地面观测和卫星遥感作为水文监测的两种主要手段，其覆盖范围分别呈空间不连续和时间不连续的特点。如何协同星地传感器进行联合观测，充分发挥卫星和地面站的时空覆盖优势，是提高水文监测能力和观测资源利用效率的重要科学问题。本项目以服务于水文监测的星地传感器覆盖能力为切入点，建立面向多维度水文观测需求的星地传感器覆盖能力表征模式；基于星地传感器覆盖能力的时空互补和增强，构建多目标星地传感器时空最大覆盖模型；通过将水文站选址和卫星配置的无限搜索空间转换为有限可行解集，提出连续时空条件下的星地联合覆盖问题高效求解方法；综合上述模型与方法，设计并开发面向长江上游金沙江流域水文观测的星地传感器优化布局原型系统，提出针对日常和应急监测的星地协同水文观测最优布局策略。本项目的研究成果有利于提高对水文传感网的时空连续覆盖能力并实现有限观测资源的最优配置，能够为天地一体化水文传感网的规划与设计提供决策支持。

The two major hydrological monitoring techniques, ground-based measurements and remote sensing, have distinct coverage characteristics. The coverage of ground station is continuous in time but discontinuous in space, whereas satellite coverage is continuous in space but discontinuous in time. Therefore, how to cooperate satellite sensors with ground-based sensors through mutual complementation of their coverage to improve the sensing capability and utilization rate of limited resources is an important scientific issue and presents a great challenge. This project takes coverage capability as the breakthrough point and designs a multidimensional representation mode of the coverage capabilities of space-borne and ground-based sensors oriented to hydrological monitoring. Then, we formulate a multi-objective spatio-temporal maximal covering location model based on the spatio-temporal complementation and enhancement of the coverage capabilities of space-ground sensors. By transforming the infinite search space of satellite planning and hydrological station siting to a finite dominant feasible solution set, a method for solving the spatio-temporal continuous covering problem can be proposed. To test the availability and performance of the proposed optimization models and methods, we will develop an optimal deployment prototype system which is applied in the Jinsha River Basin. The space-ground collaborative deployments oriented to rainfall monitoring and streamflow monitoring will be selected as two case studies. Several optimal deployment strategies of space-borne sensors and ground-based stations will also be analyzed in the daily monitoring and emergency monitoring scenarios. This research is expected to provide an effective method for the space-ground collaborative deployment optimization, which will be useful to improve the spatio-temporal continuous coverage capability for hydrological monitoring and to achieve the optimized configuration of limited observation resources. The research findings can provide decision support for the space-ground integrated hydrological sensor web planning and design.