Application research of GF-1/WFV data in estimation of maize leaf area index

doi:10.3969/j.issn.1004-1524.2021.05.12

Acta Agriculturae Zhejiangensis ›› 2021, Vol. 33 ›› Issue (5): 861-872.DOI: 10.3969/j.issn.1004-1524.2021.05.12

• Environmental Science • Previous Articles Next Articles

Application research of GF-1/WFV data in estimation of maize leaf area index

ZHANG Yuxun¹^,²(), WANG Lei¹^,²^,^*(), QU Xiangning¹^,², CAO Yuan¹^,², WU Mengyao¹^,², YU Ruixin¹^,², SUN Yuan³

1. Breeding Base for State Key Laboratory of Land Degradation and Ecosystem Restoration in Northwest China, Ningxia University, Yinchuan 750021, China
2. Key Laboratory for Restoration and Reconstruction of Degenerated Ecosystem in Northwest China under Ministry of Education, Ningxia University, Yinchuan 750021, China
3. Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China

Received:2020-09-25 Online:2021-05-25 Published:2021-05-25
Contact: WANG Lei

Abstract

Abstract:

The leaf area index (LAI), which plays a key role in response of the structural parameters of vegetation canopy, is closely related to the physiological process of the canopy. It is also one of the important parameters in the field of vegetation remote sensing. In the typical maize distribution area in Nong’an County of Changchun, combined with the measured data, the leaf area index and canopy spectrum were simultaneously observed on the ground. With the help of the normalized vegetation index (NDVI), ratio vegetation index (RVI), atmospheric regulation vegetation index (ARVI), soil adjustment vegetation index (SAVI), modified soil adjustment vegetation index (MSAVI).The ground measured spectrum data and GF-1/WFV spectrum data in estimating maize canopy leaf area index were compared and analyzed. After that, the optimal model was screened by parameters, such as coefficient of determination (R²), root-mean-square error (RMSE), relative error (RE) and residual prediction deviation (RPD). The results showed that the correlation between the different vegetation indexes and LAI of the ground-measured spectral data was higher than that of the GF-1/WFV spaceborne spectral data. In the comparison of the correlation between the different vegetation indexes and LAI, it was found that MSAVI had the highest correlation with LAI among the vegetation indexes of the ground spectrum and the satellite spectrum structure. In the estimation model based on ground spectral data and satellite spectral data, the function types corresponding to the highest R ² between MSAVI and LAI were different. For the measured spectrum, R ² was corresponded to the exponential function model, and for the satellite spectrum, it was corresponded to the quadratic function model.

Key words: GF-1, vegetation index, maize, leaf area index, estimation model

CLC Number:

S127
TP79

ZHANG Yuxun, WANG Lei, QU Xiangning, CAO Yuan, WU Mengyao, YU Ruixin, SUN Yuan. Application research of GF-1/WFV data in estimation of maize leaf area index[J]. Acta Agriculturae Zhejiangensis, 2021, 33(5): 861-872.

