欢迎访问作物学报，今天是
作物学报

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)

RichHTML

17

PDF

858

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
[1] Ladha J K, Pathak H P, Krupnik T J, Six J, Kessel C V . Efficiency of fertilizer nitrogen in cereal production: retrospect and prospect. Adv Agron, 2005,87:85-156.
doi: 10.1016/S0065-2113(05)87003-8
[2] Hatfield J L, Gitelson A A, Schepers J S, Walthall C L . Application of spectral remote sensing for agronomic decisions. Agron J, 2008,100:117-131.
[3] Lemaire G, Jeuffroy M H, Gastal F . Diagnosis tool for plant and crop N status in vegetative stage. Eur J Agron, 2008,28:614-624.
doi: 10.1016/j.eja.2008.01.005
[4] Mistele B, Schmidhalter U . Estimating the nitrogen nutrition index using spectral canopy reflectance measurements. Eur J Agron, 2008,29:184-190.
doi: 10.1016/j.eja.2008.05.007
[5] Plénet D, Lemaire G . Relationships between dynamics of nitrogen uptake and dry matter accumulation in maize crops. Determination of critical N concentration. Plant Soil, 2000,216:65-82.
[6] 李卫国, 赵春江, 王纪华, 刘良云 . 遥感和生长模型相结合的小麦长势监测研究现状与展望. 国土资源遥感, 2007, (2):6-9.
Li W G, Zhao C J, Wang J H, Liu L Y . Research situation and prospects of wheat condition monitoring based on growth model and remote sensing. Remote Sens Land Resour, 2007, (2):6-9 (in Chinese with English abstract).
[7] Hansen P M, Schjoerring J K . Reflectance measurement of canopy biomass and nitrogen status in wheat crops using normalized difference vegetation indices and partial least squares regression. Remote Sens Environ, 2003,86:542-553.
doi: 10.1016/S0034-4257(03)00131-7
[8] Huang J L, He F, Cui K H, Buresh R J, Xu B, Gong W H, Peng S B . Determination of optimal nitrogen rate for rice varieties using a chlorophyll meter. Field Crops Res, 2008,105:70-80.
doi: 10.1016/j.fcr.2007.07.006
[9] Justes E, Mary B, Jean-Marc M, Machet J M, Huché-Thélier L . Determination of a critical nitrogen dilution curve for winter wheat crops. Ann Bot(London), 1994,74:397-407.
[10] Zhao B, Ata-Ul-Karim S T, Liu Z D, Ning D F, Xiao J F, Liu Z G, Qin A Z, Nan J Q, Duan A W . Development of a critical nitrogen dilution curve based on leaf dry matter for summer maize. Field Crops Res, 2017,208:60-68.
doi: 10.1016/j.fcr.2017.03.010
[11] He Z Y, Qiu X L, Ata-Ul-Karim S T, Li Y D, Liu X J, Cao Q, Zhu Y, Cao W X, Tang L . Development of a critical nitrogen dilution curve of double cropping rice in south China. Front Plant Sci, 2017,8:1-14.
[12] Chen P F, Haboudane D, Tremblay N, Wang J H, Vigneault P, Li B G . New spectral indicator assessing the efficiency of crop nitrogen treatment in corn and wheat. Remote Sens Environ, 2010,114:1987-1997.
doi: 10.1016/j.rse.2010.04.006
[13] Li F, Mistele B, Hu Y C, Chen X P, Schmidhalter U . Comparing hyperspectral index optimization algorithms to estimate aerial N uptake using multi-temporal winter wheat datasets from contrasting climatic and geographic zones in China and Germany. Agric Forest Meteorol, 2013,180:44-57.
doi: 10.1016/j.agrformet.2013.05.003
[14] Feng W, Guo B B, Zhang H Y, He L, Zhang Y S, Wang Y H, Zhu Y J, Guo T C . Remote estimation of above ground nitrogen uptake during vegetative growth in winter wheat using hyperspectral red-edge ratio data. Field Crops Res, 2015,180:197-206.
doi: 10.1016/j.fcr.2015.05.020
[15] Gitelson A A, Kaufman Y J, Stark R, Rundquist D . Novel algorithms for remote estimation of vegetation fraction. Remote Sens Environ, 2002,80:76-87.
doi: 10.1016/S0034-4257(01)00289-9
[16] 贺佳, 刘冰峰, 郭燕, 王来刚, 郑国清, 李军 . 冬小麦生物量高光谱遥感监测模型研究. 植物营养与肥料学报, 2017,23:313-323.
