Zhang, X; Wang, H; Che, HZ; Tan, SC; Shi, GY; Yao, XP; Zhao, HJ (2020). Improvement of snow/haze confusion data gaps in MODIS Dark Target aerosol retrievals in East China. ATMOSPHERIC RESEARCH, 245, 105063.

The MODerate resolution Imaging Spectroradiometer (MODIS) is one of the most widely used meteorological remote sensing instruments. Its Dark Target aerosol optical depth (AOD) product has been widely used in en-vironment and meteorology researches, such as model evaluation and data assimilation. However, this product has a low coverage under conditions of heavy haze in China. This is because the haze can be misidentified as snow under some circumstances by the algorithm and therefore rejected, leading to large-scale data omission. In the most polluted regions, misidentified snow cover exceeded 8%. Regarding this issue, a new method com-bining the snow mask derived from the MODIS cloud mask product and Fisher discrimination analysis was developed to give a more accurate identification of snow and ice cover. Applying this new method increases the AOD data coverage significantly. Comparisons with AOD values from ground-based observations showed that the newly produced data under haze conditions had a similar accuracy with the original data in the MODIS AOD product. Because the newly supplemented data are more distributed at the seriously polluted regions, the average AOD increased significantly after data filling in many regions. In winter, AOD in the most severely polluted regions (average air quality index > 130) increased by 0.2-0.3 after improvement, about 30-50% of the original value. In 49 haze cases with large-scale pollution, the increase reached 0.3-0.6, about 50%-70% of the original value. During the haze episodes, the data omission led to an underestimation of the regional average AOD by 19-40%. The improvement in AOD coverage helps to provide a better reflection of the air pollution condition in East China through the perspective of AOD.