With the help of Service-Oriented Architecture (SOA), the remote sensing image processing algorithms can be abstracted into a set of component services. Then, flexible business requirements can be met through service selection and composition. In order to get the service components that meet the user’s Quality of Service (QoS) requirements for combination， QoS of all services should be firstly obtained. Indeed, the QoS of a service is unknown to its users who have never invoked the service before. Hence, many research work have been proposed to predict the missing QoS. However, these existing methods seldom take the temporal factors into consideration, which may decrease the prediction accuracy. In order to resolve the issue, a new QoS model based on time slice was firstly proposed by considering temporal factors. Furthermore, a time-aware QoS prediction method based on Collaborative Filtering (CF) was proposed. The experiments results on the WS-DREAM real dataset show that， the proposed time-aware QoS prediction method can obtain smaller Mean Square Error (MAE) and Root Mean Square Error (RMSE).In addition, some parameters may affect the time-aware QoS prediction performance. Thus, a set of experiments and analysis with various parameter combinations were carried out, which provides a certain reference for parameter selection.