He J, Liu B F, Guo Y, Wang L G, Zheng G Q, Li J . Biomass estimation model of winter wheat (Triticum aestivum L.) using hyperspectral reflectances. J Plant Nutr Fert, 2017,23:313-323 (in Chinese with English abstract).
[17] Mistele B, Schmidhalter U . Tractor-based quadrilateral spectral reflectance measurements to detect biomass and total aerial nitrogen in winter wheat. Agron J, 2010,102:499-506.
doi: 10.2134/agronj2009.0282
[18] Li F, Mistele B, Hu Y C, Chen X P, Schmidhalter U . Optimising three-band spectral indices to assess aerial N concentration, N uptake and aboveground biomass of winter wheat remotely in China and Germany. ISPRS J Photogramm, 2014,92:112-123.
doi: 10.1016/j.isprsjprs.2014.03.006
[19] 马波, 田军仓 . 作物生长模拟模型研究综述. 节水灌溉, 2010, (2):1-5.
Ma B, Tian J C . A review on crop growth simulation model research. Water Sav Irrig, 2010, (2):1-5 (in Chinese with English abstract).
[20] Jones J W, Hoogenboom G, Porter C H, Boote K J, Batchelor W D, Hunt L A, Wilkens P W, Singh U, Gijsman A J, Ritchie J T . The DSSAT cropping system model. Eur J Agron, 2003,18:235-265.
doi: 10.1016/S1161-0301(02)00107-7
[21] Mccown R L, Hammer G L, Hargreaves J N G, Holzworth D P, Freebairn D M . APSIM: a novel software system for model development, model testing and simulation in agricultural systems research. Agric Syst, 1996,50:255-271.
doi: 10.1016/0308-521X(94)00055-V
[22] 王康, 沈荣开, 王富庆 . 冬小麦生长及根系吸氮的动态模拟研究. 灌溉排水学报, 2002,21(1):6-10.
Wang K, Shen R K, Wang F Q . Dynamic simulation research of winter wheat growth and root nitrogen uptake. J Irrig Drain, 2002,21(1):6-10 (in Chinese with English abstract).
[23] 曹静, 刘小军, 汤亮, 曹卫星, 朱艳 . 稻麦适宜氮素营养指标动态的模型设计. 应用生态学报, 2010,21:359-364.
Cao J, Liu X J, Tang L, Cao W X, Zhu Y . Model designing for suitable nitrogen index dynamics of rice and wheat. Chin J Appl Ecol, 2010,21:359-364 (in Chinese with English abstract).
[24] 庄东英, 李卫国, 武立权 . 冬小麦生物量卫星遥感估测研究. 干旱区资源与环境, 2013,27(10):158-162.
Zhuang D Y, Li W G, Wu L Q . Estimating winter wheat biomass based on satellite remote sensing. J Arid Land Resour Environ, 2013,27(10):158-162 (in Chinese with English abstract).
[25] 帅细强, 王石立, 马玉平, 李迎春 . 基于水稻生长模型的气象影响评价和产量动态预测. 应用气象学报, 2008,19:71-81.
Shuai X Q, Wang S L, Ma Y P, Li Y C . Assessment of meteorologic condition effects and dynamic yield forecasting based on rice growth model. J Appl Meteorol Sci, 2008,19:71-81 (in Chinese with English abstract).
[26] Dorigo W A, Zurita-Milla R, de Wit A J W, Brazile J, Singh R, Schaepman M E . A review on reflective remote sensing and data assimilation techniques for enhanced agroecosystem modeling. Int J Appl Earth Obs, 2007,9:165-193.
doi: 10.1016/j.jag.2006.05.003
[27] Zheng B J, Myint S W, Thenkabail P S, Aggarwal R M . A support vector machine to identify irrigated crop types using time-series Landsat NDVI data. Int J Appl Earth Obs, 2015,34:103-112.
doi: 10.1016/j.jag.2014.07.002
[28] Shao Y, Ren J, Campbell J B . 9.04-Multitemporal remote sensing data analysis for agricultural application. Compreh Remote Sens, 2018,9:29-38.
