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In order to further accurately and real-time monitor the growth of winter wheat and estimate its yield, taking Guanzhong Plain in Shaanxi Province as study area, and vegetation temperature condition index (VTCI), leaf area index (LAI), fraction of photosynthetically active radiation (FPAR) at the ten-day or growth stage scales were selected as remotely sensed characteristic parameters. The GRU model was constructed based on different input parameters and time scales to obtain the growth comprehensive monitoring index I of winter wheat. The results showed that the accuracy of the models at the ten-day scale were generally higher than those of the growth stage scales. Based on the five-fold cross-validation method, the robustness of the multi-parameter GRU model on the ten-day scale was further verified, and the winter wheat yield was estimated based on the linear regression model between the growth comprehensive monitoring index I and the official yield records. The results showed that the R2 between the estimated and official yield records of winter wheat was 0.62, the RMSE was 509.08kg/hm2, the mean relative error (MRE) was 9.01%, and the correlation reached the extremely significant level (P<0.01), indicating that the multi-parameter yield estimation model at the ten-day scale can accurately estimate the yield of winter wheat in the Guanzhong Plain. The distribution of yield presented the spatial characteristics of high yield in the west and low yield in the east, and the inter-annual change characteristics of overall stability and steady growth. In addition, based on the GRU model, the cumulative effect of winter wheat growth was captured, and the influence of inputting parameters step by step in consecutive ten days on yield estimation was analyzed. The results showed that the model had the ability to identify the key growth stages of winter wheat, and late March to late April was the critical period for the growth of winter wheat.