论文部分内容阅读
叶绿素作为绿色植物光合作用的必要组成成分,其含量的高低可反映作物的长势状况。实时监测植物叶片叶绿素含量的动态变化是监测植物长势的重要环节。以山西省闻喜县冬小麦为研究对象,基于高光谱技术和实测数据,对研究区冬小麦拔节期的叶绿素含量进行定量估算,并在此基础上利用卫星遥感数据对冬小麦的叶绿素含量进行反演,以达到仅应用卫星遥感数据估测叶绿素含量的目的。结果表明,水旱地冬小麦叶绿素含量敏感波段在可见光区域不同,在近红外区域一致;水旱地分别以DVI和NDVI为变量所构建的预测模型效果最佳,R2值均达到0.9以上,均方根误差分别为0.470 0和0.458 7;对叶绿素含量反演值与实测值对比分析,水地反演值与实测值大致吻合,而旱地反演值则偏高;采用均方根误差(RMSE)法,检验反演值和实际值的符合度,水地RMSE为0.926,旱地RMSE为1.540。
As an essential component of photosynthesis of green plants, chlorophyll content can reflect the status of crops. Real-time monitoring of plant leaf chlorophyll content of the dynamic changes in the monitoring of plant growth is an important part. Taking winter wheat in Wenxi County of Shanxi Province as the research object, the chlorophyll content of winter wheat at jointing stage in the study area was quantitatively estimated based on hyperspectral data and measured data. On the basis of this, the satellite remote sensing data was used to retrieve the chlorophyll content of winter wheat. In order to achieve the purpose of using only satellite remote sensing data to estimate chlorophyll content. The results showed that the sensitive bands of winter wheat chlorophyll content in water and dryland were different in the visible light region and the same in the near infrared region. The prediction models constructed by using DVI and NDVI as the variables respectively had the best effect, with R2 values above 0.9 and root mean square error Respectively 0.470 0 and 0.458 7. The contrasted values of chlorophyll content and measured data show that the inversion values of water are in good agreement with the measured values, while those of dry land are higher. The root mean square error (RMSE) The coincidence of the inversion value and the actual value was tested. The RMSE of water was 0.926 and the RMSE of dry land was 1.540.