The space-air-ground-sea stereo and integrated geospatial sensor web have gradually formed along with the development of remote sensing data network, sensor web, internet of things (IoT), and artificial intelligence. The sensing resources of the geospatial sensor web are of multiple sources, heterogeneity, and dispersion. These characteristics result in the grand technical challenges of sharing and managing heterogeneous resources, real-time access of geospatial information with multiple protocols, spatiotemporally seamless and autonomous sensing of geospatial information, and accurate prediction of key parameters, especially when facing the personalized, instant, and smart application needs of multi-level users. It is hard for static geographical information service to meet the demands of geo-events for integrated monitoring, early warning and decision support, and focusing application. Therefore, there are urgent needs to develop fusion service technologies of the geospatial sensor web, as well as real-time dynamic geographical information service platforms. To solve these problems, this paper proposed online access, integrated management, space-ground fusion, spatiotemporal prediction, and focusing service models and methods. With the online access, dynamic management methods for sensing spectrum resources, and transparent access methods based on heterogeneous sensor protocols pool were proposed; a cyber-physical spatiotemporal information service environment was established, which realized the efficient access of spatiotemporal information with heterogeneous protocols. With the integrated management, sharable and interoperable information models including sensor observation process information description model, observation data description model, observation event description model, and dynamic observation capability index model were proposed, which tackled the coupling problem of sensor web and GIS, and realized large-scale integrated management and sharing of space-air-ground-sea platforms and sensors for the integrated monitoring of fairway, hydrology, soil, meteorology, and ocean. With space-ground fusion, a point-surface-collaboration and seamless reconstruction model, and evaluation-collaboration-reconstruction, cross-scale, seamless and continuous sensing methods were proposed, which improved the sensing quality by 14 times with respect to using satellite only and meanwhile keeping sensing frequency the same as the station networks, providing new ways for continuous monitoring of resources, environments, and disasters. For spatiotemporal predictions, ensemble models of multiple machine learning models, ensemble models of statistical models and dynamic models, and a spatiotemporal deep learning model were proposed, which realized high-resolution and high-precision predictions of meteorological parameters at regional scales. For the focusing service, a geo-control method based on instant sensing feedback, a time-continuous maximal covering location model, and an automatic aggregation sensing method were proposed, which improved the spatiotemporal coverage of sensing by 18%, and realized active and on-demand sensing of spatiotemporal information. Based on the sensor web observation information models and using the architecture of satellite-ground-collaboration spatiotemporal information sensing as a service, a geospatial sensor web spatiotemporal information sensing and service system named GeoSensor was developed, which has the functions of sensing, access, cognition, and control. The GeoSensor has been successfully applied to the sensing management and service of spatiotemporal information in the Yangtze River, the ocean and the smart city. In the future, the theory of smart sensing and cognition of people, water, and city will be further developed, the technology of crowd-sourced sensing, spatial intelligence, and cognition service of space-air-ground-sea-people will be developed, and large-scale applications in the Yangtze River Economic Zone will be conducted as well.