冬小麦生物量及氮积累量的植被指数动态模型研究

doi:10.3724/SP.J.1006.2019.81084

Abstract

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 (CI_red-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, CI_red-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

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.

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References 72

[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

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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]
CI_red-edge	(R_840-870)/(R_720-730)-1	Gitelson et al. (2005)^[36]
CI_green	(R_840-870)/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]

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^***
CI_red-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^***

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
	CI_red-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
	CI_red-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
	CI_red-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
	CI_red-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

Dynamic model of vegetation indices for biomass and nitrogen accumulation in winter wheat

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