Aerosol Optical Depth Retrieval Over Ocean and Land From Fengyun-3F MERSI-III: First Results and Validation
Leiku Yang, Xiuqing Hu, Yizhe Fan, Weiqian Ji, Ping Zhang, Xiaofeng Lü, Xiaoqian Cheng
- Year
- 2025
- Citations
- 1
Abstract
The Fengyun-3F (FY-3F) satellite, China's latest launched polar-orbiting meteorological satellite with morning overpass, carries the advanced Medium Resolution Spectral Imager-III (MERSI-III). Sharing similar characteristics with the Moderate Resolution Imaging Spectroradiometer (MODIS), MERSI-III/FY-3F has the potential to fill the observational gap in morning-orbit aerosol monitoring once Terra's mission concludes. This study adapts the aerosol retrieval algorithm originally developed for the MERSI-II sensor aboard the FY-3D satellite to the MERSI-III sensor. Algorithm improvements have been made to accommodate MERSI-III's characteristics, primarily focusing on optimizing the surface reflectance estimation model and updating the lookup tables. The improved algorithm has been applied to global MERSI-III observations for the entire year of 2024, and the first aerosol optical depth (AOD) retrieval results over ocean and land for this sensor have been produced and validated. The results show that MERSI-III AOD retrievals agree well with Aerosol Robotic Network (AERONET) data, with 69.8% and 71.0% of retrievals falling within expected error (EE) envelopes over ocean (± (0.03 + 10%)) and land (± (0.05 + 20%)), respectively. The accuracy of MERSI-III retrievals is comparable to that of Terra MODIS Dark Target (DT) products (EE: 65.3% over ocean, 73.4% over land). Comparative analysis of collocated MERSI-III and MODIS AOD datasets reveals consistent spatial patterns, although regional biases are observed in areas with high aerosol loading and near desert fringes. These results indicate that MERSI-III/FY-3F can provide reliable global aerosol observations and is expected to serve as an important candidate for the AOD data record from the morning-orbiting Terra/MODIS satellite.
Keywords
Related papers
Statistical Learning Theory
Yuhai Wu, Vladimir Vapnik
1999
Fractional Differential Equations
Igor Podlubný
2025
Applied Nonlinear Control
Jean-Jacques Slotine, Weiping Li
1991
Genetic Programming: On the Programming of Computers by Means of Natural Selection
John R. Koza
1992