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冬小麦生物量及氮积累量的植被指数动态模型研究
引用本文:吴亚鹏,贺利,王洋洋,刘北城,王永华,郭天财,冯伟. 冬小麦生物量及氮积累量的植被指数动态模型研究[J]. 作物学报, 2019, 45(8): 1238-1249. DOI: 10.3724/SP.J.1006.2019.81084
作者姓名:吴亚鹏  贺利  王洋洋  刘北城  王永华  郭天财  冯伟
作者单位:河南农业大学农学院/国家小麦工程技术研究中心
基金项目:This study was supported by grants from the “Thirteenth Five-year Plan” of National Key Research Project of China(2016YFD0300604);the National Natural Science Foundation of China(31671624);the China Agricultural Research System(CARS-03-01-22)
摘    要:利用遥感技术实时监测小麦生长状况,依据监测结果适时促控,可提高产量。本研究以高产小麦品种周麦27为试验材料,在不同试验地点设置了水氮耦合的大田试验,筛选出了适宜监测冬小麦地上部氮积累量和生物量的植被指数,并构建了不同产量水平下优选植被指数的动态模型。结果表明,(1)不同的水氮耦合模式显著影响小麦冠层光谱变化,在350~700nm和750~900nm表现相反的反应特征;(2)对2个农学生长指标反应敏感且兼容性好的植被指数主要有修正型红边比率(mRER)、土壤调整植被指数[SAVI(825,735)]、红边叶绿素指数(CIred-edge)和归一化差异光谱指数(NDSI),其与产量间相关性较好的时期为拔节至灌浆中期;(3)双Logistic模型可以很好地拟合植被指数的动态变化,高产和超高产水平下拟合精度较高(R^2>0.82),而低产水平下相对较低(R2=0.608~0.736)。比较而言,CIred-edge和SAVI(825,735)用于评价小麦长势较为适宜。研究结果对作物因地定产、以苗管理、分类促控具有重要意义。

关 键 词:冬小麦  高光谱遥感  植被指数  产量  动态模型
收稿时间:2018-11-25

Dynamic model of vegetation indices for biomass and nitrogen accumulation in winter wheat
WU Ya-Peng,HE Li,WANG Yang-Yang,LIU Bei-Cheng,WANG Yong-Hua,GUO Tian-Cai,FENG Wei. Dynamic model of vegetation indices for biomass and nitrogen accumulation in winter wheat[J]. Acta Agronomica Sinica, 2019, 45(8): 1238-1249. DOI: 10.3724/SP.J.1006.2019.81084
Authors:WU Ya-Peng  HE Li  WANG Yang-Yang  LIU Bei-Cheng  WANG Yong-Hua  GUO Tian-Cai  FENG Wei
Affiliation:College of Agronomy/National Engineering Research Center for Wheat, Henan Agricultural University, Zhengzhou 450046, Henan, China
Abstract:Using remote sensing technology to monitor and timely promote and control wheat growth in real time may improve the yield. In this study, the water-nitrogen coupling test was set up at different locations using a high yield cultivar Zhoumai 27. The suitable vegetation indices for monitoring above ground nitrogen uptake and biomass of winter wheat were selected and the dynamic models with preferred vegetation indices at different yield levels were established. The results showed that (1) different water-nitrogen coupling patterns significantly affected the canopy spectral changes of wheat, with the opposite characteristics at 350-700 nm and 750-900 nm; (2) The modified red-edge ratio (mRER), soil-adjusted vegetation index [SAVI (825, 735)], red edge chlorophyll index (CIred-edge) and normalized difference spectral index (NDSI) were the main vegetation indices sensitive to the two agronomic growth indices and with a good compatibility, and the stages well correlated with yield were from jointing to mid-filling; (3) the double Logistic model could fit the dynamic changes of vegetation index very well, and the fitting accuracy was higher at high and super high yield levels (R 2 > 0.825), but lower at low yield level (R 2 = 0.608-0.736). In comparison, CIred-edge and SAVI (825, 735) were more suitable for evaluating wheat growth. The results of this study are of great significance for evaluating crop yield faced on growing situation in the field, seedling management, and promoting or controlling plant growth according to classification in wheat production.
Keywords:winter wheat  hyperspectral remote sensing  vegetation indices  yield  dynamic models  
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