基于高光谱的春玉米大斑病害遥感监测指数选择

玉米大斑病是春玉米主要病害之一,采用地面光谱观测的方式构建遥感监测指数,是实施区域遥感监测的基础,也是卫星传感器谱段设计的主要依据。该文在陕西省眉县设计了人工控制小区试验,针对高抗、抗、感和高感4个品种,通过人工接种不同浓度大斑病分生孢子的方法,获得了正常、轻微、中度以及严重等4个病害感染梯度小区,并在春玉米抽雄、吐丝、乳熟以及成熟4个生长期进行了地面高光谱观测。为了实现对春玉米大斑病的遥感监测,该项研究在春玉米冠层光谱数据基础上,分析了不同种植区不同生长期春玉米冠层光谱反射率和光谱一阶微分特征,并以此确定了大斑病敏感波段位置以及病害适宜监测期,同时根据敏感波段位置的光谱特征构建了专门的春玉米大斑病的遥感监测指数,最后结合180个光谱观测样本,对比了所提指数以及其他病害指数与病害严重度之间的相关性,并通过聚类分析了所建遥感指数的稳定性。研究结果表明,乳熟期的春玉米大斑病在红边波谱内的响应较为敏感,尤其红边核心区(725~740 nm)的光谱一阶微分与病害严重程度间存在明显地单调变化关系,具有非常显著的负相关性;同时,该文所提病害监测指数与病情指数具有较高的相关性,其相关系数达到了0.995 0,最后结果表明利用红边一阶微分指数的对病害程度的聚类总体精度达到100.0%,指数值分布稳定性也更高,具有在遥感监测业务中应用的潜力。

Remote sensing index selection of leaf blight disease in spring maize based on hyperspectral data

Abstract: Leaf blight is one of the major diseases of spring corns. Analyzing crop canopy spectral features and establishing remote sensing monitoring indices by employing the method of ground spectrum test are the foundation for implementing regional disease remote sensing monitoring, and the major basis for designing satellite sensor spectrum. By taking 4 varieties of spring corns i.e. highly resistant, resistant, infected, and highly infected spring corns as the study objects, this paper designed an artificially controlled plot experiment in Meixian County, Shannxi Province. Through artificial inoculation of leaf blight spores with different concentrations, the study established 4 disease infected land plots including normal corn, mildly infected corn, moderately infected corn, and severely infected corn, and conducted ground hyperspectral observation of 4 development stages i.e. tasseling stage, silking stage, milk-ripe stage and ripe stage of spring corns. In order to realize the remote sensing monitoring on leaf blight of spring corns, based on the spring corn canopy spectral data, this paper analyzed the change features of spring corn canopy spectral reflectance and first derivative spectral value of crop areas with different disease severities and at different growth stages, and identified the sensitive band range of spring corn leaf blight and proper disease monitoring period; meanwhile the study established a special remote sensing index for spring corns based on the spectral features of sensitive wave bands, namely, the first derivative of spectral in red edge core area. Finally, to validate the effectiveness of the index proposed in this paper, the study used a total of 180 spectral observation samples in the 4 crop areas, which were obtained by using the crossing sampling method, and made a comparison between the indices and the other commonly used disease monitoring indices in terms of their correlation with disease severity. The result showed that, spring corn leaf blight with different order of severity could be more significantly represented during the milk-ripe stage of the spring corn. Along with the increase of disease severity, the reflectance in near-infrared band decreased gradually, and showed a change of gradient, which was suitable for the leaf blight remote sensing monitoring and classification of disease severity; response of first derivative of spring corn canopy spectrum was relatively sensitive, especially within the range of red edge core area (725-740 nm). There was a significant monotonous change relation between first derivative of spectrum and disease severity, showing a very significant negative correlation. The experimental result also showed that there was a relatively high correlation between the remote sensing monitoring index proposed in this paper and the disease index, with the correlation coefficient of 0.9950. Classification accuracy of different disease severity reached 100%, and the dispersion degree of index values was lower than that of other commonly used monitoring indices, with a higher distribution stability, indicating that the indices proposed in this paper can be applied in remote sensing monitoring operations.