Figures/Tables 11

References 31

[1]	王枭轩, 孟庆岩, 张海香, 等. 基于粒子群神经网络模型反演玉米、小麦叶面积指数[J]. 浙江农业学报, 2019,31(7):1170-1176.
	WANG X X, MENG Q Y, ZHANG H X, et al. Inversion of maize and wheat leaf area index based on particle swarm optimization neural network model[J]. Acta Agriculturae Zhejiangensis, 2019,31(7):1170-1176.(in Chinese with English abstract)
[2]	高林, 王晓菲, 顾行发, 等. 植冠下土壤类型差异对遥感估算冬小麦叶面积指数的影响[J]. 植物生态学报, 2017,41(12):1273-1288. DOI
	GAO L, WANG X F, GU X F, et al. Exploring the influence of soil types underneath the canopy in winter wheat leaf area index remote estimating[J]. Chinese Journal of Plant Ecology, 2017,41(12):1273-1288.(in Chinese with English abstract)
[3]	叶舒, 范文义, 孟庆岩. 基于高分一号数据的PROSAIL模型叶面积指数反演[J]. 森林工程, 2016,32(4):18-21.
	YE S, FAN W Y, MENG Q Y. Leaf area index retrieval of GF-1 using PROSAIL[J]. Forest Engineering, 2016,32(4):18-21.(in Chinese with English abstract)
[4]	马磊, 闫浩文, 何毅, 等. 2001-2015年喜马拉雅南麓地区植被变化遥感监测[J]. 干旱区地理, 2017,40(2):405-414.
	MA L, YAN H W, HE Y, et al. Vegetation changes in south Himalayas areas based on remote sensing monitoring during 2001-2015[J]. Arid Land Geography, 2017,40(2):405-414.(in Chinese with English abstract)
[5]	刘珺, 庞鑫, 李彦荣, 等. 夏玉米叶面积指数遥感反演研究[J]. 农业机械学报, 2016,47(9):309-317.
	LIU J, PANG X, LI Y R, et al. Inversion study on leaf area index of summer maize using remote sensing[J]. Transactions of the Chinese Society for Agricultural Machinery, 2016,47(9):309-317.(in Chinese with English abstract)
[6]	蒋丽伟, 张家琦, 赵一臣, 等. 北京山区典型林分生长季叶面积指数动态变化[J]. 林业资源管理, 2019(2):132-136.
	JIANG L W, ZHANG J Q, ZHAO Y C, et al. Dynamic change of leaf area index in the growing season of typical forests in Beijing mountainous area[J]. Forest Resources Management, 2019(2):132-136.(in Chinese with English abstract)
[7]	倪衡, 李效顺, 鹿瑶, 等. 基于光谱特征的煤矿覆煤区遥感识别与监测方法研究: 以云南小龙潭矿区为例[J]. 生态与农村环境学报, 2019,35(1):9-15.
	NI H, LI X S, LU Y, et al. Research on remote sensing recognition and monitoring method of coal-covered area based on spectral characteristics: a case study of Xiaolongtan mining area[J]. Journal of Ecology and Rural Environment, 2019,35(1):9-15.(in Chinese with English abstract)
[8]	刘婷, 陈晨, 范文义, 等. 基于不同空间尺度遥感影像估算森林叶面积指数的差异[J]. 应用生态学报, 2019,30(5):1687-1698.
	LIU T, CHEN C, FAN W Y, et al. Variation of leaf area index estimation in forests based on remote sensing images of different spatial scales[J]. Chinese Journal of Applied Ecology, 2019,30(5):1687-1698.(in Chinese with English abstract)
[9]	李军玲, 彭记永. 不同生育时期冬小麦叶面积指数地面高光谱遥感模型研究[J]. 麦类作物学报, 2018,38(8):979-987.
	LI J L, PENG J Y. Estimation of winter wheat LAI at different growth stages based on canopy hyperspectral remote sensing system[J]. Journal of Triticeae Crops, 2018,38(8):979-987.(in Chinese with English abstract)
[10]	CHEN J M, BLACK T A. Foliage area and architecture of plant canopies from sunfleck size distributions[J]. Agricultural and Forest Meteorology, 1992,60(3/4):249-266. DOI URL
[11]	覃泽林, 谢国雪, 李宇翔, 等. 多时相高分一号影像在丘陵地区大宗农作物提取中的应用[J]. 南方农业学报, 2017,48(1):181-188.
	QIN Z L, XIE G X, LI Y X, et al. Application of multi-temporal GF-1 image in extraction of staple crops in hilly region[J]. Journal of Southern Agriculture, 2017,48(1):181-188.(in Chinese with English abstract)
[12]	郭燕, 武喜红, 程永政, 等. 用高分一号数据提取玉米面积及精度分析[J]. 遥感信息, 2015,30(6):31-36.
	GUO Y, WU X H, CHENG Y Z, et al. Maize recognition and accuracy evaluation based on high resolution remote sensing(GF-1)data[J]. Remote Sensing Information, 2015,30(6):31-36.(in Chinese with English abstract)
[13]	邱鹏勋, 汪小钦, 茶明星, 等. 基于TWDTW的时间序列GF-1 WFV农作物分类[J]. 中国农业科学, 2019,52(17):2951-2961.
	QIU P X, WANG X Q, CHA M X, et al. Crop identification based on TWDTW method and time series GF-1 WFV[J]. Scientia Agricultura Sinica, 2019,52(17):2951-2961.(in Chinese with English abstract)
[14]	王磊, 耿君, 杨冉冉, 等. 高分一号卫星影像特征及其在草地监测中的应用[J]. 草地学报, 2015,23(5):1093-1100.
	WANG L, GENG J, YANG R R, et al. Characteristics and application of GF-1 image in grassland monitoring[J]. Acta Agrestia Sinica, 2015,23(5):1093-1100.(in Chinese with English abstract)
[15]	李晓彤, 覃先林, 刘树超, 等. 基于GF-1 WFV数据森林叶面积指数估算[J]. 国土资源遥感, 2019,31(3):80-86.
	LI X T, QIN X L, LIU S C, et al. Estimation of forest leaf area index based on GF-1 WFV data[J]. Remote Sensing for Land & Resources, 2019,31(3):80-86.(in Chinese with English abstract)
