Improvement of MODIS cloud mask over severe polluted eastern China

Area of Science:

• Atmospheric Science
• Remote Sensing
• Environmental Science

Background:

• MODIS cloud mask product (MYD35) frequently overestimates cloud cover in heavily polluted areas.
• Dense aerosols are often misclassified as clouds by passive remote sensing, posing a challenge for accurate atmospheric monitoring.
• Distinguishing between fog and haze in eastern China is difficult due to their frequent co-occurrence.

Purpose of the Study:

• To develop and validate an algorithm for rectifying cloud-free pixels misclassified as cloudy in the MYD35 product.
• To improve the accuracy of cloud detection in regions with severe air pollution, specifically eastern China.
• To assess the algorithm's performance in identifying surface fog mixed with haze.

Main Methods:

• Developed an algorithm combining a screening method and adjusted Fisher Discriminant Analysis (AFDA).
• Utilized CALIPSO vertical feature mask (VFM) product as a reference for accurate cloud classification.
• Validated the algorithm's performance against surface-observed fog data.

Main Results:

• The algorithm achieved an average accuracy of 96.72% in discriminating true clouds from misclassified ones, including those caused by haze.
• Demonstrated good performance in identifying surface fog in eastern China with 81.53% accuracy.
• Filtering misclassified pixels significantly altered cloud properties, decreasing cloud cover by 0.13 and impacting cloud top height, optical thickness, effective radius, and water path.

Conclusions:

• The developed algorithm effectively corrects cloud misclassification in the MYD35 product, particularly in polluted environments.
• This method enhances the reliability of satellite-derived cloud data for atmospheric studies.
• The improved cloud data has significant implications for understanding cloud properties and their interactions with aerosols and climate.