Long-term reconstruction of daily global OMI NO2 product between 2005–2023 with spatiotemporally constrained compressive sensing

Accurate and continuous monitoring of long-term global atmospheric nitrogen dioxide (NO₂) content is of great significance, which can help air pollution control and climate impact assessment. However, satellite observations of NO₂ generally contain large areas of missing information, and few methods meet the near real-time demand for their fast and convenient recovery on a global scale. To address this issue, we propose a novel spatiotemporally constrained compressive sensing (STCS) framework to recover the missing pixels in the daily global ozone monitoring instrument (OMI) NO₂ total vertical column density (TotVCD) data from 2005-2023, which can exploit the spatiotemporal autocorrelation properties of data. Validation results reveal that the STCS model achieves favorable reconstruction that remains broadly consistent with original OMI observations. Specifically, the OMI recovered dataset exhibits the root-mean-square error (RMSE) = 0.25 Dobson Unit (DU), and mean bias (MB) = -0.085 DU against Pandora measurements, compared with RMSE of 0.242 DU and MB of -0.096 DU for the original OMI dataset. The recovered results also show a high consistency with the NO₂ TotVCD data from TROPOspheric Monitoring Instrument, with the correlation coefficient of 0.752. Furthermore, the spatial distributions of the recovered NO₂ TotVCD on multi-temporal scales demonstrate detailed information, clearly revealing regional and global spatial patterns and variations. This dataset provides long-term daily seamless NO₂ TotVCD information across the globe, which can facilitate the detection of short-term NO₂ changes for identifying emission sources and support the analysis of long-term NO₂ patterns for air pollution assessment.