Recent studies have shown that the high-resolution satellite Landsat-8 has the capability to retrieve aerosol optical depth (AOD) over urban areas at a 30 m spatial resolution. However, its long revisiting time and narrow swath limit the coverage and frequency of the high resolution AOD observations. With the increasing number of Earth observation satellites launched in recent years, combining the observations of multiple satellites can provide higher temporal-spatial coverage. In this study, a fusing retrieval algorithm is developed to retrieve high-resolution (30 m) aerosols over urban areas from Landsat-8 and Sentinel-2 A/B satellite measurements. The new fusing algorithm was tested and evaluated over Beijing city and its surrounding area in China. The validation results show that the retrieved AODs show a high level of agreement with the local urban ground-based Aerosol Robotic Network (AERONET) AOD measurements, with an overall high coefficient of determination (R²) of 0.905 and small root mean square error (RMSE) of 0.119. Compared with the operational AOD products processed by the Landsat-8 Surface Reflectance Code (LaSRC-AOD), Sentinel Radiative Transfer Atmospheric Correction code (SEN2COR-AOD), and MODIS Collection 6 AOD (MOD04) products, the AOD retrieved from the new fusing algorithm based on the Landsat-8 and Sentinel-2 A/B observations exhibits an overall higher accuracy and better performance in spatial continuity over the complex urban area. Moreover, the temporal resolution of the high spatial resolution AOD observations was greatly improved (from 16/10/10 days to about two to four days over globe land in theory under cloud-free conditions) and the daily spatial coverage was increased by two to three times compared to the coverage gained using a single sensor.

Snow cover is recognized as one of the most variable land cover parameters and plays a critical role in the global energy balance, climate change, and hydrological processes. Polar-orbiting satellites serve as the primary data source for monitoring both polar and global snow cover, providing wide coverage and high spatial resolution products. However, the utility of these snow cover products is significantly limited by data gaps caused by unfavorable observation conditions, such as cloud cover. Various reconstruction approaches are required to fill these gaps, depending on the snow cover product type (binary snow cover (BSC), normalized difference snow index (NDSI), or fractional snow cover (FSC)), snow characteristics, and availability of auxiliary datasets. This paper categorizes current reconstruction approaches into eight types: temporal filters, spatial filters, multisensor fusion, and the hidden Markov random field (HMRF) model for BSC mapping, as well as temporal and spatial interpolation methods, spatiotemporal reconstruction algorithms, machine learning-based reconstruction techniques, and data assimilation methods for NDSI or FSC mapping. This paper provides a comprehensive review of the principles, advantages, and limitations of these approaches and offers recommendations for their appropriate application. The discussion highlights that future improvements in snow cover reconstruction can be achieved through three key approaches. First, enhancing snow cover recognition algorithms will increase the accuracy of the original snow cover products, providing more reliable prior information for reconstruction. Second, careful consideration of spatiotemporal environmental factors, such as terrain, temperature, precipitation, solar radiation, and forest cover, along with the development of corresponding multisource data processing and fusion techniques, is essential. Third, further exploration of the synergy between machine learning and data assimilation could leverage their strengths in multisource data processing scenarios, offering novel insights for conducting snow monitoring and forecasting in complex environments. This review contributes to snow cover mapping and related research by offering a comprehensive analysis and guidelines for generating gap-filled snow cover products across a variety of spatiotemporal scales.

Due to its capacity of long-time automatic observation, the Vaisala CL51 Ceilometer, which is a simple single wavelength lidar, has great potential to retrieve aerosol vertical profiles. However, the backscattering signals from ceilometers around 910 nm, which are seriously affected by background signals and water vapor absorption, strongly limits the performance of aerosol retrievals. To overcome this issue, a signal correction process would be crucial to reduce errors of backscattering signals of the CL51 ceilometer. Herein, we develop a signal correction method including background signal correction and efficient water vapor correction. Using the profile observed by the collocated Raman lidar as reference data, we demonstrate that the signal correction significantly improves the accuracy of aerosol measurements from the ceilometer, reducing the median relative error between the two instruments from 29.34% to 21.54%. Although the median error remains slightly above the generally acceptable level, the improvement is nonetheless evident and meaningful. We further indicate that the water vapor correction, based on the humidity profiles, has effectively scaled the underestimated ceilometer backscatter profiles, particularly under humid conditions. The water correction method is validated in Leipzig, and the results imply the effectiveness of water vapor correction in different locations. For individual profiles, at around 1.3 km, where the largest profile differences occur, the relative error between the original CL61 and PollyXT decreases from 28.0% to 13.2% after water vapor correction. Our findings underscore the importance of the ceilometer in capturing the vertical distribution of aerosols through refined signal processing, offering a practical approach for observing the atmosphere.