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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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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