Research On The Inversion Methods Of Wheat Leaf Area Index Based On Unmanned Aerial Vehicle Remote Sensing

Retrieving wheat leaf area index by using unmanned aerial vehicle remote sensing was estimated crop leaf area index quickly.This paper describes the inversion models of leaf area index.It included the following steps.First,obtained the pictures of the study area by using six wing unmanned aerial vehicle equipment with ADC Lite,meanwhile mosaicking and preprocessing the multispectral images.Second,extracting the vegetation indexs from the multispectral images and extracting leaf area index from LAI-2200,meanwhile choosing samples to model and validate.Finally,built the models of the retrieving leaf area index by using vegetation indexs and leaf area index.the models were empirical models,back propagation neural network models and support vector machine model.And models were evaluated the accuracy of prediction results,meanwhile determining the best model of retrieving leaf area index.The main content and conclusions of the paper were as follows.(1)The multispectral images by using the unmanned aerial vehicle remote sensing system,were mosaicked in the Pix4 D mapper software.(2)The empirical models of retrieving leaf area index were built by using vegetation indexs and leaf area index.And models were evaluated the accuracy of prediction results by using the determination coefficient and the root of mean square error.The results show that the linear model of normalized difference vegetation index and leaf area index was best,and the determination coefficient was 0.657.The linear and logarithm model of soil-adjusted vegetation index and leaf area inex were next.The linear model of ratio vegetation Index and leaf area index was followed.(3)Built the six back propagation neural network models of vegetation indexs and leaf area index.Five vegetation indexs and the combination of five vegetation indexs inputted in the back propagation neural network.And models were evaluated the accuracy of prediction results by using the determination coefficient and the root of mean square error.The results show that the combination of five vegetation indexs as the input was the best back propagation neural network model,and the determination coefficient was 0.78,the root of mean square error was 0.567.(4)The support vector machine model of vegetation indexs and leaf area index was built,which the combination of five vegetation indexs as the input and leaf area index as output.Prediction results were better by using the support vector machine model,and the determination coefficient was 0.828,the root of mean square error was 0.411.(5)The accuracy of prediction were evaluated by using the better models.The better models were the linear model of normalized difference vegetation index and leaf area index,the linear model and logarithm model of soil-adjusted vegetation index and leaf area inex,The linear model of ratio vegetation Index and leaf area index,the back propagation neural network model of vegetation indexs and leaf area index,the support vector machine model of vegetation indexs and leaf area index.The results show that back propagation neural network model precision is 85.62%,and support vector machine model precision was 89.74%.The back propagation neural network model precision improved 2.5% than the best empirical model of vegetation index and leaf area index,but the support vector machine model precision improved 4.12% than the back propagation neural network model.Hence,the support vector machine model of vegetation indexs and leaf area index was the best inversion model.The four multispectral images were inverted by using the support vector machine model.