基于星地协同的水文传感网时空覆盖优化布局方法研究

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.

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。

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，是提高水文综合监测能力和有限观测资源利用效率的重要科学问题。本项目针对星地传感器水文观测的协同规划需求：（一）提出了点观测、线观测和面观测三种类型的水文观测覆盖能力表征模式，建立了星地多传感器观测能力信息关联模型，定义了互补、增强、竞争、协作等四类传感器关联模式，实现了面向多维度水文观测需求的星地传感器覆盖能力的时空关联表征与计算；（二）构建了面向单要素观测任务的星地协同观测时空互补覆盖模型TMCLP-PC、面向多要素复杂观测任务的星地协同观测时空增强覆盖模型SGMC-MP和顾及多目标的水文站网优化选址模型，通过引入部分覆盖判定、卫星动态侧摆等复杂约束条件，以及综合考虑空间覆盖、测站可达性和分布均匀性的复合加权模式，实现了基于覆盖能力时空互补与增强的星地传感器协同规划；（三）提出了连续时空需求条件下的星地协同规划布局问题求解方法，基于时空离散化思想设计了星地传感器规划的有限控制解集，将连续的无限搜索空间转换为离散的有限搜索空间，并设计了一种基于时空融合交叉的改进型遗传算法ST-swap GA，实现了对星地覆盖问题的高效求解；（四）开发了面向流域水文观测的星地传感器优化布局原型系统GeoSensorNodeOptimization，实现了水文传感器资源注册与查询、星地传感器覆盖能力计算、星地协同覆盖建模与求解、布局结果可视化等功能；（五）在长江中上游流域开展了一系列星地协同水文观测布局实验，结果表明本项目所提出的方法系统能够有效提升水文传感网的时空连续覆盖能力，有利于实现星地观测资源的协同规划与最优配置，能够为天地一体化水文传感网设计提供决策支持，同时也有利于实现时空优化技术在水文综合观测领域的多学科交叉应用。