A New Geostationary Satellite-Based Snow Cover Recognition Method for FY-4A AGRI

Abstract

Snow cover is an important component of the cryosphere. Clouds have a large influence on optical remote sensing satellites when recognizing snow cover. Geostationary satellites, due to their high-frequency observations over coverage areas, can effectively compensate for the drawback of snow cover recognition from polar orbit optical satellites under cloud-covered conditions. However, past geostationary satellites have relatively few band settings to produce sensitive factors for snow cover recognition. The FY-4A Advanced Geostationary Radiation Imager (AGRI) satellite has the advantage of high temporal resolution with a wealth of bands, which highlights its potential in reducing the impact of clouds and accurately obtaining snow cover information. Based on the advantages of FY-4A AGRI data and the flow characteristics of clouds, we developed an improved maximum brightness temperature image synthesis algorithm, which can greatly reduce the probability of cloud and snow cover misclassification. Combining the features of FY-4A AGRI data, we reorganized the snow cover recognition factor and developed a new snow cover recognition method. The results show that the proposed method can reduce cloud cover by 57.172% compared with MOD10A1 data. After evaluating the proposed method using meteorological ground observation datasets and MOD10A1 data, we found that the overall accuracy of the proposed method can reach 94.11% and 98.55%, respectively, and the F-score (FS) can reach 73.05% and 85.40%, respectively.