[29] Fischer A . A model for the seasonal variations of vegetation indices in coarse resolution data and its inversion to extract crop parameters. Remote Sens Environ, 1994,48:220-230.
doi: 10.1016/0034-4257(94)90143-0
[30] Skakun S, Franch B, Vermote E, Roger J C, Beckerreshef I, Justice C, Kussul N . Early season large-area winter crop mapping using MODIS NDVI data, growing degree days information and a Gaussian mixture model. Remote Sens Environ, 2017,195:244-258.
doi: 10.1016/j.rse.2017.04.026
[31] Franch B, Vermote E F, Becker-Reshef I, Claverie M, Huang J, Zhang J, Justice C, Sobrino J A . Improving the timeliness of winter wheat production forecast in the United States of America, Ukraine and China using MODIS data and NCAR Growing Degree Day information. Remote Sens Environ, 2015,161:131-148.
doi: 10.1016/j.rse.2015.02.014
[32] Zhang X Y, Friedl M A, Schaaf C B, Strahler A H, Hodges J C F, Gao F, Reed B C, Huete A . Monitoring vegetation phenology using MODIS. Remote Sens Environ, 2003,84:471-475.
doi: 10.1016/S0034-4257(02)00135-9
[33] Zheng H B, Cheng T, Yao X, Deng X Q, Tian Y C, Cao W X, Zhu Y . Detection of rice phenology through time series analysis of ground-based spectral index data. Field Crops Res, 2016,198:131-139.
doi: 10.1016/j.fcr.2016.08.027
[34] Russelle M P, Wilhelm W W, Olson R A, Power J F . Growth analysis based on degree days. Crop Sci, 1984,24:28-32.
doi: 10.2135/cropsci1984.0011183X002400010007x
[35] McMaster G S, Smika D E . Estimation and evaluation of winter wheat phenology in the central Great Plains. Agric Forest Meteorol, 1988,43:1-18.
doi: 10.1016/0168-1923(88)90002-0
[36] Gitelson A A, Viña A, Ciganda A, Rundquist D C, Arkebauer T J . Remote estimation of canopy chlorophyll content in crops. Geophys Res Lett, 2005,32:1-4.
[37] Dash J, Curran P J . The MERIS terrestrial chlorophyll index. Int J Remote Sens, 2004,25:5403-5413.
doi: 10.1080/0143116042000274015
[38] Huete A R . A soil-adjusted vegetation index (SAVI). Remote Sens Environ, 1988,25:295-309.
doi: 10.1016/0034-4257(88)90106-X
[39] Zarco-Tejada P J, Miller J R, Noland T L, Mohammed G H . Scaling-up and model inversion methods with narrowband optical indices for chlorophyll content estimation in closed forest canopies with hyperspectral data. IEEE T Geosci Remote, 2001,39:1491-1507.
doi: 10.1109/36.934080
[40] Rouse J W, Haas R H, Schell J A, Deering D W. Monitoring the vernal advancement and retrogradation (green wave effect) of natural vegetation. In: NASA/GSFC, Type III Final Report, Greenbelt, MD, USA, 1974. pp 371-373.
[41] Fitzgerald G J, Rodriguez D, Christensen L K, Belford R, Sadras V O, Clarke T R . Spectral and thermal sensing for nitrogen and water status in rainfed and irrigated wheat environments. Precis Agric, 2006,7:233-248.
doi: 10.1007/s11119-006-9011-z
[42] Pearson R L, Miller D L. Remote mapping of standing crop biomass for estimation of the productivity of the short-grass prairie. In: Proceedings of the Eighth International Symposium on Remote Sensing of Environment, Vol. 2, 1972. pp 1357-1381.
[43] Gitelson A, Merzlyak M N . Spectral reflectance changes associated with autumn senescence of Aesculus hippocastanum L. and Acer platanoides L. leaves. Spectral features and relation to chlorophyll estimation. J Plant Physiol, 1994,143:286-292.
[44] Guyot G, Baret F . Utilisation de la haute resolution spectrale pour suivre L’etat des couverts vegetaux. Spec Signat Object Remote, 1988,287:279.