[16]	CHEN Q C, ZHANG Z L, LIU P F, et al. Monitoring of growth parameters of sweet corn using CGMD302 spectrometer[J]. Agricultural Science & Technology, 2015,16(2):364-368.
[17]	刘明, 冯锐, 纪瑞鹏, 等. 基于MODIS-NDVI的春玉米叶面积指数和地上生物量估算[J]. 中国农学通报, 2015,31(6):80-87.
	LIU M, FENG R, JI R P, et al. Estimation of leaf area index and aboveground biomass of spring maize by MODIS-NDVI[J]. Chinese Agricultural Science Bulletin, 2015,31(6):80-87.(in Chinese with English abstract)
[18]	易秋香. 基于Sentinel-2多光谱数据的棉花叶面积指数估算[J]. 农业工程学报, 2019,35(16):189-197.
	YI Q X. Remote estimation of cotton LAI using Sentinel-2 multispectral data[J]. Transactions of the Chinese Society of Agricultural Engineering, 2019,35(16):189-197.(in Chinese with English abstract)
[19]	孙华林, 耿石英, 王小燕, 等. 晚播条件下基于高光谱的小麦叶面积指数估算方法[J]. 光谱学与光谱分析, 2019,39(4):1199-1206.
	SUN H L, GENG S Y, WANG X Y, et al. Estimation method of wheat leaf area index based on hyperspectral under late sowing conditions[J]. Spectroscopy and Spectral Analysis, 2019,39(4):1199-1206.(in Chinese with English abstract)
[20]	郭建彪, 马新明, 时雷, 等. 冬小麦叶面积指数的品种差异性与高光谱估算研究[J]. 麦类作物学报, 2018,38(3):340-347.
	GUO J B, MA X M, SHI L, et al. Variety variation and hyperspectral estimate model of leaf area index of winter wheat[J]. Journal of Triticeae Crops, 2018,38(3):340-347.(in Chinese with English abstract)
[21]	李正国, 杨鹏, 唐华俊, 等. 近20年来东北三省春玉米物候期变化趋势及其对温度的时空响应[J]. 生态学报, 2013,33(18):5818-5827.
	LI Z G, YANG P, TANG H J, et al. Trends of spring maize phenophases and spatio-temporal responses to temperature in three provinces of Northeast China during the past 20 years[J]. Acta Ecologica Sinica, 2013,33(18):5818-5827.(in Chinese with English abstract)
[22]	淮贺举, 李存军, 李奇峰, 等. 东北地区春玉米物候期时空动态变化特征[J]. 玉米科学, 2019,27(1):81-86.
	HUAI H J, LI C J, LI Q F, et al. Changes of spring maize phenology from 1981 to 2012 in the northeast plain of China[J]. Journal of Maize Sciences, 2019,27(1):81-86.(in Chinese with English abstract)
[23]	蔡亮红, 丁建丽. 基于改进植被指数土壤水分遥感反演[J]. 干旱区地理, 2017,40(6):1248-1255.
	CAI L H, DING J L. Remote sensing inversion of soil moisture based on modified vegetation index[J]. Arid Land Geography, 2017,40(6):1248-1255.(in Chinese with English abstract)
[24]	朱绪超, 袁国富, 易小波, 等. 基于Landsat 8 OLI影像的塔里木河下游河岸林叶面积指数反演[J]. 干旱区地理, 2014,37(6):1248-1256.
	ZHU X C, YUAN G F, YI X B, et al. Leaf area index inversion of riparian forest in the lower basin of Tarim River based on Landsat 8 OLI images[J]. Arid Land Geography, 2014,37(6):1248-1256.(in Chinese with English abstract)
[25]	HUETE A R. A soil-adjusted vegetation index (SAVI)[J]. Remote Sensing of Environment, 1988,25(3):295-309. DOI URL
[26]	ALMEIDA A Q, MELLO A A, DÓRIA NETO A L, et al. Empíricas entre características dendrométricas da caatinga brasileira e dados TM Landsat 5.[J]. Pesquisa Agropecuaria Brasileira, 2014,49(4):306-315. DOI URL
[27]	亚森江·喀哈尔, 茹克亚·萨吾提, 尼加提·卡斯木, 等. 优化光谱指数的露天煤矿区土壤重金属含量估算[J]. 光谱学与光谱分析, 2019,39(8):2486-2494.
	YASENJIANG K, RUKEYA S, NIJAT K, et al. Estimation of heavy metal contents in soil around open pit coal mine area based on optimized spectral index[J]. Spectroscopy and Spectral Analysis, 2019,39(8):2486-2494.(in Chinese with English abstract)
[28]	李喆, 胡蝶, 赵登忠, 等. 宽波段遥感植被指数研究进展综述[J]. 长江科学院院报, 2015,32(1):125-130.
	LI Z, HU D, ZHAO D Z, et al. Research advance of broadband vegetation index using remotely sensed images[J]. Journal of Yangtze River Scientific Research Institute, 2015,32(1):125-130.(in Chinese with English abstract)
[29]	郑踊谦, 董恒, 张城芳, 等. 植被指数与作物叶面积指数的相关关系研究[J]. 农机化研究, 2019,41(10):1-6.
	ZHENG Y Q, DONG H, ZHANG C F, et al. Study on the relationship between vegetation indices and leaf area index of crop[J]. Journal of Agricultural Mechanization Research, 2019,41(10):1-6.(in Chinese)
[30]	WANG L, YANG R R, TIAN Q J, et al. Comparative analysis of GF-1 WFV, ZY-3 MUX, and HJ-1 CCD sensor data for grassland monitoring applications[J]. Remote Sensing, 2015,7(2):2089-2108. DOI URL
[31]	胡古月, 李少达, 杨容浩. 叶面积指数遥感估算的三种回归模型分析[J]. 测绘科学, 2018,43(10):46-50.
	HU G Y, LI S D, YANG R H. Comparison of three regression models for remote sensing estimation of leaf area index[J]. Science of Surveying and Mapping, 2018,43(10):46-50.(in Chinese with English abstract)

