Precision farming based on soil total nitrogen status is crucial to informed fertilization strategies while enhancing environmental quality of agricultural ecosystems. Hyperspectral technology was applied for rapid assessment of soil total nitrogen content in a long-term fertilization experiment of wheat-corn rotation system. The first-order derivative of squared spectra and Soil Adjusted Spectral Index transformation provided the highest correlation with soil total nitrogen contents. Inversion modeling results involving multiple linear regression, back propagation neural network (BPNN), and partial least square regression indicated that BPNN provided the best performance with coefficient of determination (R²) reaching 0.8 and 0.9, respectively, for wheat and corn seasons. Model performance with wheat and corn season combined was poor due to seasonal difference in hyperspectral patterns and different decomposition of straws returned to the field. This study provided a quantitative tool for rapid and accurate diagnosis of soil total nitrogen content for precision agriculture.

在提高农业生态系统环境质量的同时，基于土壤总氮状况的精确农业对明智的施肥策略至关重要。在小麦-玉米轮作系统的长期施肥试验中，高光谱技术被用于土壤总氮含量的快速评估。平方光谱的一阶导数和土壤调整的光谱指数转换提供了与土壤总氮含量的最高相关性。涉及多重线性回归，反向传播神经网络（BPNN）和偏最小二乘回归的反演建模结果表明，BPNN在确定系数方面提供了最佳性能（R ²）在小麦和玉米季节分别达到0.8和0.9。由于高光谱模式的季节性差异以及返回田间秸秆的分解不同，因此小麦和玉米季节组合的模型性能很差。这项研究提供了一种定量工具，可以快速，准确地诊断精密农业的土壤总氮含量。