基于HJ-CCD数据和随机森林算法的小麦叶面积指数反演

为给小麦长势的遥感监测提供技术支持,该文运用随机森林回归（RF,random forest）算法建立小麦叶面积指数（LAI）遥感反演模型。首先基于2010-2013年江苏地区小麦环境减灾卫星HJ-CCD的影像数据,提取拔节、孕穗和开花3个生育期的卫星植被指数,进而根据各生育期植被指数和相应实测LAI数据,利用RF算法构建各期小麦LAI反演模型,并以人工神经网络（ANN,artificial neural network）模型为参比模型进行预测精度的比较。结果表明：RF算法模型在3个生育期的预测结果均好于同期的ANN模型。拔节、孕穗和开花3个生育期RF模型预测值与地面实测值的R2分别为0.79,...

Full description

Saved in:
Bibliographic Details
Published in农业工程学报 Vol. 32; no. 3; pp. 149 - 154
Main Author 王丽爱 周旭东 朱新开 郭文善
Format Journal Article
LanguageChinese
Published 扬州大学江苏省作物遗传生理重点实验室,扬州,225009%扬州大学信息工程学院,扬州,225127 2016
Subjects
叶面积指数
小麦
机器学习
植被
神经网络
算法
随机森林
algorithms
neural networks
vegetation
leaf area index
神经网络
random forest
算法
machine-learning
植被
机器学习
随机森林
wheat
叶面积指数
小麦
Online AccessGet full text
ISSN1002-6819
DOI10.11975/j.issn.1002-6819.2016.03.021

Cover

More Information
Summary:为给小麦长势的遥感监测提供技术支持,该文运用随机森林回归（RF,random forest）算法建立小麦叶面积指数（LAI）遥感反演模型。首先基于2010-2013年江苏地区小麦环境减灾卫星HJ-CCD的影像数据,提取拔节、孕穗和开花3个生育期的卫星植被指数,进而根据各生育期植被指数和相应实测LAI数据,利用RF算法构建各期小麦LAI反演模型,并以人工神经网络（ANN,artificial neural network）模型为参比模型进行预测精度的比较。结果表明：RF算法模型在3个生育期的预测结果均好于同期的ANN模型。拔节、孕穗和开花3个生育期RF模型预测值与地面实测值的R2分别为0.79,0.67和0.59,对应的RMSE分别为0.57,0.90和0.78;ANN模型的R2分别为0.67,0.31和0.30,对应的RMSE分别为0.82,1.94和1.43。该研究结果为提高大田尺度下的小麦LAI遥感预测精度提供了技术和方法。
Bibliography:11-2047/S
Wang Liai, Zhou Xudong, Zhu Xinkai, Guo Wenshan （1. Key Laboratory of Crop Genetics and Physiology of Jiangsu Province, Yangzhou University, Yangzhou 225009, China; 2. Information Engineering College of Yangzhou University, Yangzhou 225127, China）
vegetation; neural networks; algorithms; random forest; machine-learning; leaf area index; wheat
The leaf area index （LAI） of crops is an important parameter for crop monitoring. With the remote sensing application in agriculture, inverting LAI of crops fromremote sensing data has been studied. Among these studies, vegetation indices are widely used because they can reduce effect background noise on the spectral reflectance of plant canopies. In addition to using vegetation indices, modeling algorithm also plays an important role in improving the remote estimation accuracy of crop LAI. Recently, the emerging Random Forest （RF） machine-learning algorithm is regarded as one of the most precise prediction methods for regression. In this paper, we conducted studi
ISSN:1002-6819
DOI:10.11975/j.issn.1002-6819.2016.03.021