For earthquake disaster assessment using remote sensing (RS), multisource image registration is an important step. However, severe earthquakes will increase the deformation between the remote sensing images acquired before and after the earthquakes on different platforms. Traditional image registration methods can hardly meet the requirements of accuracy and efficiency of image registration of post-earthquake RS images used for disaster assessment. Therefore, an improved image registration method was proposed for the registration of multisource high-resolution remote sensing images. The proposed method used the combination of the Shi_Tomasi corner detection algorithm and scale-invariant feature transform (SIFT) to detect tie points from image patches obtained by an image partition strategy considering geographic information constraints. Then, the random sample consensus (RANSAC) and greedy algorithms were employed to remove outliers and redundant matched tie points. Additionally, a pre-earthquake RS image database was constructed using pre-earthquake high-resolution RS images and used as the references for image registration. The performance of the proposed method was evaluated using three image pairs covering regions affected by severe earthquakes. It was shown that the proposed method provided higher accuracy, less running time, and more tie points with a more even distribution than the classic SIFT method and the SIFT method using the same image partitioning strategy.

The horizontal and vertical distributions of aerosol optical properties over China in 2013–2015 were investigated using RAMS (Regional Atmospheric Modeling System)-CMAQ (Models-3 Community Multiscale Air Quality) simulations and CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) observations. To better understand the performance of the RAMS-CMAQ model over China, comparisons with the ground-based Sun photometers AERONET (Aerosol Robotic Network), MODIS (Moderate Resolution Imaging Spectroradiometers) data and the on-board Lidar CALIOP were used for comprehensive evaluations, which could characterize the abilities of the model to simulate the spatial and vertical distributions of the AOD (Aerosol Optical Depth) as well as the optical properties for four seasons. Several high value areas (e.g., the Sichuan Basin, Taklamakan Desert, North China Plain, and Yangtze River Delta) were found over China during the study period, with the maximum mean AOD (CALIOP: ~0.7; RAMS-CMAQ: >1) in the Sichuan district. Compared with AODs of AERONET, both the CALIOP and RAMS-CMAQ AODs were underestimated, but the RAMS-CMAQ data show a better correlation with AERONET (AERONET vs. RAMS-CMAQ R: 0.69, AERONET vs. CALIOP R: 0.5). The correlation coefficients between RAMS-CMAQ and CALIOP are approximately 0.6 for all four seasons. The AEC (Aerosol Extinction Coefficient) vertical profiles over major cities and their cross sections exhibit two typical features: (1) most of the AEC peaks occurred in the lowest ~0.5 km, decreasing with increasing altitude; and (2) the RAMS-CMAQ AEC underestimated the region with high AODs in the northwest of China and overestimated the region with high AODs in the east–central plain and the central basin regions. The major difference in the AEC values of RAMS-CMAQ and CALIOP is mainly caused by the level of relative humidity and the hygroscopic growth effects of water-soluble aerosols, especially, in the Sichuan district. In general, both the column and vertical RAMS-CMAQ aerosol optical properties could be supplemented efficiently when satellite observations are not available or invalid over China in the applications of climate change and air pollution.