为探究遥感监测水稻冠层叶片氮素含量的较优高光谱反演模型, 以水稻小区试验为基础, 获取了不同生长期水稻冠层高光谱数据。 在综合比较一阶导数变换(1-Der)、 标准正态变量变换(SNV)和SG滤波法等处理方法基础上, 提出一种将SNV与一阶导数变换的SG滤波法相结合的光谱处理方法(SNV-FDSGF), 并将处理后的数据经无信息变量消除法(UVE)与竞争自适应重加权采样法(CARS)选出不同生长期的敏感波段。 将各生长期的敏感波段两两随机组合, 并构建与水稻叶片含氮量相关性较高的差值光谱植被指数(DSI)、 比值光谱植被指数(RSI)、 归一化光谱植被指数(NDSI)。 其中分蘖、 拔节和抽穗3个时期的最优植被指数和决定系数R2分别为: DSI(R857, R623), 0.704; DSI(R670, R578), 0.786; DSI(R995, R508), 0.754。 以各生长期内的较优的三种植被指数作为输入分别构建自适应差分优化的极限学习机(SaDE-ELM)、 径向基神经网络(RBF-NN)以及粒子群优化的BP神经网络(PSO-BPNN)反演模型。 结果表明: SaDE-ELM建模效果最好, 在模型稳定性和预测能力上比RBF-NN和PSO-BPNN都有了明显提高, 各生长期反演模型的训练集和验证集决定系数R2均在0.810以上, RMSE均在0.400以下, 可为东北水粳稻冠层叶片含氮量的检测与评估提供科学和技术依据。

In order to explore a better hyperspectral inversion model for monitoring nitrogen content in rice canopy leaves by remote sensing, based on rice plot experiments, the canopy height spectral data of rice at different growth stages were obtained. Based on the comprehensive comparison of the first derivative (1-Der), standard normal variable transformation (SNV) and SG smoothing method, a spectral processing method (SNV-FDSGF) combining standard normal variable transformation with SG filtering method of first derivative was proposed. The sensitive bands of different growth stages were screened out by non-information variable - competitive adaptive reweighted sampling method (UVE-CARS). Two sensitive bands of each growth period were randomly combined to construct a difference spectrum index DSI (difference spectral index), a ratio spectral index RSI (ratio vegetation index) and a normalized spectrum index NDSI (normalized defference spectral index) with high correlation with nitrogen content in rice leaves. Among them, the optimal vegetation index and determination coefficient R2 at the tillering, jointing and heading stages were: DSI(R857, R623), 0.704; DSI(R670, R578), 0.786; DSI(R995, R508), 0.754. Using the superior three planting indices in each growth period as inputs, the adaptive differential optimization extreme learning machine (SaDE-ELM), radial basis function (RBF-NN) and particle swarm optimization BP neural network (PSO-BPNN) inversion models were constructed respectively. The results showed that SaDE-ELM had the best modeling effect. Compared with RBF-NN and PSO-BPNN, the stability and prediction ability of the model were significantly improved. The determination coefficient R2 of training set and verification set of each growth phase inversion model was above 0.810 and RMSE was below 0.400, which could provide certain theoretical basis for quantitative prediction of nitrogen content in rice canopy leaves.