High-precision temperature control of large-area blackbodies has a pivotal role in temperature calibration and thermal imaging correction. Meanwhile, it is necessary to correct the temperature difference between the radiating (surface of use) and back surfaces (where the temperature sensor is installed) of the blackbody during the testing phase. Moreover, large-area blackbodies are usually composed of multiple temperature control channels, and manual correction in this scenario is error-prone and inefficient. At present, there is no method that can achieve temperature-automated calibration for a large-area blackbody radiation source. Therefore, this article is dedicated to achieving temperature-automated calibration for a large-area blackbody radiation source. First, utilizing two calibrated infrared thermometers, the optimal temperature measurement location was determined using a focusing algorithm. Then, a three-axis movement system was used to obtain the true temperature at the same measurement location on a large-area blackbody surface from different channels. This temperature was subtracted from the blackbody’s back surface. The temperature difference was calculated employing a weighted algorithm to derive the parameters for calibration. Finally, regarding experimental verification, the consistency error of the temperature measurement point was reduced by 85.4%, the temperature uniformity of the surface source was improved by 40.4%, and the average temperature measurement deviation decreased by 43.8%. In addition, this system demonstrated the characteristics of strong environmental adaptability that was able to perform temperature calibration under the working conditions of a blackbody surface temperature from 100 K to 573 K, which decreased the calibration time by 9.82 times.

Underwater laser signal attenuation challenges conventional detection, while single-photon LiDAR (SPL) with high sensitivity shows promise. Existing underwater SPL studies primarily focus on isolated parameters, while the coupled effects of environmental and system parameters remain insufficiently investigated. In this work, a 532 nm underwater SPL system was developed to systematically explore multi-parameter coupling mechanisms in laboratory water tanks, including air and three turbidity levels, three detection distances, four laser energy levels, three integration times, and seven targets. This provides quantitative guidance for optimizing SPL systems in complex underwater environments. The results show that the SPL system maintained sub-nanosecond ranging precision, with the standard deviation (SD) of the ranging measurement at 50 cm being 0.0117 ns under low turbidity (0.11 m⁻¹) with 50% laser energy, while under high turbidity (4.2 m⁻¹) conditions, it increased to 0.0338 ns. At 100 cm, the SD was 0.0187 ns in low turbidity and rose to 0.0877 ns in high turbidity. Furthermore, the inversion error of the highly reflectivity minerals was kept within 3%, and the inversion value of reflectivity decreased exponentially with the increase of turbidity. Moreover, there is an important discovery for the phenomenon of the forward shift of photon flight time detected for highly reflectivity targets. Longer integration times effectively enhanced the signal-to-noise ratio (SNR) under severe attenuation, whereas excessive laser energy risked detector saturation. These findings provide a systematic characterization of how multifactor coupling governs SPL signal dynamics. The results validate the feasibility of SPL for complex underwater detection and offer theoretical insights and technical guidance for future marine applications in resource exploration, environmental monitoring, and national security.

The spectral selectivity of underwater multiwavelength single-photon LiDAR offers a promising pathway to discriminate target materials beyond conventional geometric imaging. However, the complex interactions among wavelength-dependent water attenuation, target reflectance, and scattering-induced waveform distortion remain poorly quantified. This study establishes a comprehensive theoretical and experimental framework linking these factors, validated through controlled experiments across two water turbidity levels (attenuation coefficients of 0.1 m⁻¹ and 2.0 m⁻¹), six wavelengths (490–570 nm), and diverse target types. We demonstrate that target ranging bias exhibits a wavelength-dependent linear trend (8.3 ps/nm) in turbid waters. This phenomenon is fundamentally attributable to forward-scattering-induced centroid shifts rather than true spatial displacements, a mechanism we quantify through comparative peak-detection and Gaussian fitting analyses. Contrary to intuitive expectations, we reveal that spectral discrimination efficacy decouples from received photon counts. Principal component analysis confirms that a multidimensional spectral feature space enables accurate target clustering independent of absolute intensity, with specific bands (e.g., 510 nm and 550 nm) exhibiting heightened sensitivity to material signatures. These findings establish that underwater target recognition is primarily influenced by the spectral contrast between target reflectance and water transmission windows, rather than solely depending on received photon counts, providing a robust physical basis for next-generation underwater LiDAR optimization.

Current methods for the high-precision real-time monitoring and parameter inversion of industrial methane point source leakage are insufficient. This research introduces a novel laser-based methane leakage monitoring approach for deployment on an unmanned aerial vehicle platform. An enhanced two-dimensional integral Gaussian diffusion model paired with a point sampling technique is employed to simultaneously determine the leakage rate and source location, integrating a genetic algorithm and an interior point penalty function algorithm for optimization. Simulations incorporating observational error sources are performed to quantitatively assess the accuracy of leakage parameter inversion under diverse errors, demonstrating the scheme’s viability. The accuracy of leakage parameter inversion achieved by the algorithm across various point sampling methods, gas plume characteristics, and wind speeds was examined, validating the assessment under multivariable influences in real observations. The proposed methodology was compared with two other leakage inversion optimization techniques, demonstrating its efficiency in addressing wind speed and directional effects. This study offers a practical method with significant implications for monitoring and quantifying industrial methane point source leakages.

Temperature control of large-area blackbodies is crucial for the metrological performance of radiometric instruments, yet it remains challenging due to the complexities of traditional PID-based algorithms, which struggle with parameter tuning and fail to account for thermodynamic differences across channels in multichannel systems. To enhance the measurement accuracy and stability of such instruments, this study proposes a novel reinforcement-learning-based, parameter-free temperature control reinforcement-learning temperature controller (RLTC) method aimed at balancing heating rates across the blackbody surface and achieving precise temperature regulation. We first established a real temperature data acquisition system and developed a simulation model to generate temperature data, supplementing the real dataset. Thereby, we collected and labeled the first extensive temperature dataset for a large-area blackbody system, which includes heating, residual heat, and cooling characteristics. The RLTC algorithm was tested on a large-area blackbody ( $2.2\times 2.2$ m) with 64 temperature control channels. Compared to traditional PID algorithms, RLTC reduces stabilization time by 15.23%. Additionally, the integrated temperature balancing module reduces the maximum stabilization time difference between channels by 93.51%. The RLTC algorithm not only improves individual channel heating rates but also ensures more consistent heating across multiple channels, thereby providing faster and more balanced temperature adjustments in complex environments. This research introduces an advanced reinforcement learning (RL) control framework that directly enhances the metrological performance of radiometric systems, contributing to more precise and long-range detection capabilities for large-aperture optical detection applications.