monitoring is sparse and often unreliable in regions with complicated geomorphology. Subsequently, the spatial variability of the precipitation distribution is frequently represented incorrectly. Satellite precipitation data provide an attractive supplement to ground observations. However, satellite data involve errors due to the complexity of the retrieval algorithms and/or the presence of obstacles that affect the infrared observation capability. This work presents a methodology that combines satellite and ground observations leading to improved spatiotemporal mapping and analysis of precipitation. The applied methodology is based on space–time regression kriging. The case study refers to the island of Crete, Greece, for the time period of 2010–2018. Precipitation data from 53 stations are used in combination with satellite images for the reference period. This work introduces an improved spatiotemporal approach for precipitation mapping.

降水数据可用于水资源管理以及洪水和干旱事件。然而，在地貌复杂的地区，降水监测稀少且往往不可靠。随后，降水分布的空间变异性经常被错误地表示。卫星降水数据为地面观测提供了有吸引力的补充。然而，由于检索算法的复杂性和/或影响红外观测能力的障碍物的存在，卫星数据存在误差。这项工作提出了一种将卫星和地面观测相结合的方法，从而改进了降水的时空制图和分析。所应用的方法基于时空回归克里金法。案例研究涉及希腊克里特岛，2010-2018 年期间。来自 53 个站点的降水数据与参考时期的卫星图像结合使用。这项工作介绍了一种改进的降水制图时空方法。