Application of photoelectric sensors in the design of hydrologic remote sensing measurement system Open Access

Hydrologic remote sensing is a crucial technology for water resource management, providing vital data for flood prediction, drought mitigation, and water quality assessment. With the increasing complexity of hydrological systems and the need for accurate, real-time data, photoelectric sensors have emerged as a transformative solution (Star et al. 1997). Their precision, reliability, and efficiency make them highly effective for monitoring water levels, turbidity, and flow velocities, addressing the limitations of conventional measurement methods.

Photoelectric sensors operate on the principle of the photoelectric effect, where light interactions with the environment are converted into electrical signals (Rango 1994). Configurations such as through-beam, retroreflective, and diffuse sensors allow these systems to measure parameters with high accuracy (Wu et al. 2021). They provide continuous, high-resolution data critical for applications like flood forecasting, early warning systems, pollution tracking, and understanding river dynamics. In hydrology, remote sensing may track a variety of variables, such as snowfall, soil moisture, surface water levels, and precipitation. Satellites, planes, or even sensors on the ground can collect this data remotely (Kedia 2015). Understanding and predicting the behaviour of water in natural systems is crucial for efficient water resource management, and this technology provides such understanding and forecast. The limitations of conventional hydrological measurement techniques in terms of data coverage and frequency have made remote sensing a priceless adjunct tool (Glasgow et al. 2004).

Compared to traditional methods, photoelectric sensors offer significant advantages for hydrologic remote sensing systems. These sensors are capable of detecting variations in light intensity associated with critical hydrological parameters such as water level, turbidity, and flow velocity (Trevathan et al. 2020). Their ability to deliver continuous, high-resolution data makes them particularly effective in environments where water conditions change rapidly. Photoelectric sensors operate based on the photoelectric effect, where interactions between light and its surroundings enable the detection of changes in light intensity. They consist of three primary components: a light source, a photodetector, and an evaluation module. The light source emits a beam that interacts with the target, either being reflected, absorbed, or transmitted. The photodetector then converts the resulting light intensity into an electrical signal, which is analyzed to extract key information such as intensity, presence, or distance (Muste et al. 2008).

There are three main types of photoelectric sensors: through-beam, retroreflective, and diffuse (Banda et al. 2022). Through-beam sensors, with the emitter and receiver placed opposite each other, detect interruptions when an object blocks the light beam. Retroreflective sensors house both the emitter and receiver in a single unit and use a reflector to redirect the light beam back to the receiver (Banda et al. 2022). Diffuse sensors detect light that reflects directly off the target. Each configuration offers unique advantages, allowing for flexible adaptation to diverse hydrological measurement needs.

Photoelectric sensors are increasingly utilized in hydrologic remote sensing systems, particularly in applications requiring precise and immediate data collection. A key use case is water level monitoring, where these sensors are deployed in water bodies like rivers, lakes, and reservoirs (Fascista 2022). They accurately measure water levels by determining the time taken for light to reflect from the water's surface. This real-time data are vital for flood prediction and early warning systems, which play a critical role in mitigating the damage caused by floods to communities and infrastructure (Mishra et al. 2023). Photoelectric sensors are also effective in detecting turbidity, a key parameter for assessing water quality. Turbidity refers to the cloudiness or haziness of water caused by suspended particles (Li et al. 2023). By measuring the scattering and absorption of light as it passes through the water, photoelectric sensors enable real-time monitoring of turbidity levels (Zhang et al. 2022). This capability is essential for tracking pollution in aquatic ecosystems and ensuring safe access to potable water by detecting contaminants that could affect water safety and treatment processes.

