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作物学报 ›› 2019, Vol. 45 ›› Issue (8): 1238-1249.doi: 10.3724/SP.J.1006.2019.81084

• 耕作栽培·生理生化 • 上一篇    下一篇

冬小麦生物量及氮积累量的植被指数动态模型研究

吴亚鹏,贺利,王洋洋,刘北城,王永华,郭天财,冯伟()   

  1. 河南农业大学农学院/国家小麦工程技术研究中心, 河南郑州 450046
  • 收稿日期:2018-11-25 接受日期:2019-04-15 出版日期:2019-08-12 网络出版日期:2019-07-16
  • 通讯作者: 冯伟
  • 作者简介:E-mail: wyp18237183802@163.com
  • 基金资助:
    本研究由“十三五”国家重点研发计划项目(2016YFD0300604);国家自然科学基金项目(31671624);国家现代农业(小麦)产业技术体系建设专项资助(CARS-03-01-22)

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()   

  1. College of Agronomy/National Engineering Research Center for Wheat, Henan Agricultural University, Zhengzhou 450046, Henan, China
  • Received:2018-11-25 Accepted:2019-04-15 Published:2019-08-12 Published online:2019-07-16
  • Contact: Wei FENG
  • Supported by:
    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)

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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350~700 nm和750~900 nm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(R 2 = 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

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.

Key words: winter wheat, hyperspectral remote sensing, vegetation indices, yield, dynamic models

表1

优选植被指数的计算方法和参考文献"

植被指数
Vegetation index		 计算公式
Calculation formula		 参考文献
Reference
NDVI (810, 680) (R810-R680)/(R810+R680) Rouse et al. (1974)[40]
NDVI (810, 560) (R810-R560)/(R810+R560) Rouse et al. (1974)[40]
NDRE (R790-R720)/(R790+R720) Fitzgerald et al. (2006)[41]
RVI (810, 560) R810/R560 Pearson et al. (1972)[42]
SR705 R750/R705 Gitelson and Merzlyak (1994)[43]
CIred-edge (R840-870)/(R720-730)-1 Gitelson et al. (2005)[36]
CIgreen (R840-870)/R550-1 Gitelson et al. (2005)[36]
MTCI (R754-R709)/(R709-R681) Dash and Curran (2004)[37]
OTVI 0.5×[204×(R776-R754) -22×(R754-R550)] Li et al. (2013)[13]
SAVI (825, 735) (1-0.08)×(R825-R735)/(R825+R735-0.08) Huete (1988)[38]
NDSI (R788-R756)/(R788+R756) Li et al. (2013)[13]
VOG3 (R734-R747)/(R715+R720) Zarco-Tejada et al. (2001)[39]
mRER (R759-1.8×R419)/(R742-1.8×R419) Feng et al. (2015)[14]
REP 700+40×[(R670+R780)/2-R700]/(R740-R700) Guyot and Baret (1988)[44]
EVI2 2.5×(R800-R660)/(1+R800+2.4×R660) Jiang et al. (2008)[45]

图1

不同水氮处理下的冠层光谱变化 W0: 不灌溉; W1: 拔节期灌溉一次; W2: 拔节期和开花期各灌溉一次。"

图2

与小麦氮积累量和生物量关系较好的植被指数间比较(n = 400) AGNU: 地上部氮积累量; AGDW: 地上部生物量; 其他缩写同表1。"

图3

小麦氮积累量和生物量与植被指数之间的定量关系(n = 400) "

图4

不同水氮处理下植被指数的动态变化 AGDD: 累积生长度日; 其他缩写同表1和图1。"

表2

不同时期植被指数与小麦产量间线性决定系数(n = 50) "

植被指数
Vegetation index		 越冬期
Wintering		 返青期
Regreening		 拔节期
Jointing		 孕穗期
Booting		 抽穗期
Heading		 开花期
Anthesis		 灌浆前期
Initial-filling		 灌浆中期
Mid-filling		 灌浆后期
Late-filling
mRER 0.005 0.454*** 0.817*** 0.815*** 0.862*** 0.800*** 0.781*** 0.669*** 0.358***
CIred-edge 0.328*** 0.604*** 0.830*** 0.825*** 0.845*** 0.829*** 0.836*** 0.681*** 0.360***
NDSI 0.022 0.211*** 0.681*** 0.813*** 0.871*** 0.807*** 0.827*** 0.739*** 0.592***
SAVI (825, 735) 0.325*** 0.649*** 0.825*** 0.833*** 0.876*** 0.847*** 0.867*** 0.736*** 0.341***

图5

不同产量水平下植被指数的动态模型"

表3

不同产量水平下植被指数的双Logistic模型参数"

产量水平
Yield level		 植被指数
Vegetation index		 y0 a1 a2 t1
(℃ d)		 t2
(℃ d)		 b1
(℃ d)		 b2
(℃ d)		 R2 RMSE
低产
Low yield		 mRER 1.018 0.103 0.073 497.99 1560.38 207.55 266.55 0.608 0.024
CIred-edge 0.105 0.541 0.570 650.37 1818.35 218.81 258.45 0.736 0.110
NDSI 0.015 0.010 0.010 856.32 1817.99 211.89 242.12 0.637 0.003
SAVI (825, 735) 0.067 0.078 0.107 762.94 1870.95 156.52 327.55 0.700 0.023
中产
Medium yield		 mRER 1.029 0.269 0.249 766.34 1854.98 142.29 148.59 0.788 0.051
CIred-edge 0.165 1.401 1.580 792.61 1881.39 124.23 169.07 0.822 0.293
NDSI 0.015 0.032 0.036 911.92 1973.14 134.91 185.07 0.735 0.008
SAVI (825, 735) 0.064 0.223 0.250 797.02 1940.42 149.19 191.40 0.862 0.036
高产
High yield		 mRER 1.026 0.354 0.356 778.57 1905.52 143.95 161.59 0.878 0.048
CIred-edge 0.175 2.003 2.324 814.39 1908.69 136.04 192.63 0.891 0.268
NDSI 0.016 0.044 0.051 919.84 2024.15 119.12 186.65 0.827 0.007
SAVI (825, 735) 0.074 0.265 0.328 811.25 1995.09 124.05 183.68 0.917 0.034
超高产
Super high yield		 mRER 1.021 0.415 0.430 815.03 1900.98 160.16 167.94 0.957 0.037
CIred-edge 0.186 2.246 2.493 815.88 1890.37 144.05 170.35 0.954 0.202
NDSI 0.017 0.048 0.055 917.39 1996.33 90.17 160.00 0.882 0.006
SAVI (825, 735) 0.092 0.270 0.327 809.89 1950.72 86.97 153.58 0.950 0.023
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