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In the autumn and winter, aerosol is the important atmospheric pollutant over the Beijing–Tianjin–Hebei region. For monitoring aerosol in the autumn and winter, the lack of vegetation and the aging of MODIS sensor are two problems that needed to be solved. In this paper, after analyzing the characteristics of aerosol radiance in the red and shortwave infrared ($2.12~\mu \text{m}$) bands of MODIS, we develop a new algorithm for terrestrial aerosol with the assumption that the reflectance ratio between the red and $2.12~\mu \text{m}$ bands is invariant. With MODIS data over the Beijing–Tianjin–Hebei region from September 2016 to February 2017, the algorithm is applied to aerosol retrieval. The retrieved aerosol optical depth images show that our algorithm can retrieve aerosol over sparse vegetation, and the validation with the AERONET/PHOTONS Beijing site shows that the correlation is greater than 0.9% and 77% of the retrievals fall within the expected error. An error analysis shows that a 2% error in the proportion of the soot component can lead to 15% retrieval error, and over more than 60% of the surface area, the error from the changes in the ratio between the red and $2.12~\mu \text{m}$ bands can lead to retrieved errors less than 0.1. [ABSTRACT FROM AUTHOR]

China's industrialized regions have seen increasing occurrence of heavy haze caused by severe particle pollution. However, aerosol retrieval under these circumstances is often excluded from NASA's Moderate Resolution Imaging Spectrometer (MODIS) aerosol products due to cloud mask and suspected high surface reflectance. An algorithm to retrieve the haze aerosol optical thickness (HAOT) is developed using MODIS data to supplement the current MODIS retrieval algorithm. This method includes 1) haze identification, 2) the generation of a surface reflectance database using MODIS data in hazy conditions, and 3) the development of haze aerosol models with four aerosol components simulated by a global 3-D atmospheric chemical transport model (GEOS-Chem). This algorithm was used in combination with the MODIS dense dark vegetation algorithm to retrieve 1 km HAOT over North China Plain from March to September of 2008. The values of the retrieved HAOT values are mostly between 0.7–3, with a correlation coefficient of 0.82 with the Aerosol Robotic NETwork observations and a 19% mean relative difference. Retrieval uncertainties associated with the errors in haze detection, surface reflectance, and haze models were analyzed using ground measurements. [ABSTRACT FROM PUBLISHER]