GF-6 WFV is a high-spatial resolution multi-spectral sensor loaded on Chinese GF-6 satellite, which realizes the combination of high spatial resolution and wide coverage. Accurately identifying the cloud pixels of GF-6 WFV data is of great significance for supporting agricultural resources monitoring, forestry resources investigation, disaster prevention and mitigation and other industry applications. Based on the global land cover product—FROM-GLC10 (Finer Resolution Observation and Monitoring-Global Land Cover 10) data, the LCCD (Land Cover-based Cloud Detection) algorithm is improved to carry out cloud detection of GF-6 WFV data in the paper. Taking FROM-GLC10 data as a priori data, fully considering the change of reflectivity of different surface types, different methods are used to set thresholds for each surface type. The accuracy of cloud detection results was evaluated by visual interpretation, and the cloud accuracy rate as a whole reached 92.46%, among which the cloud accuracy rates of vegetation type, water type and highlighted surface type were 93.09%, 95.60% and 88.70%, respectively. The results show that the improved cloud detection algorithm based on surface type effectively improves the accuracy of cloud detection of GF-6 WFV data.