资源环境科技发展态势分析平台

MODerate resolution Imaging Spectroradiometer (MODIS) data can play an important role in aerosol retrieval at the global scale due to their short revisit period and long-term observations. The operational MODIS aerosol optical depth (AOD) products are severely limited in air quality studies at the city or local scales due to their coarse spatial resolutions. Therefore, an improved aerosol retrieval algorithm for MODIS images at 1-km spatial resolution is proposed in this paper. This algorithm is based on the high-resolution aerosol retrieval algorithm with a priori land surface reflectance database support (HARLS), which was developed over bright urban areas and was subsequently modified and validated over land. For this study, an eight-day surface reflectance database and a seasonal aerosol-type database over land are constructed using the MODIS surface reflectance and aerosol products. Four typical regions (in Europe, North America, Beijing-Tianjin-Hebei, and the Sahara) with different underlying surfaces and aerosol types are selected to perform the aerosol retrieval experiments. The AOD retrievals are validated against the AERosol RObotic NETwork (AERONET) version 2 level 2.0 AOD measurements and compared with the operational MODIS AOD product at 3-km resolution (MOD04_3K). The results show that AOD retrievals adequately match with AERONET AOD measurements, with 79.56%, 72.69%, 74.71%, and 61.01% of the collections falling within the MOD04_3K expected error over land [+/-(0.05 + 20%)] for each of the above-listed regions, respectively. The Improved HARLS (I-HARLS) algorithm performs well overall under different surface conditions, but the data quality gradually decreases with the increase in surface reflectance. Moreover, the I-HARLS algorithm is robust with less bias and can provide more detailed aerosol spatial distributions than those provided by the MOD04_3K AOD product. These results suggest that the I-HARLS algorithm can be used for air-pollution-and climate-related studies at medium or small scales.