植被指数 Vegetation index	函数类型 Function type	实测光谱数据Measured spectrum data		高分一号GF-1
植被指数 Vegetation index	函数类型 Function type	R²	调整R² Residual sum of squares	R²	调整R² Residual sum of squares
NDVI	线性Linear function	0.738	0.722	0.625	0.609
	对数Logarithmic function	0.737	0.728	0.635	0.620
	二次Quadratic function	0.737	0.708	0.685	0.657
	幂Power function	0.747	0.734	0.613	0.579
	指数Exponential function	0.757	0.740	0.602	0.585
RVI	线性Linear function	0.700	0.687	0.548	0.530
	对数Logarithmic function	0.736	0.722	0.592	0.576
	二次Quadratic function	0.731	0.712	0.669	0.642
	幂Power function	0.718	0.704	0.583	0.566
	指数Exponential function	0.693	0.677	0.534	0.515
ARVI	线性Linear function	0.687	0.653	0.680	0.667
	对数Logarithmic function	0.649	0.636	0.682	0.669
	二次Quadratic function	0.670	0.648	0.682	0.657
	幂Power function	0.688	0.680	0.674	0.661
	指数Exponential function	0.716	0.704	0.671	0.657
SAVI	线性Linear function	0.771	0.760	0.671	0.655
	对数Logarithmic function	0.765	0.754	0.682	0.667
	二次Quadratic function	0.764	0.732	0.714	0.685
	幂Power function	0.763	0.751	0.654	0.638
	指数Exponential function	0.757	0.745	0.642	0.625
MSAVI	线性Linear function	0.808	0.798	0.676	0.650
	对数Logarithmic function	0.808	0.798	0.693	0.678
	二次Quadratic function	0.808	0.787	0.742	0.716
	幂Power function	0.812	0.802	0.665	0.639
	指数Exponential function	0.814	0.804	0.656	0.619