While photoelectric sensors offer numerous benefits for hydrologic remote sensing, they also face certain challenges (Guo et al. 2020). Environmental factors such as sunlight, fog, and rain can interfere with sensor readings, affecting their accuracy. Although advancements in sensor design and signal processing have alleviated some of these issues, further research is needed to enhance the sensors' ability to perform reliably under a broader range of environmental conditions (Chen et al. 2020). Additionally, integrating photoelectric sensors with other types of measurement tools presents another challenge. Given the complexity and diversity of hydrological systems, a single sensor type may not be sufficient to capture all necessary data. To address this, a more comprehensive understanding of hydrological processes can be achieved by developing integrated sensing systems that combine photoelectric sensors with other technologies like radar or sonar (Sapra et al. 2020).

This work examines the application of intelligent photoelectric detection systems in hydrologic remote sensing, highlighting their potential to transform environmental monitoring and water resource management. Unlike traditional systems that rely on manual calibration, these advanced systems automatically adjust parameters in real time, allowing for highly accurate measurements of water levels, turbidity, and flow velocities, even under fluctuating environmental conditions. With adaptive sensing capabilities and sophisticated data processing, they function in semi-intelligent or fully intelligent modes, providing continuous and precise data in complex scenarios. Their deployment effectively addresses key challenges, including real-time data collection, flood prediction, and water quality monitoring, positioning them as essential tools for sustainable water management and disaster mitigation in response to increasing climate-related challenges.

An essential component of space remote sensing technology, the photoelectric detection system has been painstakingly designed to carry out accurate task detections. The effectiveness of job completion is directly affected by the careful selection of system parameter configurations, the final function design, and the quality of performance (Yang et al. 2023). Target detection, data capture, and data transmission are all areas where the photoelectric detection system might be useful (Yang et al. 2023). Clearly, the mission-critical requirements dictate the evolving performance features of the supplementary sensors.

Adaptive parameter adjustment is an inherent strength of the intelligent photoelectric detecting system due to the many functions described in Table 1. It is possible to evaluate the system's ability to detect targets in real time thanks to this crucial feature, which generates a dynamic method to measure the effectiveness of detection quality. Analysing the results of spatial remote sensing data and providing feedback is crucial for guiding the adaptive re-calibration of system settings. This method of strategic adaptation is in sync with the main objective of reaching the necessary level of detection quality.

To achieve the best image results from photoelectric detection systems, it is essential to configure the settings to account for the fact that different activities require different types of detection and targets. Environmental conditions and the time of observation are two contextual factors that influence these metrics (Chen et al. 2020). To address this complexity, a well-designed photoelectric detection system should incorporate task-specific functional parameter links. With this adaptive parameter approach, the system can automatically adjust its settings, enabling it to efficiently detect and identify a wide variety of targets.

The intelligent photoelectric detecting system is implemented according to a well-crafted design process, which is briefly explained in Figure 4. Here is how the procedure goes down:

• Mode selection: In the first stage, known as ‘mode selection,’ the system's operation is aligned with the goals of the task at hand. The semi-intelligent mode is most useful when the work objectives are clear. In this mode, task instructions direct the system to fine-tune its parameters, allowing for accurate target detection. In contrast, when no tasks are present, the fully intelligent mode is activated. In this mode, the spectrometer's spectral curve is compared to the extensive background target spectral database in great detail. Predicting possible detection scenarios, thoroughly evaluating potential targets, and laying the groundwork for eventual job planning are all key aspects of this phase.

• Parameter database task requirements matching: Upon receiving task instructions, the photoelectric detecting system synchronizes the parameter database with the task requirements. The specified parameter database is searched by extracting keywords from the task specifications and analyzing their relevance with the stored parameter instances. If a matching task instance is found, the corresponding parameter data is immediately used. Adaptive parameter selection is necessary when the inputs do not match. In full mode, predicted background results and target analysis are used to select the most appropriate instance from the parameter database.

• System parameter initialization without matching cases: When no similar instances are found in the parameter database to match the task requirements, it is necessary to initialize system parameters. The first step is to examine the spectral curve to determine which operational wavelength provides the best signal-to-noise ratio for detecting targets. Other parameters are also configured simultaneously. With this optical setup, the goal is to maximize the aperture and minimize the focal length. Additionally, the integration time and gain are maximized.

