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中国农学通报

中国农学通报 ›› 2015, Vol. 31 ›› Issue (6): 80-87.doi: 10.11924/j.issn.1000-6850.2014-2384

所属专题： 玉米

• 农学 农业基础科学 • 上一篇    下一篇

基于MODIS-NDVI的春玉米叶面积指数和地上生物量估算

刘 明1,冯 锐2,纪瑞鹏2,武晋雯2,王宏博2,于文颖2   

  1. (1抚顺市气象局,辽宁抚顺 113006；2中国气象局沈阳大气环境研究所,沈阳 110166）
  • 收稿日期:2014-09-01 修回日期:2014-09-24 接受日期:2014-11-07 出版日期:2015-03-20 发布日期:2015-03-20
  • 通讯作者: 于文颖
  • 基金资助:
    中央级公益性科研院所基本科研业务费专项“水分胁迫及复水对玉米光合及叶绿素荧光的影响”(2013IAE-CMA09)；公益性行业（气象）科研专项“遥感技术在作物生长模式及农业气象预报中的应用研究”(GYHY201106027)；辽宁省科技厅重大农业攻关项目“主要农业气象灾害发生规律及预警和评估机制研究”(2011210002)；辽宁省气象局2014年度科研课题“不同干旱胁迫程度对玉米生理生态指标的定量影响评价”(201404)。

Estimation of Leaf Area Index and Aboveground Biomass of Spring Maize by MODIS-NDVI

Liu Ming1, Feng Rui2, Ji Ruipeng2, Wu Jinwen2, Wang Hongbo2, Yu Wenying2   

  1. (1Fushun Meteorological Service, Fushun Liaoning 113006;2Institute of Atmospheric Environment, China Meteorological Administration, Shenyang 110016)
  • Received:2014-09-01 Revised:2014-09-24 Accepted:2014-11-07 Online:2015-03-20 Published:2015-03-20

摘要： 为了构建春玉米叶面积指数和地上生物量的估算模型，基于辽宁省大田条件下9个站点春玉米不同生育期叶面积指数LAI和地上鲜生物量数据，利用MODIS提取的10种植被指数NDVI、RVI、DVI、PVI、EVI、GNDVI、RDVI、SAVI、OSAVI和NLI反演春玉米LAI和地上鲜生物量。结果表明：10种植被指数与春玉米LAI和地上鲜生物量相关性显著，采用植被指数反演春玉米LAI和地上鲜生物量是可行的；分别基于回归分析法和人工神经网络法，利用10种植被指数反演春玉米LAI和地上鲜生物量，利用回归分析法结合OSAVI和NDVI反演春玉米LAI和地上鲜生物量效果较好；利用人工神经网络法进行模拟反演采用RVI、DVI和EVI效果最佳，反演精度高于回归分析法。

关键词: 出水腐肉, 出水腐肉

Abstract: In order to construct the model of the leaf area index and aboveground biomass of spring maize, the leaf area index and aboveground biomass of spring maize were measured in the field experiments at different growth stage in 9 sites. In order to estimate the LAI and aboveground biomass, ten spectral vegetation indices (NDVI, RVI, DVI, PVI, EVI, GNDVI, RDVI, SAVI, OSAVI and NLI) were extracted from MODIS data. The results showed that the correlation between ten vegetation index and LAI, aboveground biomass was significant, using vegetation index to inverse LAI and aboveground biomass was feasible. Based on regression analysis method and artificial neural network method, LAI and aboveground biomass of spring maize were inversed by using ten vegetation indexes. With regression analysis method and OSAVI, NDVI, the inversion results were better. With artificial neural network method and RVI, DVI, EVI, the inversion accuracy was higher than that of regression analysis method.