Efficient night-time vessel detection is of significant importance for maritime traffic management, fishery activity monitoring, and environmental protection. With the advancement in object-detection approaches, the method of night-time vessel detection has gradually shifted from traditional threshold segmentation to deep learning that balances efficiency and accuracy. However, the restricted spatial resolution of night-time light (NTL) remote sensing data (e.g., VIIRS/DNB images) results in fewer discernible features and insufficient training performance when detecting vessels that are considered small targets. To address this, we establish an Enhanced Dense Nested-Attention Network (DNA-net) to improve the detection of small vessel targets under low-light conditions. This approach effectively integrates the original VIIRS/DNB, spike median index (SMI), and spike height index (SHI) images to maintain deep-level features and enhance feature extraction. On this basis, we performed vessel detection based on the Enhanced DNA-net using VIIRS/DNB images of the Japan Sea, the South China Sea, and the Java Sea. It is noteworthy that the VIIRS Boat Detection (VBD) observations and the Automatic Identification System (AIS) data were cross-matched as the actual status of the vessels (VBD-AIS). The results show that the proposed Enhanced DNA-net achieves significant improvements in the evaluation metrics (e.g., IOU, P_d, F_a, and MPD) compared to the original DNA-net, achieving performance of 87.81%, 96.72%, 5.42%, and 0.36 Wpx, respectively. Meanwhile, we validated the detection performance of Enhanced DNA-net and strong VBD detection against VBD-AIS, showing that the Enhanced DNA-net achieves 1% better accuracy than strong VBD detection.

Aerosol Optical Depth (AOD) is a crucial physical parameter used to measure the radiative and scattering properties of the atmosphere. Obtaining full-coverage AOD measurements is essential for a thorough understanding of its impact on climate and air quality. However, satellite-based AOD products can be affected by abnormal weather conditions and high reflectance surfaces, leading to gaps in spatial coverage. To address this issue, we propose a satellite-based AOD filling method based on hourly level-3 Himawari-8 AOD products. In this study, the optimal model with a mean bias error (MBE) less than 0.01 and a root-mean-square error (RMSE) less than 0.1 in most land cover types was selected to generate the full-coverage AOD. The generated full-coverage AOD was validated against in situ measurements from the AERONET sites and compared with the performance of Himawari-8 AOD and MERRA-2 AOD over the AERONET sites. The validation results indicate that the accuracy of full-coverage AOD is comparable to that of the Advanced Himawari Imager (AHI) AOD, and for other land cover types (excluding barren land), the accuracy of full-coverage AOD is superior to that of MERRA-2 AOD. To investigate the practical application of full-coverage AOD, we utilized it as an input parameter to perform radiative transfer simulations in northwestern and southern China. The validation results showed that the simulated at-sensor radiance based on full-coverage AOD was in good agreement with the at-sensor radiance observations from MODIS. These results indicate that complete and accurate measurements of AOD have considerable potential for application in the simulation of at-sensor radiance and other related topics.