基于冠层时序植被指数的冬小麦单产预测

doi:10.3864/j.issn.0578-1752.2020.18.005

摘要/Abstract

摘要：

【目的】研究冬小麦冠层时序植被指数的动态变化规律并基于其构建单产预测模型,为田间实时、准确获取作物单产信息提供有效的技术手段。【方法】本研究于2017—2019年在江苏省兴化市万亩粮食产业园开展不同品种及氮肥水平的田间小区试验,利用主动传感器RapidSCAN CS-45获取冠层归一化红边植被指数（normalized difference red edge,NDRE）和归一化植被指数（normalized difference vegetation index,NDVI）,基于双Logistic函数拟合时序植被指数并提取曲线特征参数,进而分析各特征参数与单产的相关关系,并以独立试验数据对单产预测模型进行验证。【结果】NDRE在孕穗期和抽穗期与单产关系最好,R²达到0.84以上;通过多元逐步线性回归法发现,利用2个或多个时期NDRE预测单产的效果较单生育时期有所提高,且第一和第二被选择的时期分别为拔节期和孕穗期。基于全生育时期相对NDRE（relative NDRE,RNDRE）和相对NDVI（relative NDVI,RNDVI）构建时序曲线,并利用曲线特征参数建立单产预测模型,其中RNDRE和RNDVI的最大值、累积值及增长速率与单产关系较好。利用独立试验数据对上述单产预测模型进行检验,结果表明基于RNDRE时序曲线最大值和累积值所构建的单产模型验证效果较好,R²大于0.80,相对均方根误差和相对误差均小于10%,其验证效果优于单时期或多时期基于NDRE的预测模型,且优于基于NDVI构建的单产模型。【结论】基于冠层时序植被指数提取的特征参数RNDRE最大值和累积RNDRE具有良好估测单产的潜力,研究结果为田间进行实时、准确预测冬小麦单产提供了技术支持。

关键词: 冬小麦, 冠层传感器, 时序植被指数, 单产, 预测模型

Abstract:

【Objective】The research elaborated the dynamic change trend of canopy time series vegetation index in winter wheat. The yield prediction model was constructed based on time series vegetation index in wheat, which provided effective technical support for obtaining crop yield information timely and accurately. 【Method】 From 2017 to 2019, the field experiments involving different nitrogen (N) rates and varieties were conducted in Ten Thousand Acres Grain Industrial Park located in Xinghua, Jiangsu province. The normalized difference red edge (NDRE) and normalized difference vegetation index (NDVI) were obtained from the active canopy sensor RapidSCAN CS-45. The curve of time-series vegetation index was fitted based on the double logistic function, and the characteristic parameters of curve were extracted. The correlation between each characteristic parameters and yield were analyzed. The yield estimation models were verified with independent test data. 【Result】 The results of the study indicated that the relationship between NDRE and yield was performed well at booting and heading stage, and R² of them was 0.86 and 0.85, respectively. The results of multiple stepwise linear regression showed that the yield prediction models could be improved using NDRE of two or more growth stages, compared with using single growth stage information. The first and second selected periods were jointing and booting stage, respectively. Based on the relative NDRE (RNDRE) and relative NDVI (RNDVI) of the whole growth period, the time-series curve was constructed and the yield prediction models were developed using the characteristic parameters of the curve. The maximum value, accumulative value and growth rate of RNDRE and RNDVI time series curve had a good relationship with the yield. The yield prediction models based on the maximum and accumulative values of RNDRE performed satisfactorily with validation using independent data, the R² was greater than 0.80, and the relative root mean square error and relative error were less than 10%. The validation effect was better than NDRE-based prediction model with the single-period or multi-period, which was better than NDVI-based yield predicted model. 【Conclusion】 The maximum and the accumulative RNDRE extracted from the canopy time series vegetation index had a good potential to estimate the yield, which provided technical support for real-time and accurate yield prediction in the field.

Key words: winter wheat, canopy sensor, time series vegetation index, yield, prediction model

项方林,李鑫格,马吉锋,刘小军,田永超,朱艳,曹卫星,曹强. 基于冠层时序植被指数的冬小麦单产预测[J]. 中国农业科学, 2020, 53(18): 3679-3692.

XIANG FangLin,LI XinGe,MA JiFeng,LIU XiaoJun,TIAN YongChao,ZHU Yan,CAO WeiXing,CAO Qiang. Using Canopy Time-Series Vegetation Index to Predict Yield of Winter Wheat[J]. Scientia Agricultura Sinica, 2020, 53(18): 3679-3692.

