The recently deployed Transition Region Explorer(TREx)-RGB(red-green-blue)all-sky imager(ASI)is designed to capture“true color”images of the aurora and airglow.Because the 557.7 nm green line is usually the brightes...The recently deployed Transition Region Explorer(TREx)-RGB(red-green-blue)all-sky imager(ASI)is designed to capture“true color”images of the aurora and airglow.Because the 557.7 nm green line is usually the brightest emission line in visible auroras,the green channel of a TREx-RGB camera is usually dominated by the 557.7 nm emission.Under this rationale,the TREx mission does not include a specific 557.7 nm imager and is designed to use the RGB green-channel data as a proxy for the 557.7 nm aurora.In this study,we present an initial effort to establish the conversion ratio or formula linking the RGB green-channel data to the absolute intensity of 557.7 nm auroras,which is crucial for quantitative uses of the RGB data.We illustrate two approaches:(1)through a comparison with the collocated measurement of green-line auroras from the TREx spectrograph,and(2)through a comparison with the modeled green-line intensity according to realistic electron precipitation flux measurements from low-Earth-orbit satellites,with the aid of an auroral transport model.We demonstrate the procedures and provide initial results for the TREx-RGB ASIs at the Rabbit Lake and Lucky Lake stations.The RGB response is found to be nonlinear.Empirical conversion ratios or formulas between RGB green-channel data and the green-line auroral intensity are given and can be applied immediately by TREx-RGB data users.The methodology established in this study will also be applicable to the upcoming SMILE ASI mission,which will adopt a similar RGB camera system in its deployment.展开更多
为推进富含花青素的紫苏品种选育,指导逆境胁迫下的紫苏生产管理,以紫苏为研究对象,采集田间叶片并使用数码相机拍照,结合红绿蓝色彩空间(red green blue color space,RGB)和CIELab色彩空间(CIELab color space)2种图像色彩分析手段处...为推进富含花青素的紫苏品种选育,指导逆境胁迫下的紫苏生产管理,以紫苏为研究对象,采集田间叶片并使用数码相机拍照,结合红绿蓝色彩空间(red green blue color space,RGB)和CIELab色彩空间(CIELab color space)2种图像色彩分析手段处理图片,与叶片花青素含量进行相关性和显著性分析,筛选出相关系数较高的色彩参数,建立单变量回归反演模型,最终综合建模得到预测效果最优的紫苏叶片花青素含量预测模型。结果表明,在RGB色彩空间中,红光标准化值(normalized redness intensity,NRI)、绿光标准化值(normalized greenness intensity,NGI)与花青素含量呈极显著相关,其中NGI的相关系数大于NRI。当叶片正反面色彩贡献比为2∶1时,NGI与花青素含量的相关性最大,相关系数为0.8532。对比不同模型发现,以NGI为自变量建立的指数模型拟合效果最好,相关系数为0.8381,决定系数(R^(2))达0.7550。在CIELab色彩空间中,红度(a^(*))与花青素含量的相关性最好,且相关系数同样在叶片正反面色彩贡献比为2∶1时达最大,为0.7356。基于a^(*)建立的幂模型拟合效果最好,相关系数和R^(2)分别为0.7438和0.6798。分别使用NGI模型和a^(*)模型对叶片花青素含量进行估测,验证后发现a^(*)模型的预测效果更好,准确性和稳定性更高,因此以a^(*)模型为预测紫苏叶片花青素含量的最优模型。展开更多
基金jointly funded by the Canada Foundation for Innovationthe Alberta Economic Development and Trade organization+1 种基金the University of Calgarysupported by the Canadian Space Agency。
文摘The recently deployed Transition Region Explorer(TREx)-RGB(red-green-blue)all-sky imager(ASI)is designed to capture“true color”images of the aurora and airglow.Because the 557.7 nm green line is usually the brightest emission line in visible auroras,the green channel of a TREx-RGB camera is usually dominated by the 557.7 nm emission.Under this rationale,the TREx mission does not include a specific 557.7 nm imager and is designed to use the RGB green-channel data as a proxy for the 557.7 nm aurora.In this study,we present an initial effort to establish the conversion ratio or formula linking the RGB green-channel data to the absolute intensity of 557.7 nm auroras,which is crucial for quantitative uses of the RGB data.We illustrate two approaches:(1)through a comparison with the collocated measurement of green-line auroras from the TREx spectrograph,and(2)through a comparison with the modeled green-line intensity according to realistic electron precipitation flux measurements from low-Earth-orbit satellites,with the aid of an auroral transport model.We demonstrate the procedures and provide initial results for the TREx-RGB ASIs at the Rabbit Lake and Lucky Lake stations.The RGB response is found to be nonlinear.Empirical conversion ratios or formulas between RGB green-channel data and the green-line auroral intensity are given and can be applied immediately by TREx-RGB data users.The methodology established in this study will also be applicable to the upcoming SMILE ASI mission,which will adopt a similar RGB camera system in its deployment.
文摘为推进富含花青素的紫苏品种选育,指导逆境胁迫下的紫苏生产管理,以紫苏为研究对象,采集田间叶片并使用数码相机拍照,结合红绿蓝色彩空间(red green blue color space,RGB)和CIELab色彩空间(CIELab color space)2种图像色彩分析手段处理图片,与叶片花青素含量进行相关性和显著性分析,筛选出相关系数较高的色彩参数,建立单变量回归反演模型,最终综合建模得到预测效果最优的紫苏叶片花青素含量预测模型。结果表明,在RGB色彩空间中,红光标准化值(normalized redness intensity,NRI)、绿光标准化值(normalized greenness intensity,NGI)与花青素含量呈极显著相关,其中NGI的相关系数大于NRI。当叶片正反面色彩贡献比为2∶1时,NGI与花青素含量的相关性最大,相关系数为0.8532。对比不同模型发现,以NGI为自变量建立的指数模型拟合效果最好,相关系数为0.8381,决定系数(R^(2))达0.7550。在CIELab色彩空间中,红度(a^(*))与花青素含量的相关性最好,且相关系数同样在叶片正反面色彩贡献比为2∶1时达最大,为0.7356。基于a^(*)建立的幂模型拟合效果最好,相关系数和R^(2)分别为0.7438和0.6798。分别使用NGI模型和a^(*)模型对叶片花青素含量进行估测,验证后发现a^(*)模型的预测效果更好,准确性和稳定性更高,因此以a^(*)模型为预测紫苏叶片花青素含量的最优模型。
文摘马铃薯植株钾含量(Plant potassium content,PKC)是监测马铃薯营养状况的重要指标,快速准确地获取马铃薯植株钾含量对田间施肥和生产管理具有指导意义。基于无人机遥感平台搭载RGB传感器分别获取马铃薯块茎形成期、块茎增长期和淀粉积累期的RGB影像,并实测马铃薯植株钾含量。首先利用各个生育期的RGB影像提取每个小区冠层平均光谱和纹理特征。然后分别基于冠层光谱和纹理特征构建植被指数和纹理指数(NDTI、RTI和DTI),并与实测PKC进行相关性分析。最后利用多元线性回归(Multiple linear regression,MLR)、偏最小二乘(Partial least squares regression,PLSR)和人工神经网络(Artificial neural networks,ANN)构建马铃薯PKC估算模型。结果表明:各生育期NDTI、RTI和DTI与马铃薯PKC相关性均高于单一纹理特征,植被指数结合纹理指数均能提高模型的可靠性和稳定性,MLR和PLSR构建的估算模型精度均优于ANN。本研究可为马铃薯PKC监测提供科学参考。