• Adaptive parameter tuning: After the parameters have been configured, the system proceeds to adaptive parameter tuning. Target imaging begins once the parameters are adjusted. Following imaging, the results are evaluated to determine if they meet the criteria for optimal imaging. If the results meet the requirements, the process moves forward. If the imaging quality does not meet the criteria, iterative parameter adjustments are applied until the detection outcome reaches the desired level of optimal imaging.

The spectroscope prism divides the visible light spectrum (400–760 nm), shortwave infrared (1–3 m), midwave infrared (3–5 m), and longwave infrared (8–15 m) into their respective channels, allowing the imaging link to enable multiband scene imaging. To make additional wavelength selection easier, each channel has a filter wheel that spins.

This sonic wave causes reflection and refraction when a laser beam is focused onto the surface of microwave-modulated water. The reflected light carries the vibrational information of certain locations on the water's surface, which is then received by the optical reception system as a signal. Underwater acoustic signal information can be retrieved from the resulting electrical signal after amplification, filtering, and conversion. Using remote sensing technologies, this study shows how pressure effects can be produced by bringing the underwater sound source's acoustic field to the water's surface. So, as a laser travels through water, it experiences reflection and refraction, transforming the microwave waveform into a form that contains the information inherent to underwater audio waves. The reflected light carries the vibrational information of certain locations on the water's surface, which is then received by the optical reception system as a signal. Underwater acoustic signal information can be retrieved from the resulting electrical signal after amplification, filtering, and conversion. Using remote sensing technologies, this study shows how pressure effects can be produced by bringing the underwater sound source's acoustic field to the water's surface. So what happens is that the information contained in underwater acoustic impulses is transferred to the surface of the water by means of a microwave waveform.

Figures 10(a)–10(h) show, at different frequencies, different underwater acoustic sounds. Detection results do not always coincide with the signal strength. Seafloor acoustic communication remote sensing images at 40, 50, 70, and 70 Hz do not produce full resolution (Figures 10(c)–10(g)). The main control system may not be able to receive enough acoustic communication remote sensing signals, which could be due to difficulties caused by either low signal reflection frequencies or extremely high frequencies. Most acoustic communication remote sensing signals are reflected above the capture range of the primary control system when the frequencies are high. Figures 10(a) and 10(e) show that even while complete acoustic communication remote sensing images are possible at certain frequencies, the signal intensity is generally lowered in such cases. In contrast, the photoelectric detection system depicted in this study is able to acquire complete communication remote sensing images and display a larger light signal intensity inside these images at 30 Hz (Figure 10(b)) and 100 Hz (Figure 10(h)).

The integration of sophisticated photoelectric detection systems with hydrologic remote sensing measuring systems may achieve an enormous step forward in environmental monitoring and resource management. Intelligent photoelectric systems have autonomous and adaptable capabilities that allow for the optimisation of measuring conditions in real-time across varied environmental circumstances, as opposed to traditional systems that rely on set parameters and require human modifications. Even under difficult situations, these systems can dynamically change their settings to detect water levels, turbidity, and flow velocities more accurately. Their incorporation of state-of-the-art data processing methods and dual operation in semi- and fully intelligent modes further increases their efficacy, rendering them priceless for intricate and multifaceted hydrologic investigations. In order to effectively manage water resources and mitigate disasters, such intelligent systems will be crucial in delivering high-resolution, real-time data, which is becoming increasingly important as environmental issues multiply. Hydrologic research and remote sensing applications will surely benefit greatly from the ongoing development and improvement of these systems, which are being propelled by advances in sensor technology and data analytics.

The future scope of work includes enhancing the adaptive capabilities of photoelectric detecting systems to handle more complex environments and integrating them with other sensing technologies for more accurate, multi-dimensional data collection.

All relevant data are included in the paper or its Supplementary Information.

The authors declare there is no conflict.