As pioneering non-contact water level measurement technologies, both computer vision and radar have effectively addressed challenges posed by traditional water level sensors in terms of maintenance cost, real-time responsiveness, and operational complexity. Moreover, they ensure high-precision measurements in appropriate conditions. These techniques can be seamlessly integrated into unmanned aerial vehicle (UAV) systems, significantly enhancing the spatiotemporal granularity of water level data. However, computer-vision-based water level measurement methods face the core problems of accurately identifying water level lines and elevation calculations, which can lead to measurement errors due to lighting variations and camera position offsets. Although deep learning has received much attention in improving the generation, the effectiveness of the models is limited by the diversity of the datasets. For the radar water level sensor, the hardware structure and signal processing algorithms have to be further improved. In the future, by constructing more comprehensive datasets, developing fast calibration algorithms, and implementing multi-sensor data fusion, it is expected that the robustness, accuracy, and computational efficiency of water level monitoring will be significantly improved, laying a solid foundation for further innovations and developments of hydrological monitoring.

As an emerging non-contact method of flow monitoring, radar technology compensates for the shortcomings of traditional methods in terms of the efficiency, timeliness, and difficulty in monitoring high floods, and can provide accurate measurement results, making it one of the most promising flow monitoring methods in the future. This paper describes in detail the workflow from radar data acquisition to flow calculation; compares and analyzes the current state-of-the-art signal sampling and its limitations, Doppler spectrum estimation, signal processing and flow inversion; points out the challenges that these technologies may face in the future; and provides corresponding solutions in order to improve the real-time and accuracy of hydrometric as well as promote the development of non-contact flow monitoring technology.

The hydrological monitoring of flow data is important for flood prevention and modern river management. However, traditional contact methods are increasingly struggling to meet the requirements of simplicity, accuracy, and continuity. The video-based river discharge measurement is a technique to monitor flow velocity without contacting the water body by using the image-recognition algorithms, which has been verified to have the advantages of full coverage and full automation compared with the traditional contact technique. In order to provide a timely summary of the available results and to inform further research and applications, this paper reviews and synthesizes the literature on the general implementation routes of the video-based river discharge measurement technique and the principles and advances of today’s popular image-recognition algorithms for velocity detection. Then, it discusses the challenges of image-recognition algorithms in terms of image acquisition conditions, parameter uncertainties, and complex meteorological and water environments. It is concluded that the performance of this technique can be improved by enhancing the robustness and accuracy of video-based discharge measurement algorithms, minimizing weather effects, and improving computational efficiency. Finally, future development directions for further perfecting this technique are outlined.