Abstract: Timely county-level estimation of wheat harvesting area was essential for cross-regional machinery dispatching, operational resource coordination, and emergency response during the concentrated harvest season. However, conventional monitoring workflows based on post-processing of agricultural machinery trajectories usually produced statistical results by noon of the following day, which limited their value for same-day decision-making. To address this time-lag problem, a rapid prediction method for county-level wheat harvesting area was developed by integrating heatmaps derived from operation trajectories with meteorological information. The study used operation trajectory data from the national agricultural machinery trajectory big data platform, county-level daily harvested area generated by standardized trajectory post-processing, and meteorological data derived from the European Centre for Medium-Range Weather Forecasts ERA5-Land dataset. For each county and day, operation trajectory points were converted into heatmaps through kernel density estimation. A Residual Network-152 model was then used as a fixed feature extractor after removal of the final fully connected classification layer, and deep spatial features were obtained from the heatmaps. These heatmap features were fused with cumulative precipitation, average air temperature, and average humidity aggregated up to 11:00 a.m. A random forest regression model was subsequently established to estimate the final cumulative harvested area of the same day from morning information. The model was trained and evaluated using data from 40 counties distributed across 8 major winter-wheat-producing provinces in China, namely Shanxi, Shaanxi, Henan, Hebei, Jiangsu, Anhui, Hubei, and Shandong. The observation window was uniformly defined from May 25 to June 25 in 2021–2023. Samples from 2021 and 2022 were used for model training, while samples from 2023 were used for testing. The results showed that the proposed method achieved stable prediction performance across all 40 counties. The coefficient of determination exceeded 0.6 in every county, with an average of 0.71 and a maximum of 0.91. The normalized root mean square error ranged from 0.06 to 0.21, with an average of 0.14. Better performance was observed in large and contiguous plain areas, where operation hotspots in the heatmaps were more coherent and easier to characterize. For example, the model performed particularly well in Quanjiao County, Feixi County, Wenshang County, Wudi County, Hua County, and Qing County. In contrast, prediction accuracy was relatively lower, though still acceptable, in fragmented hilly and mountainous counties such as Hengshan District, Lantian County, Yingshan County, Tongshan County, and Jianshi County. This decrease in accuracy was associated with more dispersed field patterns, weaker aggregation structures in the heatmaps, and possible heterogeneity in terminal coverage. The ablation analysis further clarified the contribution of each input source. When only heatmap features were used, the average coefficient of determination reached 0.62 and the average normalized root mean square error was 0.17, indicating that the heatmaps already captured most of the effective information related to harvesting intensity and spatial concentration. When only meteorological inputs were used, the average coefficient of determination dropped sharply to 0.07 and the average normalized root mean square error increased to 0.29, suggesting that meteorological conditions alone were insufficient to describe daily harvesting dynamics. When heatmap features and meteorological factors were combined, the average coefficient of determination increased to 0.71 and the average normalized root mean square error decreased to 0.14, confirming that meteorological information effectively complemented the operation-state information represented by the heatmaps. In addition, the sensitivity analysis on the Gaussian smoothing scale showed that the best overall performance was obtained when the smoothing parameter was set to 7 pixels, which balanced local noise suppression and preservation of spatial differences. Overall, the proposed method shifted county-level wheat harvesting area estimation from next-day statistics to same-day midday prediction while maintaining good agreement with trajectory-derived reference values. The result output time was advanced by about 20 hours, which demonstrated clear practical value for same-day machinery scheduling and rapid operational response during the wheat harvest season. At the same time, the results indicated that future improvements should further consider terminal coverage heterogeneity and complex terrain conditions to enhance model robustness in fragmented production regions.