基于无人机高光谱遥感的粳稻冠层叶绿素含量反演研究

摘要：叶绿素含量是衡量粳稻生长状况的重要指标。本文的目的是利用无人机采集的水稻冠层高光谱图像，建立预测粳 稻叶绿素含量的反演模型。基于无人机的高光谱遥感技术可提供大范围内叶绿素含量的实时、高性价比监测。该研究基 于沈阳农学院院士粳稻实验基地2018年和2019年收集的高光谱数据。为了提取嵌入在高维高光谱数据中的显著信息， 我们首先使用逐次投影算法(SPA)进行降维。SPA提取特征高光谱波段作为反演模型的输入。SPA提取的特征波段分别为 410 nm、481 nm、533 nm、702 nm和798 nm。利用极限学习机(ELM)建立了反演模型，并利用粒子群算法(PSO)对模型 参数进行优化。PSO-ELM算法可以准确地模拟高光谱数据与叶绿素含量之间的非线性关系。模型的决定系数R2 = 0.791， 均方根误差RMSE = 8.215 mg/L。该模型具有较好的预测能力，可为粳稻营养诊断研究提供数据支持和模型参考。

关键词：松树枯萎病、高光谱图像、GA、SVM、分类

期刊：RESEARCH ARTICLE

DOI：https://DOI. org/10.1371/journal.pone.0238530

设备：GaiaSky

Inversion modeling of japonica rice canopy chlorophyll content with UAV hyperspectral remote sensing

Abstract：Chlorophyll content is an important indicator of the growth status of japonica rice. The objective of this paper is to develop an inversion model that can predict japonica rice chlorophyll content by using hyperspectral image of rice canopy collected with unmanned aerial vehicle (UAV). UAV-based hyperspectral remote sensing can provide timely and cost-effective monitoring of chlorophyll content over a large region. The study was based on hyperspectral data collected at the Shenyang Agricultural College Academician Japonica Rice Experimental Base in 2018 and 2019. In order to extract the salient information embedded in the highdimensional hyperspectral data, we first perform dimension reduction by using a successive projection algorithm (SPA). The SPA extracts the characteristic hyperspectral bands that are used as input to the inversion model. The characteristic bands extracted by SPA are 410 nm, 481 nm, 533 nm, 702 nm, and 798 nm, respectively. The inversion model is developed by using an extreme learning machine (ELM), the parameters of which are optimized by using particle swarm optimization (PSO). The PSO-ELM algorithm can accurately model the nonlinear relationship between hyperspectral data and chlorophyll content. The model achieves a coefficient of determination R2 = 0.791 and a root mean square error of RMSE = 8.215 mg/L. The model exhibits good predictive ability and can provide data support and model reference for research on nutrient diagnosis of japonica rice.