植被指数 Vegetation index	函数类型 Function type	实测光谱数据Measured spectrum data		高分一号GF-1
植被指数 Vegetation index	函数类型 Function type	R²	调整R² Residual sum of squares	R²	调整R² Residual sum of squares
NDVI	线性Linear function	0.738	0.722	0.625	0.609
	对数Logarithmic function	0.737	0.728	0.635	0.620
	二次Quadratic function	0.737	0.708	0.685	0.657
	幂Power function	0.747	0.734	0.613	0.579
	指数Exponential function	0.757	0.740	0.602	0.585
RVI	线性Linear function	0.700	0.687	0.548	0.530
	对数Logarithmic function	0.736	0.722	0.592	0.576
	二次Quadratic function	0.731	0.712	0.669	0.642
	幂Power function	0.718	0.704	0.583	0.566
	指数Exponential function	0.693	0.677	0.534	0.515
ARVI	线性Linear function	0.687	0.653	0.680	0.667
	对数Logarithmic function	0.649	0.636	0.682	0.669
	二次Quadratic function	0.670	0.648	0.682	0.657
	幂Power function	0.688	0.680	0.674	0.661
	指数Exponential function	0.716	0.704	0.671	0.657
SAVI	线性Linear function	0.771	0.760	0.671	0.655
	对数Logarithmic function	0.765	0.754	0.682	0.667
	二次Quadratic function	0.764	0.732	0.714	0.685
	幂Power function	0.763	0.751	0.654	0.638
	指数Exponential function	0.757	0.745	0.642	0.625
MSAVI	线性Linear function	0.808	0.798	0.676	0.650
	对数Logarithmic function	0.808	0.798	0.693	0.678
	二次Quadratic function	0.808	0.787	0.742	0.716
	幂Power function	0.812	0.802	0.665	0.639
	指数Exponential function	0.814	0.804	0.656	0.619

数据 Data	植被指数 Vegetation index	函数类型 Function type	R²	调整R² Residual sum of squares
实测光谱数据	NDVI	指数Exponential function	0.757	0.740
Measured spectrum data	RVI	对数Logarithmic function	0.736	0.722
	ARVI	指数Exponential function	0.716	0.704
	SAVI	线性Linear function	0.771	0.760
	MSAVI	指数Exponential function	0.814	0.804
高分一号	NDVI	二次Quadratic function	0.684	0.657
GF-1/WFV	RVI	二次Quadratic function	0.669	0.642
	ARVI	对数Logarithmic function	0.682	0.669
	SAVI	二次Quadratic function	0.714	0.685
	MSAVI	二次Quadratic function	0.742	0.716

数据 Data	植被指数 Vegetation index	函数类型 Function type	R²	调整R² Residual sum of squares
实测光谱数据	NDVI	指数Exponential function	0.757	0.740
Measured spectrum data	RVI	对数Logarithmic function	0.736	0.722
	ARVI	指数Exponential function	0.716	0.704
	SAVI	线性Linear function	0.771	0.760
	MSAVI	指数Exponential function	0.814	0.804
高分一号	NDVI	二次Quadratic function	0.684	0.657
GF-1/WFV	RVI	二次Quadratic function	0.669	0.642
	ARVI	对数Logarithmic function	0.682	0.669
	SAVI	二次Quadratic function	0.714	0.685
	MSAVI	二次Quadratic function	0.742	0.716

数据 Data	植被指数 Vegetation index	函数类型 Function type	表达式 Expression
实测光谱数据	NDVI	指数Exponential function	y =0.0493e^4.7955^x
Measured spectrum data	RVI	对数Logarithmic function	y=1.9421ln(x)-1.9457
	ARVI	指数Exponential function	y=0.1341e^3.8751^x
	SAVI	线性Linear function	y =12.197x-12.596
	MSAVI	指数Exponential	y =0.0019e^8.0142^x
高分一号	NDVI	二次Polynomial function	y =-105.51x²+166.75x - 61.886
GF-1/WFV	RVI	二次Polynomial function	y=-0.1668x²+2.6269x-6.4054
	ARVI	对数Logarithmic function	y=8.3808ln(x)+5.9208
	SAVI	二次Polynomial function	y=-116.16x²+123.92x - 28.995
	MSAVI	二次Polynomial function	y=-96.689x²+102.23x - 22.956

Application research of GF-1/WFV data in estimation of maize leaf area index

RichHTML

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 11

References 31

Related Articles 15

Recommended Articles

Metrics

Comments

指标 Index	数据来源 Data sources	NDVI	RVI	ARVI	SAVI	MSAVI
RE	高分一号GF-1/WFV	0.0614	0.0665	0.0709	0.0590	0.0581
	实测光谱Measured spectrum	0.0563	0.0560	0.0588	0.0548	0.0476
RPD	高分一号GF-1/WFV	1.6070	1.5471	1.4936	1.7035	1.7108
	实测光谱Measured spectrum	1.7379	1.7100	1.6533	1.7573	2.0986
RMSE	高分一号GF-1/WFV	0.2621	0.2675	0.2937	0.2508	0.2502
	实测光谱Measured spectrum	0.2372	0.2340	0.2561	0.2293	0.2082

