Soil available nutrient (SAN) plays an important role in crop growth, yield formation, and plant-soil-atmosphere system exchange. Nitrogen (N), phosphorus (P) and potassium (K) are recognized as three primary nutrients in crop production. Accurate and timely information on SAN conditions at key crop growth stages is important for developing beneficial management practices. While traditional field sampling can obtain reliable information for limited number of sites, it is infeasible for spatially intensive sampling across an extended area at frequent temporal intervals. With recent advancements in Earth observation (EO) technologies, both hardware and software, spatial-temporal information on soil nutrients and crop growth conditions can be successfully captured. Conventional methods to link EO data with SAN conditions rely heavily on statistical models. The robustness and accuracy of these models require further improvements. In this study, we developed a new approach to improve model performance by integrating the World Food Studies (WOFOST) model and time series EO data. First, the WOFOST model was modified to simulate the daily nutrient-limited crop growth. Then the Ensemble Kalman Filter (EnKF) method was used to assimilate the time-series data acquired by an unmanned aerial vehicle (UAV) into the modified WOFOST model to simulate crop growth. Through comparison of the above two simulations, errors in the nutrient-limited crop growth caused by inaccurate SAN input were obtained. By eliminating these errors, a method was developed to estimate the SAN status. Finally, a field experiment was conducted on spring maize to assess the SAN estimation performance of the proposed method. The results demonstrate that, in addition to providing improved spatial details, the accuracy of the SAN estimation also improved through the synergy of the UAV data and WOFOST model.