Non-methane volatile organic compounds (NMVOCs) are key precursors of ozone and secondary organic aerosols. As one of the world's largest NMVOC emitters, accurate emission inventories are essential for understanding and mitigating air pollution in China. Commonly-used inventories (e.g., MEIC) are largely based on bottom-up methods, which often fail to capture the spatiotemporal variability of NMVOC emissions, resulting in significant model-observation mismatches. This study evaluates the shape factor, filtered data volume, and monthly mean biases of OMI, OMPS, and TROPOMI formaldehyde products, with the latest OMPS and TROPOMI retrievals offering substantially higher effective spatiotemporal coverage. Monthly NMVOC emissions over China in 2020 are then optimized by independently assimilating formaldehyde retrievals either from OMPS or from TROPOMI, using a self-developed 4DEnVar assimilation emission inversion system. The OMPS- and TROPOMI-driven assimilation yields consistent seasonal and regional increments in NMVOC emissions in general, but distinctions are also notable. A consistency analysis is introduced to assess the reliability of these two posterior emissions. Highly consistent increments are obtained in the North China Plain (May-June), the Yangtze River Delta and Pearl River Delta (January-March, October-December), and the Sichuan Basin (January, June-December). These adjustments significantly improve surface ozone simulations, with 81.25 % of consistent cases demonstrating reduced biases and an average RMSE reduction of 24.7 %. These findings highlight the effectiveness of OMPS and TROPOMI formaldehyde assimilation, coupled with consistency analysis, in refining NMVOC emission estimates and enhancing ozone simulation accuracy. Similar promising results are achieved in the OMPS/TROPOMI-based NMVOC emission inversion in 2019.