地球信息科学学报 ›› 2015, Vol. 17 ›› Issue (10): 1243-1248.doi: 10.3724/SP.J.1047.2015.01243

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玉米叶面积指数的CHRIS/PROBA数据反演分析

乔海浪1,2(), 李旺1,2, 牛铮1,*()   

  1. 1. 中国科学院遥感与数字地球研究所 遥感科学国家重点实验室,北京 100101
    2. 中国科学院大学资源与环境学院,北京 100049
  • 收稿日期:2014-11-17 修回日期:2015-01-08 出版日期:2015-10-10 发布日期:2015-10-10
  • 通讯作者: 牛铮 E-mail:qiaohailang123@163.com;niuzheng@radi.ac.cn
  • 作者简介:

    作者简介:乔海浪(1990-),男,博士生,研究方向为植被定量遥感。E-mail: qiaohailang123@163.com

  • 基金资助:
    国家高技术研究发展计划项目(“863”)“先进环境监测技术设备——星-机-地生态环境质量遥感监测系统集成与示范”(2014AA06A511);云南省科技计划项目(省院省校科技合作专项)(2010AD004)“基于高分辨率卫星影像与无人机影像的昆明新国际机场遥感监测”;高分辨率国家科技重大专项

Estimating Leaf Area Index of Maize Based on Multi-angular CHRIS/PROBA Data

QIAO Hailang1,2(), LI Wang1,2, NIU Zheng1,*()   

  1. 1.The State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China
    2.College of resources and environment, University of Chinese Academy of Sciences,Beijing 100049, China
  • Received:2014-11-17 Revised:2015-01-08 Online:2015-10-10 Published:2015-10-10
  • Contact: NIU Zheng E-mail:qiaohailang123@163.com;niuzheng@radi.ac.cn
  • About author:

    *The author: CHEN Nan, E-mail:fjcn99@163.com

摘要:

叶面积指数(LAI)是衡量植被生态状况和估算作物产量的一个重要指标。LAI的反演是定量遥感研究的重要内容。传统的经验统计反演方法基于单一观测角度的遥感数据进行,忽略了地物反射率的方向性。若在反演中加入多观测角度的信息,则有可能提升LAI反演的精度。以2008年甘肃省张掖市玉米实验区为研究区,利用欧空局的CHRIS/PROBA多角度高光谱数据对比分析了传统植被指数NDVI、RVI、EVI的变化规律及其反演玉米叶面积指数LAI的精度,并根据NDVI随观测角度的变化规律,构造出新型多角度归一化植被指数MNDVI,分别对实测叶面积指数进行线性回归并利用实测数据对估算LAI进行精度验证,结果表明:新型MNDVI指数相比于传统NDVI、RVI、EVI对LAI的反演精度有了显著提升,估算模型决定系数R2达到0.716,精度验证均方根误差为0.127,平均减小了33.3%。

关键词: 植被指数, 叶面积指数, 多角度, 高光谱数据

Abstract:

Leaf area index is an important parameter for evaluating vegetation ecological conditions and estimating crop yields. Thus, the estimation of LAI has always been a hotspot of quantitative remote sensing research. A growing number of studies have focused on estimating the leaf area index (LAI) of vegetation using several traditional vegetation indices(the Normalized Difference Vegetation index (NDVI), the Ratio Vegetation Index (RVI), and the Enhanced Vegetation Index (EVI)). These vegetation indices were all based on the data of single view zenith angle, which limited the accuracy of LAI estimation. In this article, we compared the sensitivity of the three vegetation indices for crop canopies, and then put forward a new vegetation index named Multi-angle Normalized Difference Vegetation Index (MNDVI) based on CHRIS/PROBA data which includes information with respect to five different view zenith angles. Using the ground crop LAI data obtained in Zhangye city from Gansu Province in June 2008, this paper compared the estimation models of LAI based on the four vegetation indices including the three traditional indices (NDVI, RVI and EVI) and MNDVI. The result shows that: compared with the traditional ones, MNDVI has a much better correlation with LAI, and the correlation coefficient R2 of the LAI calculation model reaches up to 0.716. Besides, in order to verify the accuracy of LAI retrieval model based onMNDVI, this paper calculated the RMSE between the estimated LAI using MNDVI model and the ground-measured LAI, finding that the RMSE was 0.127, which was averagely 33.3% lower comparing with methods using traditional vegetation indices.

Key words: vegetation Index, leaf area index, multi-angle hyperspectral data