Determining the mid-season nitrogen status of rice is important for precision application of fertilizer to optimize productivity. While there has been much research aimed at developing remote-sensing-based models to predict the nitrogen status of rice, this has been predominantly limited to scientific small plot trials, relying on experts performing radiometric calibrations, encompassing limited cultivars, seasons and locations, and uniform management practices. As such, there has been little testing of models at commercial scale, against the range of conditions encountered across entire growing regions. To fill this gap, this work brings together four years of data, from both experimental replicated plot trials (38 datasets with 1734 observations) and commercial farms (12 datasets with 106 observations). Using commercial scale imagery acquired from airplanes, a number of nitrogen uptake modeling methodologies were evaluated. Universal single vegetation index based linear regression models had prediction root mean squared error (RMSE) of more than 45 kg/ha when tested at the 12 commercial sites. Machine learning models using multiple remote sensing features were able to improve predictions somewhat (RMSE $>$ 30 kg/ha). Practically useful accuracies were achieved after using three local field samples to calibrate models to each field image. The prediction RMSE using this methodology was 22.9 kg/ha, or 19.4%. This approach enables provision of optimal variable-rate mid-season rice fertilizer prescriptions to growers, while motivating continued research towards development of methods that reduce requirement of local sampling.

确定水稻的中季氮状态对于精确施肥以优化生产力非常重要。虽然有很多研究旨在开发基于遥感的模型来预测水稻的氮状况，但这主要限于科学的小块试验，依靠专家进行辐射校准，包括有限的品种、季节和地点，以及统一的管理做法。因此，针对整个种植区遇到的各种条件，几乎没有对商业规模的模型进行测试。为了填补这一空白，这项工作汇集了四年的数据，来自实验性重复小区试验（38 个数据集，包含 1734 个观测值）和商业农场（12 个数据集，包含 106 个观测值）。使用从飞机上获得的商业规模的图像，对许多氮吸收建模方法进行了评估。在 12 个商业地点进行测试时，基于通用单一植被指数的线性回归模型的预测均方根误差 (RMSE) 超过 45 公斤/公顷。使用多个遥感特征的机器学习模型能够在一定程度上改善预测（RMSE$>$30 公斤/公顷）。在使用三个局部场样本将模型校准到每个场图像后，获得了实用的精度。使用这种方法的预测 RMSE 为 22.9 公斤/公顷，或 19.4%。这种方法能够为种植者提供最佳的可变比率中季稻肥处方，同时推动持续研究开发减少当地采样要求的方法。