首页> 外文期刊>Intelligent automation and soft computing >ESTIMATION OF LEAF AREA INDEX BY USING MULTI-ANGULAR HYPERSPECTRAL IMAGING DATA BASED ON THE TWO-LAYER CANOPY REFLECTANCE MODEL
【24h】

ESTIMATION OF LEAF AREA INDEX BY USING MULTI-ANGULAR HYPERSPECTRAL IMAGING DATA BASED ON THE TWO-LAYER CANOPY REFLECTANCE MODEL

机译:基于两层树冠反射模型的多角度超光谱成像数据估算叶面积指数

获取原文
获取原文并翻译 | 示例

摘要

This study aims to investigate the effects of observation angle on the estimation of leaf area index (LAI) by using multi-angular hyperspectral imaging data. First, the bidirectional reflectance was simulated with a two-layer canopy reflectance model (ACRM), the obvious bell-shaped and bowl-shaped pattern can be found in the blue, red and NIR wavebands. Subsequently, the three most commonly used vegetation indexes, the normalized difference vegetation index (NDVI), the simple ratio index (SRI) and enhanced vegetation index (EVI) were used to exploit the effect of different observation angles. Through the analysis of simulated data, SRI and EVI displayed a greater potential for estimating LAI due to the fact that they are more sensitive to the variation of observation angle, thus the partial least square regression (PLS) based on the cross validation was applied both to the single observation angle and to various combinations of multiple observation angles. The result shows that SRI has obtained the highest estimation accuracy (R2 = 0.47, RMSE = 0.30) by the combination of six observation angles, which agreed well with the simulated result, indicating that multi-angular observation can improve the estimation accuracy of LAI.
机译:本研究旨在通过多角度高光谱成像数据研究观察角对叶面积指数(LAI)估计的影响。首先,使用两层冠层反射模型(ACRM)模拟双向反射,可以在蓝色,红色和NIR波段中找到明显的钟形和碗形图案。随后,使用了三种最常用的植被指数:归一化差异植被指数(NDVI),简单比率指数(SRI)和增强植被指数(EVI)来利用不同观测角度的影响。通过对模拟数据的分析,由于SRI和EVI对观察角的变化更为敏感,因此显示出更大的估计LAI的潜力,因此基于交叉验证的偏最小二乘回归(PLS)均被应用单个观察角和多个观察角的各种组合。结果表明,SRI通过六个观测角的组合获得了最高的估计精度(R2 = 0.47,RMSE = 0.30),与模拟结果吻合较好,表明多角度观测可以提高LAI的估计精度。

著录项

  • 来源
    《Intelligent automation and soft computing》 |2013年第3期|295-304|共10页
  • 作者单位

    Beijing Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing, 100097, China,Zhejiang University, College of Environment and Natural Resources, Hangzhou, 310058, China;

    Beijing Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing, 100097, China;

    Beijing Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing, 100097, China;

    Department of Geography, University of Lethbridge, T1K3M4, Canada;

    Beijing Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing, 100097, China;

    Beijing Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing, 100097, China,Anhui University, Key Laboratory of Intelligent Computing & Signal Processing, Ministry of Education, Hefei, 230039, China;

    Department of Geography, University of Lethbridge, T1K3M4, Canada;

  • 收录信息
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Leaf Area Index; Multi-Angular Hyperspectral Image; Canopy Reflectance Model; Vegetation Index; Partial Least Square Regression;

    机译:叶面积指数;多角度高光谱图像;顶篷反射率模型;植被指数;偏最小二乘回归;

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号