Soil-available nutrients (SANs)are essential for crop growth and yield formation. Appropriate variable rate fertilization (VRF) can control SAN at a normal level to avoid unnecessary damage to sustainable production capacity. The precondition of optimizing the application of VRF is obtaining the real-time status of SAN. A new method for SAN estimation has been proposed by integrating modified World Food Studies (WOFOST) and time-series satellite remote sensing (RS) data. This method can provide field scale SAN estimations with high accuracy. However, the estimation accuracy at a subfield scale was low for VRF application because of the poor spatial resolution of common satellite imagery. In this letter, the subfield SAN estimations were optimized to ensure the VRF value. Time-series multispectral images acquired by an unmanned aerial vehicle (UAV) were used to replace common satellite data, and the SAN values for haplic phaeozem in selected spring maize plot in Hongxing Farm (48°09ʹ N, 127°03ʹ E) were estimated. Based on the field SAN data, the estimation accuracies using satellite data and UAV data were analyzed. The results show that the UAV data improved SAN estimations at the subfield scale).

土壤有效养分（SAN）对于作物生长和产量形成至关重要。适当的可变速率施肥（VRF）可以将SAN控制在正常水平，以避免对可持续生产能力造成不必要的损害。优化VRF应用的前提是获取SAN的实时状态。通过整合修改后的世界粮食研究（WOFOST）和时间序列卫星遥感（RS）数据，提出了一种SAN估计的新方法。该方法可以提供高精度的现场规模SAN估计。但是，由于普通卫星图像的空间分辨率差，因此对于VRF应用，在子域尺度上的估计精度较低。在这封信中，优化了子字段SAN估计以确保VRF值。用无人飞行器（UAV）获取的时间序列多光谱图像代替常见的卫星数据，并估算了红星农场（48°09ʹ N，127°03ʹ E）选定春玉米田中的触觉pha的SAN值。基于现场SAN数据，分析了使用卫星数据和UAV数据的估计准确性。结果表明，无人机数据改进了子域规模的SAN估计。