This publication describes a research experiment and the process of developing a spatial digital twin of rivers and inland navigation areas using multiple measurement technologies. The main objective of the research is to develop a method for integrating data from multiple sensors and different platforms while maintaining the measurement accuracy characteristic of bathymetric data (0.1 m). The authors hypothesise that a digital twin accuracy of 0.1 m can be achieved through temporal integration of data from different sensors and by increasing the accuracy of determining the offsets of the measuring devices mounted on the boat and by applying modified Q-ST (Quasi-Similarity Transformation) to the integration of data from different measuring platforms. Finding answers to the main research problem, formulated below in the form of questions, will make it possible to realise the aim of research. * How can we use a time server to integrate measurement data? * Will using modified Q-ST transformation to integrate data from different measurement platforms improve the accuracy of data fusion? * How do we measure the offsets of survey equipment on a hydrographic boat to ensure high accuracy of their determination (within a few millimetres)? An accurate spatial model of water bodies and the surrounding infrastructure was gained by integrating in time and spatial manner the geospatial data from multiple sensors:  
* ALS--Airborne Laser Scanning. * ALB--Airborne Laser Bathymetry. * MSLS--Mobile Surface Laser Scanning onboard. * MLS--Mobile Laser Scanning on Land. * MBES--multibeam echosounder. * SBP--Sub-Bottom Profiler. * S--Side Scan Sonar. * Measurements of DGNSS--Differential Global Navigation Satellite System. * Airborne aerial photogrammetry and UAV. The main criterion for integration was to reduce the duration of measurement sessions and their number. To time-match the data, an acquisition system was developed, the central element of which is a time server. After fitting in the time domain of the data, an integrated method of measuring device offsets and a modified Q-ST (Quasi-Similarity Transformation) algorithm [8] of adjustment points were used for their spatial integration. The concept of a digital twin has been defined by the Digital Twin Consortium as a \"virtual\" representation of real-world entities and processes synchronised at a specified frequency and fidelity [9]. Digital twin technology, as discussed, represents a transformative approach by creating real-time digital replicas of physical systems, allowing for dynamic monitoring, prediction, and optimisation of water systems. These digital frameworks, coupled with data from the Internet of Things (IoT) and advanced analytics, have demonstrated their potential to revolutionise water conservancy through more precise forecasting and better resource allocation [10; 11; 12]. However, the successful deployment of digital twins in water management still faces technical and infrastructure-related challenges, particularly in integrating legacy systems and ensuring data accuracy and consistency [13; 14; 15; 16]. In parallel, the application of remote sensing technology in water management has expanded significantly. Refs. [1; 6] provide a comprehensive review of how remote sensing techniques are being utilised to monitor river corridors, assess water quality, and track hydrological changes over time. The ability to collect spatial and temporal data at various scales offers water managers unparalleled insights into watershed behaviour, which, when combined with other digital tools, enhances the predictive capabilities of water management systems [13; 16]. Digital twins enable testing in a virtual environment, providing valuable information on performance, safety, and environmental impact before resources are expended and potentially wasted [17; 18; 19; 20; 21; 22; 23]. Digital twins are classified into five levels of development (Figure 1).