Filling the missing data gaps of daily MODIS AOD using spatiotemporal interpolation

Area of Science:

• Atmospheric Science
• Environmental Monitoring
• Geostatistics

Background:

• Aerosols significantly impact climate, environment, and human health.
• Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth (AOD) products are crucial for monitoring atmospheric conditions.
• Data gaps in MODIS AOD products, especially under cloudy conditions, hinder accurate analysis.

Purpose of the Study:

• To implement a geostatistical data interpolation framework using spatiotemporal kriging.
• To address data gaps in MODIS AOD products over Beijing, China.
• To evaluate the effectiveness of spatiotemporal kriging compared to ordinary kriging for AOD interpolation.

Main Methods:

• Application of a geostatistical data interpolation framework.
• Utilizing spatiotemporal kriging to account for spatial, temporal, and spatiotemporal autocorrelations.
• Comparison with ordinary kriging for filling data gaps in satellite AOD products.

Main Results:

• Spatiotemporal interpolation achieved 67.73% data completeness, outperforming the original MODIS product (14.27%) and spatial kriging (33.3%).
• The mean absolute error for spatiotemporal kriging (0.07) was lower than that for ordinary kriging (0.09).
• The method effectively interpolates AOD products, enhancing data availability for research.

Conclusions:

• Spatiotemporal kriging is a superior method for interpolating satellite AOD products, significantly improving data completeness.
• This approach enhances the utility of MODIS AOD data for climate change and air pollution research.
• The improved data quality facilitates more robust environmental and atmospheric studies.