为构建叶绿素密度的估算模型，通过设置不同施氮量梯度的棉花盆栽试验，在盛花期对棉花叶片进行光谱测试获取反射率数据，同时采集叶片样本测定叶绿素密度，通过反射率数据构建光谱指数，优选与叶绿素密度相关性大的光谱指数，利用随机森林算法(radom forest，RF)和偏最小二乘法(partial least squares regression，PLSR)构建棉花叶片的叶绿素密度估算模型并进行精度验证。结果表明，RF与PLSR 2种估算模型的R2分别为0。723 5和0。704 7，RPD分别为1。851 4和1。456 9，表明2个模型均具有粗略的预测能力，其中，RF模型的精度要高于PLSR模型，故利用高光谱技术对盛花期棉花叶绿素密度进行无损快速估测是可行的，研究结果为棉花群体长势状况的监测提供技术支撑。
Estimation of Chlorophyll Density of Cotton Leaves Based on Spectral Index
To construct the estimation model of chlorophyll density,cotton pot experiments with different nitrogen applications were set up to obtain reflectance data by spectral testing of cotton leaves at peak flowering stage.At the same time,leaf samples were collected to determine chlorophyll density.The spectral index was constructed by reflectance data,and the spectral index with high correlation with chlorophyll density was selected.The random forest(RF)algorithm and partial least squares regression(PLSR)were used to construct the estimation model of chlorophyll density in cotton leaves and verify the accuracy.The results showed that the R2 of RF and PLSR estimation models were 0.723 5 and 0.704 7,respectively,and the RPD were 1.851 4 and 1.456 9,respectively,indicating that both models had rough prediction ability.The accuracy of RF model was higher than that of PLSR model,so it was feasible to use hyperspectral technology to estimate the chlorophyll density of cotton at peak flowering stage,which provided technical support for monitoring cotton population growth.
cottonchlorophyll densityspectral characteristics of cottonhyperspectral estimation modeling
国家科技期刊平台
NSTL
万方数据