[45] Jiang Z, Huete A R, Didan K, Miura T . Development of a two-band enhanced vegetation index without a blue band. Remote Sens Environ, 2008,112:3833-3845.
doi: 10.1016/j.rse.2008.06.006
[46] Horler D N H, Dockray M, Barber J . The red edge of plant leaf reflectance. Int J Remote Sens, 1983,4:273-288.
doi: 10.1080/01431168308948546
[47] Herrmann I, Pimstein A, Karnieli A, Cohen Y, Alchanatis V, Bonfil D J . LAI assessment of wheat and potato crops by VENμS and Sentinel-2 bands. Remote Sens Environ, 2011,115:2141-2151.
doi: 10.1016/j.rse.2011.04.018
[48] Hunt Jr E R, Doraiswamy P C, McMurtrey J E, Daughtry C S, Perry E M, Akhmedov B . A visible band index for remote sensing leaf chlorophyll content at the canopy scale. Int J Appl Earth Obs, 2013,21:103-112.
doi: 10.1016/j.jag.2012.07.020
[49] 唐延林, 王人潮, 黄敬峰, 孔维姝, 程乾 . 不同供氮水平下水稻高光谱及其红边特征研究. 遥感学报, 2004,8:185-192.
Tang Y L, Wang R C, Huang J F, Kong W S, Cheng Q . Hyperspectral data and their relationships correlative to the pigment contents for rice under different nitrogen support level. J Remote Sens, 2004,8:185-192 (in Chinese with English abstract).
[50] Tian Y C, Yao X, Yang J, Cao W X, Hannaway D B, Zhu Y . Assessing newly developed and published vegetation indices for estimating rice leaf nitrogen concentration with ground- and space-based hyperspectral reflectance. Field Crops Res, 2011,120:299-310.
doi: 10.1016/j.fcr.2010.11.002
[51] Huete A, Didan K, Miura T, Rodriguez E P, Gao X, Ferreira L G . Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sens Environ, 2002,83:195-213.
doi: 10.1016/S0034-4257(02)00096-2
[52] Li F, Miao Y X, Feng G H, Yuan F, Yue S C, Gao X W, Liu Y Q, Liu B, Ustin S L, Chen X P . Improving estimation of summer maize nitrogen status with red edge-based spectral vegetation indices. Field Crops Res, 2014,157:111-123.
doi: 10.1016/j.fcr.2013.12.018
[53] Cho M A, Skidmore A, Corsi F, Wieren S E V, Sobhan I . Estimation of green grass/herb biomass from airborne hyperspectral imagery using spectral indices and partial least squares regression. Int J Appl Earth Obs, 2007,9:414-424.
doi: 10.1016/j.jag.2007.02.001
[54] 宋开山, 张柏, 李方, 段洪涛, 王宗明 . 高光谱反射率与大豆叶面积及地上鲜生物量的相关分析. 农业工程学报, 2005,21(1):38-42.
Song K S, Zhang B, Li F, Duan H T, Wang Z M . Correlative analyses of hyperspectral reflectance, soybean LAI and aboveground biomass. Trans CSAE, 2005,21(1):38-42 (in Chinese with English abstract).
[55] 王秀珍, 黄敬峰, 李云梅, 王人潮 . 水稻地上鲜生物量的高光谱遥感估算模型研究. 作物学报, 2003,29:16-22.
Wang X Z, Huang J F, Li Y M, Wang R C . Study on hyperspectral remote sensing estimation models for the ground fresh biomass of rice. Acta Agron Sin, 2003,29:16-22 (in Chinese with English abstract).
[56] 焦险峰, 杨邦杰, 裴志远, 王飞 . 基于植被指数的作物产量监测方法研究. 农业工程学报, 2005,21(4):104-108.
Jiao X F, Yang B J, Pei Z Y, Wang F . Monitoring crop yield using NOAA/AVHRR-based vegetation indices. Trans CSAE, 2005,21(4):104-108 (in Chinese with English abstract).
[57] 王延颐 . 植被指数与水稻长势及产量结构要素关系的研究. 国土资源遥感, 1996, (1):56-59.
Wang Y Y . The relationship between vegetation index and rice growth and rice yield components. Remote Sens Land Resour, 1996, (1):56-59 (in Chinese with English abstract).
[58] Dempewolf J, Adusei B, Becker-Reshef I, Hansen M, Potapov P, Khan A, Barker B . Wheat yield forecasting for Punjab Province from vegetation index time series and historic crop statistics. Remote Sens, 2014,6:9653-9675.
doi: 10.3390/rs6109653
[59] Mkhabela M S, Bullock P, Raj S, Wang S, Yang Y . Crop yield forecasting on the Canadian Prairies using MODIS NDVI data. Agric Forest Meteorol, 2011,151:385-393.
doi: 10.1016/j.agrformet.2010.11.012
[60] Ren J Q, Chen Z X, Zhou Q B, Tang H J . Regional yield estimation for winter wheat with MODIS-NDVI data in Shandong, China. Int J Appl Earth Obs, 2008,10:403-413.
doi: 10.1016/j.jag.2007.11.003
[61] 黄楠, 王强, 王鹏, 张国庆, 刘凌菲 . 应用归一化植被指数的玉米产量预测研究. 黑龙江农业科学, 2014, (2):130-132.
