An Adaptive Dark-Target Algorithm for Retrieving Land AOD Applied to FY-4B/AGRI Data

Abstract

The Fengyun 4B (FY-4B) as an operational satellite was launched on 3 June 2021. The advanced geostationary radiation imager (AGRI) aboard on the FY-4B has ability to monitor aerosols information. The objective of this study is to introduce the operational algorithm for retrieving land aerosol based on FY-4B/AGRI data, which is called as an adaptive dark-target algorithm. First, on the basis of FY-4B/AGRI cloud mask and snow cover products, a novelty identification scheme was proposed to classify the pixels into cloud, haze and clear categories. Then, aiming at the characteristics of FY-4B/AGRI instrument, the adaptive relationships of surface reflectance varying with the normalized vegetation index or view zenith angle were established by atmospheric correction. By validating the AGRI retrievals every 15 min with AERONET AOD, AGRI performance (R: 0.95, bias: –0.014, RMSE: 0.091, within expected error (EE:): 81.99%) was comparable to that of Aqua/MODIS deep blue AOD (R: 0.95, bias: 0.024, RMSE: 0.095, EE: 78.57%), while have the largest difference with those of MYDDT. At daily mean scale, the statistics of AGRI AOD against AERONET performed well (R: 0.93, bias: 0.062, RMSE: 0.18, EE: 64%). Additionally, the agreement of AGRI retrievals with AERONET AOD varied with observation moments and stations. The FY-4B/AGRI operational aerosol product based on adaptive dark target algorithm were proved to be robust.

Keywords

• Advanced geostationary radiation imager (AGRI)
• aerosol optical depth (AOD)
• dark target algorithm (DT)
• Fengyun-4B (FY4B)
• land