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Because of its high spatial and temporal variability, rainfall remains one of the most challenging meteorological variables to measure accurately. Obtaining high-quality rainfall products is essential for flood monitoring, disaster warning, and weather forecasting systems, but this is not always possible on the basis of current rainfall observation networks. Innovative alternatives draw inspiration from "citizen science" and "crowdsourcing," allowing for opportunistic sensing of rainfall from existing measurements at a low cost, which has become a popular topic and is beginning to play an important role in developing rainfall observation systems. This paper reviews the current state of new rainfall observation approaches and explores their opportunities to complement more traditional ways of rainfall data collection in a hydrological context. Furthermore, the challenges of each new approach are discussed. Although these new options show great potential in enhancing the current rainfall network, they still face problems in terms of their accuracy, real-time accessibility, and limited applicability when individually employed. In contrast, the fusion of new measurements with traditional observation networks is feasible and will be effective for regional rainfall monitoring. This study also serves as an important reference in developing monitoring techniques for other environmental factors. Significance Statement: New rainfall observation techniques provide a meaningful supplement to current rainfall networks in terms of spatiotemporal resolution and accuracy. In this paper, we present a comprehensive overview of the innovations in rainfall observation and their popularity in different regions around the world. Then, the application value and future opportunities that new techniques bring to hydrological research are analyzed. It is anticipated that this paper will be of value to researchers with an interest in improving the quality of rainfall data, thus paving the way to accelerate these studies, as well as the application and implementation of their findings, to the next stage. Furthermore, we expect to prompt a rethink on utilizing and exploiting these new rainfall products to enhance our understanding and optimization of current rainfall sensing systems. [ABSTRACT FROM AUTHOR]

• Achieving all-weather rainfall observations using a surveillance camera. • Building a new rain streak extraction algorithm for NIR surveillance video. • A novel rainfall intensity estimation mechanism for surveillance video. Widespread surveillance cameras that continuously record rainfall information offer an opportunity for high-spatiotemporal resolution rainfall inversion. Surveillance video-based rainfall data estimation has become one of the most promising methods. However, existing relevant studies have focused on rainfall observations during the day. Little attention has been given to developing a surveillance camera-based rain gauge that works at night. Generally, ordinary surveillance cameras actively emit near-infrared light (NIR) to supplement the insufficient illumination of a surveillance scenario, providing an essential prerequisite for rainfall observations at night. In this paper, an NIR-surveillance video-based rain gauge (NIR-VRG) was constructed. First, combining the meteorological and microphysical characteristics of raindrops with the camera imaging principle, the abilities of different NIR-surveillance cameras to capture raindrops during different rainfall scenarios were discussed, providing the theoretical basis for subsequent work; second, a tensor-based algorithm was proposed for rain streak extraction from NIR surveillance video; and finally, a one-dimensional convolutional neural network (1D CNN)-based regression algorithm was proposed and used to build a mapping relationship between the extracted rain streaks and the rainfall intensity (RI). Experimental results on synthetic rainy videos showed that the proposed rain streak extraction algorithm achieves robust performance in a light breeze (speed approximately 3 m/s) and still works in a gentle breeze (speed approximately 5 m/s). Moreover, experiments during various rainfall scenarios show that the designed NIR-VRG measures rainfall information with high accuracy. The relative error of the RI and cumulative rainfall ranged from 8.86 % to 84.84 % and 7.82 % to 30.70 % respectively. The NIR-VRG fills the gap of rainfall observations at night by using surveillance cameras and provides a reference for constructing an all-weather rainfall observation network. [ABSTRACT FROM AUTHOR]