森林工程2017，Vol.33Issue(2)：28-32,5.
三种叶绿素含量遥感估算模型比较
Comparisons of Three Remote sensing Models forEstimating Chlorophgll Content
摘要
Abstract
Chlorophyll is an important participant in the photosynthetic process.Chlorophyll has been used to study and evaluate the forest productivity and forest health and is the basis of study forest ecosystem carbon sequestration ability.Therefore,it is very important to accurately estimate the chlorophyll content.Multiple linear regression models,neural network and support vector machine methods were used to establish the chlorophyll content estimating model based on the measured leaf chlorophyll content and corresponding reflectance spectrum in this study.The advantages and disadvantages of those models was compared and analyzed to provide a theoretical basis for estimating chlorophyll content based on remote sensing technology.The result showed that estimating model established using Erf-BP neural network method was the best one with the accuracy of up to 94.46%,the root mean square error(RMSE) of which was 3.321μg/cm2.The model established using support vector machine method was better with the accuracy of 88.74%,RMSE of which was 5.705μg/cm2.The model established using multiple stepwise regression method was restively poor with the accuracy and RMSE of 92.41% and 13.354μg/cm2 respectively.Erf-BP neural network was concluded to be the best method with high fitting precision and good stability by the comparison.关键词
叶绿素含量/多元统计回归/神经网络/支持向量机Key words
chlorophyll content/multiple linear regression/neural network/support vector machine分类
农业科技引用本文复制引用
王亚卿,于颖..三种叶绿素含量遥感估算模型比较[J].森林工程,2017,33(2):28-32,5.基金项目
国家自然科学基金项目(31500518,31500519) （31500518,31500519）
