Institute of Remote Sensing and Digital Earth

Haze, as a critical factor affecting regional air quality and human health, necessitates accurate remote sensing identification for pollution monitoring and climate research. This study proposes a two-stage haze mapping algorithm (THMA), based on a backpropagation neural network and a random forest model, which achieves high-precision identification of haze, clouds, and clear air using FY-4A AGRI geostationary satellite data, with small misclassification rates and high F1 scores. Through detailed comparison with CALIOP observations, THMA performs well over most regions over Asia, successfully extending the traditional binary classification task of distinguishing only clouds and clear air. Notably, the model provides good classification capability in vertically overlapping areas of broken clouds and haze, with minimal misclassification even over bright surfaces such as deserts and ice/snow. Statistical analysis for the year 2022 shows that the annual average number of haze days is 51.3 in China. This study confirms the significant complementary value of satellite remote sensing and ground-based observations for haze monitoring.

With the intensification of environmental pollution and the increasingly prominent problem of industrial harmful gas emissions, existing mainstream gas detection technologies still have obvious limitations in terms of real-time performance, non-contact capability, detection accuracy, and multi-component identification. To address this demand, this paper proposes a lightweight and compact gas detection system based on passive Fourier Transform Infrared Spectroscopy (FTIR). The system innovatively integrates an improved parallel pendulum mirror interferometer and a low-noise signal preprocessing module, and simultaneously presents a novel oversampling method fusing equal time, equal optical path difference, and digital filtering, which effectively enhances the operational stability and sampling accuracy of the spectrometer. The system features excellent platform adaptability and can be flexibly mounted on various operation carriers. Combined with a two-dimensional rotating platform and an inertial navigation module, its monitoring range and application scenarios can be further expanded. Indoor sensitivity test results show that the detection limit of the system for sulfur hexafluoride (SF6) is less than 20 ppm; flight tests under real-world scenarios have successfully achieved accurate detection of SF6 gas, fully verifying the practical application effectiveness of the system. Based on the comprehensive results of indoor and outdoor tests, the system demonstrates core technical advantages of high sensitivity, strong flexibility, and excellent real-time performance. It is expected to be widely applied in gas monitoring tasks across multiple fields such as industrial safety monitoring, ecological environment monitoring, and transportation support in the future.

Near space, as a critical atmospheric domain with unique physical, electromagnetic, and biological characteristics, remains a frontier with extensive unresolved scientific questions in atmospheric physics, electromagnetic environment dynamics, and biological adaptability mechanisms. In response to these knowledge gaps, the Chinese Academy of Sciences (CAS) initiated a strategic pilot science and technology project dedicated to systematically investigating the aforementioned three core domains of near space. This project has allowed for a series of near-space scientific experiments to be successfully conducted on the Qinghai–Tibet Plateau, utilizing large zero-pressure high-altitude balloons to carry diverse scientific payloads. From an engineering perspective, all experiments achieved complete success: high-altitude balloons with volumes ranging from 2000 m3 to 50,000 m3 safely transported scientific exploration equipment and payload cabins (with payload masses of 100 kg to 400 kg) to the near space; all scientific instruments maintained stable operational status throughout the missions; payload cabins reliably provided essential support functions, including power supply, data storage, real-time data transmission, and video monitoring for the scientific payloads; and both the scientific equipment and payload cabins were successfully recovered. These efforts are expected to enhance the scientific understanding of Earth’s near space environment and provide a technical foundation for subsequent large-scale near space exploration initiatives.

Medical endoscopic video processing requires real-time execution of color component acquisition, color filter array (CFA) demosaicing, and high dynamic range (HDR) compression under low-light conditions, while adhering to strict thermal constraints within the surgical handpiece. Traditional hardware-aware neural architecture search (NAS) relies on fixed hardware design spaces, making it difficult to balance accuracy, power consumption, and real-time performance. A collaborative “power-accuracy” optimization method is proposed for hardware-aware NAS. Firstly, we proposed a novel hardware modeling framework by abstracting FPGA heterogeneous resources into unified cell units and establishing a power–temperature closed-loop model to ensure that the handpiece surface temperature does not exceed clinical thresholds. In this framework, we constrained the interstage latency balance in pipelines to avoid routing congestion and frequency degradation caused by deep pipelines. Then, we optimized the NAS strategy by using pipeline blocks and combined with a hardware efficiency reward function. Finally, color component acquisition, CFA demosaicing, dynamic range compression, dynamic precision quantization, and streaming architecture are integrated into our framework. Experiments demonstrate that the proposed method achieves 2.8 W power consumption at 47 °C on a Xilinx ZCU102 platform, with a 54% improvement in throughput (vs. hardware-aware NAS), providing an engineer-ready lightweight network for medical edge devices such as endoscopes.