LAUSR

Significant levels of aerosols originate from anthropogenic activities, markedly influencing regional air quality and, consequently, human health. Generally, fine-mode aerosol optical depth (fAOD) data are used to evaluate the concentration of anthropogenic aerosols. Although the moderate resolution imaging spectroradiometer (MODIS) provides fine-mode fraction (FMF) data that can be used to produce fAOD products, these data remain highly uncertain over land, in terms of global validation, relative to Aerosol Robotic Network (AERONET) measurements. To overcome this limitation, we developed an improved global land-scale fAOD product combining the lookup table-spectral deconvolution algorithm (LUT-SDA), generalized additive model (GAM), and MODIS Collection 6.1 aerosol products. Validation of the improved product revealed that over 63% of the fAOD values are within an expected error (EE) envelope of ±(0.05 + 15%), with strong positive correlations ( $R^{2}= 0.65$ ) and low bias (root-mean-square error = 0.185; mean absolute error = 0.104) compared to AERONET-observed fAOD values. Furthermore, the fAOD developed eliminates the multiple zeroes in the MODIS FMF-based fAODs. In the improved fAOD product, eastern China and northern India exhibit the highest 9-year-mean fAOD loading, with values generally exceeding 0.6. The improved global land fAOD product provides a new avenue with which to obtain data on anthropogenic aerosols and can also be useful in aerosol–climate interaction research.