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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.

Near-surface air temperature lapse rate (NSATLR) is one of the key criteria for judging atmospheric stability . It is an important model parameter in glacier, hydrology, ecology, climate and meteorology in high mountains such as the Tibetan Plateau (TP). However, due to the complex interaction between the atmosphere and land surface, retrieving NSATLR from remotely sensed data is challenging. In this study, we develop a novel model based on the MODIS data to estimate the daily 5-km NSATLR over the TP. The input parameters of the model include DEM and the near-surface air temperature (NSAT) estimated from the MODIS data. By combining moving window and specified criteria, the pixel-based NASTLR with a 5-km spatial resolution over the TP is estimated. In addition, we also estimate the NSATLR based on the meteorological data over the TP (NSATLRin-situ) to evaluate the accuracy of NSATLR estimation model. Results demonstrate that the NSATLR estimate agrees well with NSATLRin-situ in the eastern TP, with an overall root mean square error (RMSE) of 1.51 °C/km and a mean bias error (MBE) of -0.78 °C/km. Larger deviation exists in the southern TP. Thanks to the evident advantages of simplicity, the availability of input parameters and the independence from the meteorological data, the developed model can be applied to larger areas.

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