河北省中部平原玉米长势遥感综合监测

doi:10.18402/resci.2018.10.17

摘要/Abstract

摘要：

为了提高玉米的遥感长势监测的准确度,以河北省中部平原地区为研究区域,以MODIS遥感数据反演的条件植被温度指数（Vegetation Temperature Condition Index, VTCI）与叶面积指数（Leaf Area Index, LAI）作为玉米长势监测指标,通过灰色关联度分析法确定玉米各生育时期（出苗—拔节期、拔节—抽雄期、抽雄—灌浆期与灌浆—成熟期）VTCI与LAI作为相应生育时期长势监测指标的权重值,以及4个生育时期的玉米长势在总体长势与产量形成中的权重值,并基于权重结果分别构建玉米在4个生育时期与主要生育期的长势综合监测指标,进而评估研究区域2011—2016年间的玉米长势。结果表明,各生育时期VTCI作为长势监测指标的权重值均大于LAI,且以拔节—抽雄期最大,抽雄—灌浆期次之,说明玉米各生育时期的长势与最终产量较易受到水分胁迫的影响,并以拔节—抽雄期与抽雄—灌浆期对水分胁迫最为敏感;而玉米长势综合监测指标在4个生育时期的权重值较为接近,并以灌浆—成熟期略大,说明该时期的玉米长势与最终产量之间的关系较为密切。研究区域5市的县域尺度玉米长势综合监测指标与单产之间的决定系数（R²）介于0.247~0.598之间,均达到了极显著水平（P<0.001）,优于单一的VTCI或LAI指标,表明基于长势综合监测指标的玉米长势监测结果准确度较高。研究年份间该区域的玉米长势以2011年的长势最好,2014年与2015年长势最差,且西部长势优于东部。

关键词: 灰色关联度分析, 综合监测, 叶面积指数, 玉米长势, 条件植被温度指数, 河北省中部

Abstract:

In order to improve maize growth monitoring accuracy, the central plain of Hebei Province (PR China) was chosen as the study area. The gray correlation analysis method was implemented to calculate the weight coefficients of vegetation temperature condition index (VTCI) and leaf area index (LAI) at four maize growth stages (emergence-jointing, jointing-booting, booting-filling, and filling-mature), as well as the weight coefficients of growth at the 4 stages during maize growth and yield formation. Thus, the integrated growth monitoring indices (G) at the 4 stages and main growth stage could be derived. Linear regression models between maize yields and Gs of counties in the years from 2011 to 2015 were established to analyze the precision of maize growth monitoring. The results showed that the weight coefficients of VTCI at the 4 stages were greater than those of LAI, and the joint-booting stage was the highest, the booting-filling stage was followed. The results further indicated that maize growth and yield formation were likely impacted by water stress, especially at the joint-booting and the booting-filling stage. The weight coefficients of maize growth at the 4 stages were similar. It is noted that growth at the filling-mature stage was the highest. It illustrated that maize growth at the filling-mature stage was most closely related to maize production. Coefficients of determination (R²) of the linear regression analysis between Gs and maize yields of counties in 5 cities all passed the significant test at 0. 01 level, and R²values were greater than those between maize yields and VTCIs or LAIs. The findings of current study demonstrated a high accuracy of the gs derived from gray correlation analysis method. Based on the maize growth monitoring data, this study implies that the best year was 2011, the worst year were 2014 and 2015, and the maize growth in the western plain was better than the eastern part.

Key words: gray correlation analysis, integrated monitoring, leaf area index, maize growth, vegetation temperature condition index, the central plain of Hebei Province

王蕾, 王鹏新, 李俐, 荀兰, 孔庆玲. 河北省中部平原玉米长势遥感综合监测[J]. 资源科学, 2018, 40(10): 2099-2109.

Lei WANG, Pengxin WANG, Li LI, Lan XUN, Qingling KONG. Integrated maize growth monitoring based on gray correlation analysis and remote sense data in the central plain of Hebei Province[J]. Resources Science, 2018, 40(10): 2099-2109.

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产量年景等级	全国单产	全国总产	分省总产	分省单产
丰年	P_y >7	P_y >11	P_y>14	P_y > 11
偏丰年	3< P_y ≤7	4< P_y ≤11	5<P_y≤14	4< P_y ≤11
持平略增	0< P_y ≤3	0< P_y ≤ 4	0<P_y ≤5	0< P_y ≤4
持平略减	-3≤ P_y ≤0	-4 ≤ P_y ≤0	-5≤P_y ≤0	-4≤ P_y ≤0
偏歉年	-7≤ P_y <-3	-11 ≤ P_y <-4	-14≤P_y <-5	-11≤ P_y <-4
歉年	P_y <-7	P_y <-11	P_y <-14	P_y <-11

		长势综合监测指数（G）				分级标准
		均值	标准差	置信下限	置信上限	分级标准
长势等级	差	0.427	0.044	-	0.440	G<0.418
	偏差	0.432	0.043	0.418	0.447	0.418≤G<0.435
	持平稍差	0.450	0.038	0.435	0.466	0.435≤G<0.453
	持平稍好	0.465	0.032	0.453	0.476	0.453≤G<0.480
	偏好	0.491	0.041	0.480	0.501	0.480≤G<0.503
	好	0.515	0.037	0.503	-	G≥0.503

市	回归模型	决定系数（R²）	P值
保定	Y_vtci₁=4754.2X_vtci₁+4205.1	0.147	=0.002
	Y_lai₁=12014X_lai₁+3185.9	0.281	<0.001
	Y_G₁=9803.7X_G₁+2503.1	0.300	<0.001
石家庄	Y_vtci₂=2726.6X_vtci₂+2726.6	0.206	<0.001
	Y_lai₂=5344.1X_lai₂+5344.1	0.195	=0.001
	Y_G₂=4977.3X_G₂+4977.3	0.247	<0.001
沧州	Y_vtci₃=5428.1X_vtci₃+2031.8	0.210	<0.001
	Y_lai₃=21524X_lai₃+130.9	0.688	<0.001
	Y_G₃=14445X_vtci₃-937.1	0.341	<0.001
衡水	Y_vtci₄=4434.5X_vtci₄+3341.3	0.389	<0.001
	Y_lai₄=7264.2X_lai₄+3876.1	0.304	<0.001
	Y_G₄=6735.9X_G₄+2923.7	0.450	<0.001
廊坊	Y_vtci₅=6815.3X_vtci₅+2036.4	0.616	<0.001
	Y_lai₅=14315X_lai₅+2431.9	0.487	<0.001
	Y_G₅=10842X_G₅+1381.5	0.598	<0.001