We present a new top-down approach that spatially constrains the amount of aerosol emissions using satellite (Moderate Resolution Imaging Spectroradiometer (MODIS)) observed radiances with the adjoint of a chemistry transport model (GEOS-Chem). This paper aims to demonstrate the approach through applying it to a case study that yields the following emission estimates over China for April 2008: 1.73Tg for SO2, 0.72Tg for NH3, 1.38Tg for NOx, 0.10Tg for black carbon, and 0.18Tg for organic carbon from anthropogenic sources, which reflects, respectively, a reduction of 33.5%, 34.5%, 18.8%, 9.1%, and 15% in comparison to the prior bottom-up inventories of INTEX-B 2006. The mineral dust emission from the online dust entrainment and mobilization module is reduced by 56.4% of 19.02 to 8.30Tg. Compared to the prior simulation, the posterior simulation shows a much better agreement with the following independent measurements: aerosol optical depth (AOD) measured by AERONET sun-spectrophotometers and retrieved from Multi-angle Imaging SpectroRadiometer (MISR), atmospheric NO2 and SO2 columnar amount retrieved from Ozone Monitoring Instrument (OMI), and in situ data of sulfate-nitrate-ammonium and PM10 (particular matter with aerodynamic diameter less than 10 mu m) mass concentrations over both anthropogenic pollution and dust source regions. Assuming the bottom-up (prior) anthropogenic emissions are the best estimates for their base year of 2006, the overwhelming reduction in the posterior (top-down) estimate indicates less emission in April 2008 especially for the SO2 tracer in the central and eastern parts of China, and/or an overestimation in the prior emission. The former is supported by the AOD change detected by MODIS and MISR sensors, while the latter is likely the case for NOx and NH3 emissions because no evidence shows that their atmospheric concentration has declined over China. With the promising results shown in this study, continuous efforts are needed toward a holistic and comprehensive inversion of emission using multisensor remote sensing data (of trace gases and aerosols) for constraining aerosol primary and precursor emissions at various temporal and spatial scales.