Daily seamless reconstruction of aerosol optical depth product from a single satellite data source using the novel stream method

Satellite aerosol products provide valuable large-scale atmospheric information for environment and climate research. Nevertheless, limitations due to cloud contamination and retrieval assumptions often result in significant gaps in Aerosol Optical Depth (AOD) observations, diminishing their representativeness and utility. Therefore, we propose an adaptive spatiotemporal reconstruction method (Spatio-Temporal Reconstruction with Ecw and Auds Method, STREAM) based solely on a single data source, which integrates Empirical Correlation Weighting (ECW) for interpolation with Adaptive Up/Down Scaling (AUDS) for seamless reconstruction. This method was applied to Moderate Resolution Imaging Spectroradiometer (MODIS) Deep Blue (DB) AOD over the North China Plain (NCP) from 2002 to 2023 at a 0.05° resolution. Case comparisons demonstrate that STREAM efficiently fills data gaps, and the STREAM AOD presents strong concordance with both the DB AOD and reference datasets. Cross-validation indicates that as the missing rate rises, the correlation (R) between the STREAM AOD and the DB AOD decreases, while Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) increase. Validation against AERONET data shows that STREAM AOD achieves an R value of 0.88, RMSE of 0.29, and MAE of 0.18 for STREAM AOD, with 52.39% of the data falling within the expected error range. Compared to Long-term Gap-free High-resolution Air Pollutants (LGHAP) AOD, our approach reveals minor discrepancies in values and spatial distribution despite relying on a single data source. The robust performance of STREAM AOD in the NCP highlights potential applicability to utilize in broader regions as well as other atmospheric remote sensing products.