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植被指数 Vegetation index	品种 Variety	样本 Sample	拔节期 Jointing stage	孕穗期 Booting stage	抽穗期 Heading stage	开花期 Flowering stage	灌浆期 Filling stage
NDRE	镇麦12 Zhenmai 12	30	0.66	0.89	0.88	0.86	0.89
	扬麦23 Yangmai 23	30	0.46	0.83	0.82	0.75	0.84
	宁麦13 Ningmai 13	30	0.72	0.92	0.92	0.91	0.91
	所有品种 All	90	0.60	0.86	0.85	0.81	0.81
NDVI	镇麦12 Zhenmai 12	30	0.62	0.79	0.81	0.84	0.83
	扬麦23 Yangmai 23	30	0.54	0.79	0.75	0.77	0.75
	宁麦13 Ningmai 13	30	0.81	0.86	0.81	0.84	0.85
	所有品种 All	90	0.63	0.79	0.77	0.80	0.76

植被指数 Vegetation index	回归方程 Regression equation	决定系数 R²	相对均方根误差 RRMSE (%)	相对误差 RE (%)	赤池信息准则 AIC
NDRE	y= -2.16x₁+16.83x₂+9.15x₃-4.40x₄-2.35x₅-0.75	0.86	8.98	9.82	-97.95
	y= -2.85x₁+17.69x₂+5.67x₃-3.23x₅-0.90	0.86	8.99	9.84	-99.74
	y= -2.54x₁+21.94x₂-2.14x₅-0.81	0.86	9.02	9.82	-101.37
	y= -2.53x₁+19.90x₂-0.75	0.86	9.02	9.84	-103.01
NDVI	y=0.14x₁+11.23x₂-13.22x₃+23.12x₄-3.57x₅-8.31	0.81	10.41	11.70	-71.45
	y=11.25x₂-13.36x₃+23.27x₄-3.38x₅-8.36	0.81	10.40	11.76	-73.44
	y=12.21x₂-14.79x₃+19.45x₄-7.45	0.81	10.43	11.62	-74.96

植被指数 Vegetation index	氮处理 N treatment	曲线参数 Parameters of curves						决定系数 R²	均方根误差 RMSE
植被指数 Vegetation index	氮处理 N treatment	A₁	A₂	a	b	c	d	决定系数 R²	均方根误差 RMSE
RNDRE	N0	0.655	0.506	7.10	0.21	11.84	0.84	0.78	0.066
	N1	0.876	0.714	8.97	0.27	14.25	0.85	0.91	0.063
	N2	0.969	0.791	11.42	0.28	15.04	0.88	0.96	0.047
	N3	0.988	0.786	12.74	0.28	15.51	0.90	0.97	0.043
	N4	0.995	0.788	12.70	0.28	15.10	0.90	0.97	0.043
RNDVI	N0	0.837	0.668	5.85	0.18	14.29	0.86	0.79	0.082
	N1	0.956	0.785	8.80	0.23	15.70	0.88	0.91	0.062
	N2	0.987	0.776	11.59	0.23	16.60	0.92	0.94	0.053
	N3	0.997	0.760	12.29	0.24	16.22	0.93	0.96	0.044
	N4	0.996	0.754	12.46	0.24	15.84	0.93	0.95	0.045

氮处理 N treatment	RNDRE				RNDVI
氮处理 N treatment	最大值 Maximum	累积值 Accumulative value	增长速率 Increase rate	下降速率 Decrease rate	最大值 Maximum	累积值 Accumulative value	增长速率 Increase rate	下降速率 Decrease rate
N0	0.582	0.349	0.721	1.187	0.750	0.456	0.772	1.693
N1	0.809	0.464	1.245	1.841	0.911	0.558	1.186	2.091
N2	0.931	0.550	1.598	2.037	0.968	0.623	1.396	2.039
N3	0.963	0.580	1.731	2.028	0.981	0.637	1.452	1.983
N4	0.968	0.583	1.737	2.018	0.980	0.639	1.488	1.885

植被指数 Vegetation index	特征参数 Characteristic parameter	单产预测模型 Yield prediction model	R²	RRMSE (%)	RE (%)	AIC
RNDRE	最大值 Maximum	y = 8.72x - 1.37	0.80	10.2	12.9	62.29
	累积值 Accumulative value	y = 14.39x - 1.22	0.82	9.9	12.9	60.41
	增长速率 Increase rate	y = 3.32x + 1.38	0.82	9.9	12.8	60.35
	下降速率 Decrease rate	y = 3.73x - 0.75	0.73	12.0	14.3	71.98
RNDVI	最大值 Maximum	y = 14.24x - 7.03	0.77	11.0	13.4	66.48
	累积值 Accumulative value	y = 18.24x - 4.58	0.80	10.2	12.9	62.18
	增长速率 Increase rate	y = 4.82x - 0.02	0.81	10.2	12.8	61.84
	下降速率 Decrease rate	y = 5.04x - 3.72	0.22	20.3	25.6	103.41