Huang N, Wang Q, Wang P, Zhang G Q, Liu L F . Maize yield prediction research based on the NDVI. Heilongjiang Agric Sci, 2014, (2):130-132 (in Chinese with English abstract).
[62] Wardlow B D, Egbert S L . Large-area crop mapping using time-series MODIS 250 m NDVI data: an assessment for the U.S. Central Great Plains. Remote Sens Environ, 2008,112:1096-1116.
doi: 10.1016/j.rse.2007.07.019
[63] Chu L, Liu Q S, Huang C, Liu G H . Monitoring of winter wheat distribution and phenological phases based on MODIS time- series: a case study in the Yellow River Delta, China. J Integr Agric, 2016,15:2403-2416.
doi: 10.1016/S2095-3119(15)61319-3
[64] 冯伟, 朱艳, 田永超, 姚霞, 郭天财, 曹卫星 . 基于高光谱遥感的小麦籽粒产量预测模型研究. 麦类作物学报, 2007,27:1076-1084.
Feng W, Zhu Y, Tian Y C, Yao X, Guo T C, Cao W X . Model for predicting grain yield with canopy hyperspectal remote sensing in wheat. J Triticeae Crop, 2007,27:1076-1084 (in Chinese with English abstract).
[65] 唐延林, 黄敬峰, 王人潮, 王福民 . 水稻遥感估产模拟模式比较. 农业工程学报, 2004,20(1):166-171.
Tang Y L, Huang J F, Wang R C, Wang F M . Comparsion of yield estimation simulated models of rice by remote sensing. Trans CSAE, 2004,20(1):166-171 (in Chinese with English abstract).
[66] Johnson D M . An assessment of pre- and within-season remotely sensed variables for forecasting corn and soybean yields in the United States. Remote Sens Environ, 2014,141:116-128.
doi: 10.1016/j.rse.2013.10.027
[67] Magney T S, Eitel J U H, Huggins D R, Vierling L A . Proximal NDVI derived phenology improves in-season predictions of wheat quantity and quality. Agric Forest Meteorol, 2016,217:46-60.
doi: 10.1016/j.agrformet.2015.11.009
[68] Ma H Y, Huang J X, Zhu D H, Liu J M, Su W, Zhang C, Fan J L . Estimating regional winter wheat yield by assimilation of time series of HJ-1 CCD NDVI into WOFOST-ACRM model with Ensemble Kalman Filter. Math Comput Model, 2013,58:759-770.
doi: 10.1016/j.mcm.2012.12.028
[69] Fang H L, Liang S L, Hoogenboom G . Integration of MODIS LAI and vegetation index products with the CSM-CERES-Maize model for corn yield estimation. Int J Remote Sens, 2011,32:1039-1065.
doi: 10.1080/01431160903505310
[70] 王绍中, 季书勤, 张德奇, 李秀民, 吕平安 . 河南省小麦栽培技术的演变与发展. 河南农业科学, 2007,36(10):19-26.
doi: 10.3969/j.issn.1004-3268.2007.10.005
Wang S Z, Ji S Q, Zhang D Q, Li X M, Lyu P A . Evolution and development of wheat cultivation techniques in Henan Province. J Henan Agric Sci, 2007,36(10):19-26.
doi: 10.3969/j.issn.1004-3268.2007.10.005
[71] 王永华, 郭天财, 朱云集, 王晨阳, 康国章 . 河南省不同类型麦区小麦丰产高效栽培技术规程. 河南农业科学, 2006,35(5):14-18.
Wang Y H, Guo T C, Zhu Y J, Wang C Y, Kang G Z . Regulations for high yield and high efficiency cultivation of wheat in different wheat regions of Henan province. J Henan Agric Sci, 2006,35(5):14-18 (in Chinese with English abstract).
[72] Duveiller G, Lopez-Lozano R, Cescatti A . Exploiting the multi-angularity of the MODIS temporal signal to identify spatially homogeneous vegetation cover: a demonstration for agricultural monitoring applications. Remote Sens Environ, 2015,166:61-77.
doi: 10.1016/j.rse.2015.06.001
[1] 胡川, 赵凯男, 黄修利, 吴金芝, 任开明, 王贺正, 付国占, 黄明, 李友军. 一次灌溉下耕作方式和氮肥用量对旱地小麦产量和品质的影响[J]. 作物学报, 2026, 52(6): 1830-1846.
