针对使用阈值方法反演云相态存在的不足，本文提出了一种基于Self-Organizing Feature Map(SOFM)神经网络的云相态反演方法。采用覆盖中国地域的FengYun-3A/Visible and InfRared Radiometer(FY-3A/VIRR)多光谱图像开展了云相态反演实验。实验结果表明：SOFM 神经网络方法与K-means 方法的结果具有较好的一致性，且SOFM神经网络方法反演云相态的准确性优于FY-3A 业务产品。此外，SOFM 神经网络方法反演云相态所需时间仅为FY-3A 业务产品的约1/3。

To address problems of cloud phase retrieval using the threshold method, a cloud phase retrieval approach based on Self-Organizing Feature Map (SOFM) neural network was proposed. Cloud phase retrieval experiments were conducted using FengYun-3A/Visible and InfRared Radiometer (FY-3A/VIRR) multi-channel images, which cover the China’s territory. Experiment results indicated that the results from the SOFM neural network approach and the K-means method have good consistency, and the retrieval accuracy of the SOFM neural network exceeds that of the FY-3A operational product. Additionally, retrieval time consumed by the SOFM neural network approach is only about one third of that of the FY-3A operational product.