Merging multi-sensor satellite AOD products using spatio-temporal statistical method with uncertainties at pixel scale
Abstract
Merging multi-sensor aerosol optical depth(AOD) products is an effective way to produce a more spatio-temporally complete and accurate AOD product. A spatio-temporal statistical data fusion framework based on a Bayesian Maximum Entropy (BME) method was developed for merging satellite AOD products in East Asia. The advantages of the presented merging framework are that it not only utilize the spatio-temporal autocorrelations, but also explicitly incorporates the uncertainties of the AOD products being merged. The MOD04_L2 and the SWDB_L2 derived from the SeaWiFS were used for merging. The uncertainties of the AOD products being merged were quantified by using the Triple Collocation method. The results show that the completeness of the merged AOD data is 96.3%,which is significantly superior to the completeness of MOD04_L2 (22.9%) and SWDB_L2 (20.2%). The merged AOD were validated using the AERONET AOD records. the results show that the accuracy statistics of the merged AOD are close to those of MODIS AOD products. The accuracies of merged AOD are consistent, even while both MODIS and SeaWiFS AOD observations are not available.
- Publication:
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AGU Fall Meeting Abstracts
- Pub Date:
- December 2018
- Bibcode:
- 2018AGUFM.A51L2331B
- Keywords:
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- 0305 Aerosols and particles;
- ATMOSPHERIC COMPOSITION AND STRUCTUREDE: 0322 Constituent sources and sinks;
- ATMOSPHERIC COMPOSITION AND STRUCTUREDE: 0345 Pollution: urban and regional;
- ATMOSPHERIC COMPOSITION AND STRUCTUREDE: 3305 Climate change and variability;
- ATMOSPHERIC PROCESSES