The significant wave height (SWH) is of great importance in industries such as ocean engineering, marine resource development, shipping and transportation. Haiyang-2C (HY-2C), the 2nd operational satellite of China’s marine dynamic exploration series, can provide all-weather, all-day, global observations of wave height, wind, and temperature. The altimeter can only measure the nadir wave height and other information, and the scatterometer can obtain the wind field with a wide swath. In this paper, a deep learning approach is applied to produce a wide swath SWH data through the wind field from the scatterometer and the nadir wave height from altimeter. Two validation sets, 1-month data at 6-minute intervals and 1-day data with an interval of 10 s, are fed into the trained model. Experiments indicate that the extending nadir SWH yields a real-time wide swath grid product along track, which can be offered as support for oceanographic study, and it is superior to take the swell characteristics of ERA5 into account as the input of wide swath SWH model. In conclusion, the verification results demonstrate the effectiveness and feasibility of the wide swath SWH model.