摘要
通过人工田间诱发不同等级条锈病,在不同生育期测定感染不同严重程度条锈病的冬小麦冠层光谱与冠层叶绿素密度(canopy chlorophyll density,CCD)。把CCD与高光谱指数进行相关性分析,选取相关系数大于0.7的指数构建反演模型,并对模型进行检验,结果表明微分指数(D750-D550)/(D750+D550)反演精度以及稳定性最好,其次是微分指数(D725-D702)/(D725+D702)。对上述两个微分指数分别进行饱和度分析,发现当CCD大于12μg.cm-2时微分指数(D750-D550)/(D750+D550)易达到饱和,因此当CCD小于12μg.cm-2时,微分指数(D750-D550)/(D750+D550)反演CCD结果较好;但当CCD大于12μg.cm-2时,利用微分指数(D725-D702)/(D725+D702)反演CCD较好,该指数不易达到饱和状态。由于CCD与小麦病情指数(diseaseindex,DI)之间存在极显著负相关性,利用高光谱遥感精确估测小麦冠层CCD,不仅可以帮助判断作物的长势,而且可为识别小麦病害提供辅助信息。因此,该研究对于农业防灾减灾也具有重要现实意义。
The canopy reflectance of winter wheat infected with different severity yellow rust was collected in the fields and canopy chlorophyll density （CCD） of the whole wheat was measured in the laboratory.The correlation was analyzed between hyperspectral indices and CCDs,the indices with relationship coefficients more than 0.7 were selected to build the inversion models,and comparing the predicted results and measured results to test the models,the results showed the first derivative index （D750-D550）/（D750＋D550） has higher prediction precision than other indices,while the next is first derivative index （D725-D702）/（D725＋D702）.Saturation analysis was performed for the above indices,the result indicated that when CCD was more than 12 μg·cm-2,the first derivative index （D750-D550）/（D750＋D550） was easiest to get to saturation level.Therefore,when CCD was less than 12 μg·cm-2,the first derivative index （D750-D550）/（D750＋D550） could be used to estimate wheat CCD and had higher prediction precision than other indices;and when CCD was more than 12 μg·cm-2,the first derivative index （D725-D702）/（D725＋D702） was not easiest to reach saturation level and could be used to estimate wheat CCD.There is a significant minus correlation between CCD and disease index （DI）,moreover,accurate estimation of CCD by using hyperspectral remote sensing not only can monitor wheat growth,but also can provide assistant information for identification of wheat disease.Therefore,this study has important meaning for prevention and reduction of disaster in agricultural field.
出处
《光谱学与光谱分析》
SCIE
EI
CAS
CSCD
北大核心
2010年第8期2243-2247,共5页
Spectroscopy and Spectral Analysis
基金
国家自然科学基金项目(40701119)
国际科技合作计划项目(2007DFA20640)
中央高校基本科研业务费专项资金(2009QD13)
国土环境与灾害监测国家测绘局重点实验室开放基金项目(LEDM2009B04)资助
关键词
高光谱遥感
小麦
条锈病胁迫
冠层叶绿素密度
饱和度分析
反演模型
Hyperspectral remote sensing
Wheat
Yellow rust stress
Canopy chlorophyll density
Saturation analysis
Inversion model
