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Flood disasters can have a serious impact on people's production and lives, and can cause huge losses in lives and property security. Based on multi-source remote sensing data, this study established decision tree classification rules through multi-source and multi-temporal feature fusion, classified ground objects before the disaster and extracted flood information in the disaster area based on optical images during the disaster, so as to achieve rapid acquisition of the disaster situation of each disaster bearing object. In the case of Qianliang Lake, which suffered from flooding in 2020, the results show that decision tree classification algorithms based on multi-temporal features can effectively integrate multi-temporal and multi spectral information to overcome the shortcomings of single-temporal image classification and achieve ground-truth object classification.