粳稻冠层叶绿素含量PSO-ELM高光谱遥感反演估算

目的 叶绿素含量是表征粳稻生长状态的重要指示信息，利用无人机高光谱遥感技术及时获取区域尺度的粳稻叶绿素含量。方法 以2016—2017年沈阳农业大学辽中水稻实验站粳稻无人机遥感试验数据为基础，利用连续投影算法(SPA)进行有效波段的提取，提取的特征波段分别为410、481、533、702和798 nm。将提取出的特征波段作为输入，利用极限学习机(ELM)和粒子群优化的极限学习机(PSO-ELM)分别建立粳稻冠层叶绿素含量反演模型。在PSO-ELM模型中，针对PSO算法的种群规模(p)、惯性权重(w)、学习因子(C₁、C₂)、速度位置相关系数(m)这5个参数进行了优化。结果 确定了最优参数：p为80，w为0.9~0.3线性递减，C₁和C₂分别为2.80和1.10，m为0.60。利用优化后的ELM和PSO-ELM所建立的粳稻冠层叶绿素含量模型的决定系数分别为0.734和0.887，均方根误差分别为1.824和0.783。结论 利用优化后的PSO-ELM建立的粳稻叶绿素含量反演模型精度要明显高于单纯利用ELM建立的反演模型，前者具有较好的粳稻叶绿素含量反演能力。本研究为东北粳稻叶绿素含量反演无人机遥感诊断提供了数据支撑和应用基础。

Inversion model of chlorophyll content in japonica rice canopy based on PSO-ELM and hyper-spectral remote sensing

Objective Chlorophyll content is an important indicator of the growth status of japonica rice. This study was aimed at obtaining chlorophyll content of japonica rice in a regional scale in time with UAV hyper-spectral remote sensing technology.Method This study was based on the UAV remote sensing test data of japonica rice in Liaozhong Experiment Station of Shenyang Agricultural University from 2016 to 2017. The successive projection algorithm (SPA) was used to extract the effective bands including 410, 481, 533, 702 and 798 nm. The extracted characteristic bands were used as the input, and the inversion models of chlorophyll contents in japonica rice canopy were established respectively using the extreme learning machine (ELM) and particle swarm optimization-extreme learning machine (PSO-ELM). In the PSO-ELM model, five parameters of PSO algorithm including proportion of population (p), inertial weight (w), learning factors (C₁, C₂), and velocity position correlation coefficient (m) were optimized.Result The optimal parameters were determined: p was 80, w was from 0.9 to 0.3 with a linear decline, C₁ and C₂ were 2.80 and 1.10 respectively, and m was 0.60. For the established models of chlorophyll content in japonica rice using the optimized ELM and PSO-ELM, the determination coefficients were 0.734 and 0.887 respectively, and the mean square error were 1.824 and 0.783 respectively.Conclusion The inversion model for chlorophyll content in japonica rice based on the optimized PSO-ELM has higher precision compared with the model based on ELM, and has better inversion ability of chlorophyll content in japonica rice. This study provides data support and application basis for the diagnosis of chlorophyll content in japonica rice by UAV hyper-spectral remote sensing technology in Northeast China.