植被指数 Vegetation index	高分一号 GF-1	实测光谱 Measured spectrum
NDVI	0.833^**	0.854^**
RVI	0.806^**	0.858^**
ARVI	0.825^**	0.781^**
SAVI	0.853^**	0.862^**
MSAVI	0.857^**	0.894^**

[1]	YANG Mei, HU Xiaolan, SHEN Tao, TAN Kang, LIU Dailing, QIU Hongbo. Construction of single fragment substitution lines of maize 8th chromosome and sreening of resistant maize germplasm to gray leaf spot [J]. Acta Agriculturae Zhejiangensis, 2021, 33(3): 383-389.
[2]	QU Zhan, YANG Litao. Development of plasmid DNA reference material of genetically modified maize TC1507 [J]. Acta Agriculturae Zhejiangensis, 2021, 33(3): 390-395.
[3]	WANG Tangang, SUN Ting, WANG Jichuan, LI Huiqin, GAO Zhen, SHI Yuanqiang. Effects of sowing date and density on population structure and lodging resistance of winter wheat under drip irrigation [J]. Acta Agriculturae Zhejiangensis, 2021, 33(2): 193-202.
[4]	LIU Genhong, XUE Yinxin, ZHANG Qian, ZHOU Jiarui, MAI Xiaofeng. Effects of different tillage depth and amount of straw returned to the field on maize growth under drip-irrigation [J]. Acta Agriculturae Zhejiangensis, 2021, 33(1): 8-17.
[5]	CHANG Huiqing, XU Fujin, PAN Yajie. Passivation effects of calcium carbonate with/without chitosan on chromium pollution in calcareous soil [J]. , 2020, 32(9): 1665-1671.
[6]	GUO Yanjing, XIAO Haifeng. Support level and structural characteristics of maize subsidy policy in world's major maize producing countries and region [J]. , 2020, 32(9): 1722-1731.
[7]	BAI Junyan, LU Junhao, FU Xueyan, WU Xiaohong, YANG Youbing, LEI Ying, PANG Youzhi, LU Xiaoning, GONG Huirong, HU Luxing, LIU Hongtao, FAN Hongdeng, CAO Heng, SHI Kunpeng, CHEN Mengke, MA Yongkang. Analysis of correlation between polymorphism of IGF-1R gene and body size traits in egg quail [J]. , 2020, 32(3): 398-405.
[8]	ZHAO Xingkai, SHI Haichun, YU Xuejie, YANG Shu, ZHAO Changyun, XIA Wei, KE Yongpei. Breeding potential analyses of 13 new inbred lines in maize [J]. Acta Agriculturae Zhejiangensis, 2020, 32(12): 2119-2127.
[9]	JIANG Yuanyuan, JI Yi, LAI Yongmin, CHEN Xiaoyun, XU Junfeng, XU Xiaoli, MA Lianju. Safety evaluation of Cry-transgenic insect-resistant maize on silkworm, Bombyx mori [J]. , 2020, 32(11): 2042-2049.
[10]	ZHONG Jing, TAN Fen, ZHANG Hongquan, XIONG Xiaoqin, HUANG Lixia. Expression pattern and protein structure analysis of maize XYLPs gene family [J]. , 2020, 32(10): 1741-1747.
[11]	YUE Gaohong, PAN Binrong, LIU Yongan, MEI Xixue, XU Likui, ZHANG Zongchen, ZHOU Zhihui. Genetic diversity analysis of sweet maize inbred lines in Southern Zhejiang by SSR markers [J]. , 2019, 31(7): 1029-1036.
[12]	FANG Fang, HE Xuchen, ZHANG Zhihao, ZHANG Qin, GUAN Yajing, HU Jin, HU Weimin. Response mechanism and stress resistance of maize inbred lines to high temperature stress at seedling stage [J]. , 2019, 31(7): 1045-1056.
[13]	WANG Xiaoxuan, MENG Qingyan, ZHANG Haixiang, WEI Xiangqin, YANG Zenan. Inversion of maize and wheat leaf area index based on particle swarm optimization neural network model [J]. , 2019, 31(7): 1170-1176.
[14]	LIU Zhi, HE Zheng, MIAO Fangfang, JIA Biao. Method and experiment for estimating emergence rate of water and fertilizer integrated maize based on drone technology [J]. , 2019, 31(6): 977-985.
[15]	ZHENG Xiaoxue, QIN Lijie. Water footprint of maize production in middle Jilin under different rainfall years [J]. , 2019, 31(5): 695-703.