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In order to improve the prediction accuracy of winter wheat yield in large scale region, taking remote sensing data, meteorological data, soil moisture data of Henan Province from 2005 to 2019 as characteristic variables, the correlation between them and wheat yield was analyzed. The importance of characteristic variables was analyzed based on random forest algorithm. And a wheat yield prediction model was established by means of fusing multi-source spatio-temporal data. The results showed that enhanced vegetation index (EVI), solar-induced chlorophyll fluorescence (SIF) and elevation was an important factor for remote sensing estimation of wheat yield, which was highly positively correlated with wheat yield. The importance of EVI, SIF and elevation to wheat yield exceeded 0.45, far greater than soil moisture, rainfall, maximum temperature, minimum temperature and other factors. The yield prediction model based on random forest algorithm and constructed with the wheat growth stage from October to next May and October to next April as the characteristic variables had higher accuracy, coefficient of determination (R2) were 0.85 and 0.84, and respectively, the root mean square error (RMSE) were 821.55kg/hm2 and 832.01kg/hm2. The prediction relative errors in hills and mountains of western and southern Henan was higher than that in plain areas. The research results provided a reference for large-scale crop yield.