[2] 马胜乾, 王志平, 陈浩天, 窦淑贤, 张燕, 邓艾兴, 张卫建, 原向阳, 宋振伟. 秸秆还田下耕作方式与氮肥施用量对东北玉米产量及土壤团聚体的影响[J]. 作物学报, 2026, 52(6): 1802-1816.
[3] 张思思, 赵向辉, 周洋, 姚云凤, 朱荣昱, 董元杰, 胡国庆, 徐通, 刘兆新. 冬闲期翻耕和绿肥还田对连作花生田土壤理化性质和产量的影响[J]. 作物学报, 2026, 52(5): 1472-1486.
[4] 张宁宁, 滕雨菲, 任娜娜, 魏兴卓, 闫书豪, 樊可心, 王永宏, 陈文康, 张兴华, 朱万超, 徐淑兔, 薛吉全. 201份玉米自交系抗旱表型评价及可塑性分析[J]. 作物学报, 2026, 52(5): 1309-1325.
[5] 张振, 冯连杰, 石玉, 于振文, 张永丽. 节水补灌下不同穗型小麦产量形成差异研究[J]. 作物学报, 2026, 52(5): 1522-1535.
[6] 闫安, 蒋昆炜, 王蓉圆, 田林, 张璐, 王韵, 徐建龙. 水稻剑叶小维管束数基因SVN7的鉴定与克隆[J]. 作物学报, 2026, 52(5): 1364-1372.
[7] 刘昕萌, 任昊, 张继波, 张吉旺, 赵斌, 任佰朝, 刘鹏, 王洪章. 茉莉酸甲酯(MeJA)缓解高温影响玉米雌穗分化的生理机制[J]. 作物学报, 2026, 52(5): 1561-1572.
[8] 王壮壮, 武紫君, 张永新, 张芯源, 袁丽雪, 陈如雪, 刘世举, 段剑钊, 冯伟, 王同朝, 王永华. 豫东南黏壤潮土区水氮优化协同提高冬小麦产量和氮素利用效率[J]. 作物学报, 2026, 52(5): 1501-1521.
[9] 张鸿蓉, 王菲儿, 李盼, 仇海龙, 朱静, 赵连豪, 南运有, 何蔚, 樊志龙, 胡发龙, 柴强, 殷文. 减量20%灌水与25%有机肥替代化肥提高青贮玉米产量的光合特性[J]. 作物学报, 2026, 52(5): 1487-1500.
[10] 王宇诚, 张露, 刘阿康, 黄见良, 彭少兵, 袁珅. 基于产量差的作物大面积单产提升策略与展望[J]. 作物学报, 2026, 52(5): 1279-1290.
[11] 赵佳雪, 周龙昊, 郭岂源, 尚伦霄, 王涵, 刘志涛, 陈曦, 张晓佩, 宋宪亮, 毛丽丽. 长期秸秆还田与深松通过改善土壤环境与棉花光合特性提高滨海盐碱地棉花产量[J]. 作物学报, 2026, 52(5): 1548-1560.
[12] 郭星宇, 胡丹, 林苏期, 王梦凯, 谭文峰, 黄传琴. 生物炭配施化肥提高玉米‖大豆下玉米产量和土壤生态系统多功能性[J]. 作物学报, 2026, 52(5): 1536-1547.
[13] 蔡宏玮, 于爱忠, 姜科强, 王鹏飞, 王玉珑, 霍建喆, 庞小能, 尹波, 尚永盼. 干旱灌区有机肥替代部分化肥促进甜玉米产量提升的关键机制[J]. 作物学报, 2026, 52(4): 1166-1180.
[14] 崔雪梅, 柳妍娣, 刘景辉, 米俊珍, 武俊英, 赵宝平. 不同基因型燕麦强弱势粒生理特性与产量关系研究[J]. 作物学报, 2026, 52(4): 1220-1235.
[15] 杨锐, 陈敬东, 黄郢, 张学昆, 周登文, 刘清云, 徐劲松, 谢伶俐, 许本波. 长江下游冬油菜区应对气候变化的育种和栽培策略研究[J]. 作物学报, 2026, 52(4): 1153-1165.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
京ICP备15008789号-2