冬小麦叶片光合特征高光谱遥感估算模型的比较研究

doi:10.3864/j.issn.0578-1752.2019.04.004

Abstract

Abstract:

【Objective】Photosynthesis is the basis of crop yield and quality formation. Accurate quantitative remote sensing inversion of crop photosynthetic parameters can not only understand the growth and development of crops and the accumulation of organic matter, but also can provide reference for the ecosystem process model based on remote sensing. In order to estimate the photosynthetic characteristic parameters quickly and accurately, the hyperspectral inversion model of three photosynthetic parameters of winter wheat was constructed by combining the original spectrum, three traditional spectral transformation techniques and four simulation methods. The feasibility of hyperspectral inversion of photosynthetic parameters of winter wheat was discussed, and the applicability of different spectra and simulation methods were compared. 【Method】The maximum net photosynthetic rate (Amax), PSⅡ effective photochemical quantum yield (Fv'/Fm') of different leaf ages was obtained under the support of gas exchange and hyperspectral field experiments of winter wheat under different nitrogen application conditions. The photochemical quenching coefficient (qP) and hyperspectral reflectivity were obtained, and the reciprocal, logarithmic and first-order differential transformations of the original hyperspectrum were carried out. According to the results of correlation analysis of three photosynthetic parameters and four spectra, the band whose significant level was better than 0.01 was selected as input variable, and then the partial least square (PLS), support vector machine (SVM), multivariate linear regression (MLR) and artificial neural network (ANN) were used to establish the inversion model of photosynthetic parameters of winter wheat leaves. Based on the determination coefficient (R ²) and root mean square error (RMSE) of modeling and validation process, the simulation accuracy of different models was compared and analyzed.【Result】(1) The correlation analysis of the three photosynthetic parameters and four spectra showed that the sensitive spectral regions of the primitive, reciprocal and logarithmic spectra to the three photosynthetic parameters (Amax, Fv′/Fm′ and qP) were concentrated in the 400-750 nm spectrum range. The sensitive spectral regions of the first derivative spectrum to the three photosynthetic parameters were 470-560, 630-700 and 700-770 nm, respectively. (2) The optimal inversion model of Amax, Fv'/Fm' and qP was composed of MLR model based on reciprocal spectrum, MLR model based on first derivative spectrum and MLR model based on original spectrum, respectively. The R ² of the modeling was 0.75, 0.65 and 0.65, respectively, and the R ² of the validation was 0.73, 0.59 and 0.44, respectively. The results showed that the simulation of Amax and Fv'/Fm' based on hyperspectral method was feasible, the effectiveness of simulated qP needed further be verified. (3) The spectral performance of different transformations was different. Taking PLS simulation Amax as an example, the order of spectral performance was original spectrum > reciprocal spectrum > logarithmic spectrum > first derivative spectrum. (4) The estimation ability of different models was also different. Taking Amax simulation based on original spectrum as an example, the order of estimation ability of different models was MLR > PLS > ANN > SVM.【Conclusion】By comparing four spectra and four simulation methods, the hyperspectral inversion results of three photosynthetic parameters of winter wheat showed that Amax and Fv'/Fm' could be well simulated by hyperspectral method, but hyperspectral interpretation ability to qP was low and further study was needed. The hyperspectral information was sensitive to the photosynthetic parameters of winter wheat and affected by spectral types and simulation methods. It could be used to monitor the dynamic changes of photosynthetic capacity of winter wheat and to provide a basis for understanding the growth of crops.

Key words: photosynthetic parameters, partial least squares, support vector machine, multivariate linear regression, neural network, hyperspectral

ZHANG Zhuo,LONG HuiLing,WANG ChongChang,YANG GuiJun. Comparison of Hyperspectral Remote Sensing Estimation Models Based on Photosynthetic Characteristics of Winter Wheat Leaves[J].Scientia Agricultura Sinica, 2019, 52(4): 616-628.

0
/ / Recommend

Add to citation manager EndNote|Reference Manager|ProCite|BibTeX|RefWorks

URL: https://www.chinaagrisci.com/EN/10.3864/j.issn.0578-1752.2019.04.004

https://www.chinaagrisci.com/EN/Y2019/V52/I4/616

Figures/Tables 10

Fig. 1

Fig. 2

Fig. 3

Table 1

Fig. 4

Fig. 5

Fig. 6

Table 3

Table 4

Table 2

References 32

[1]	许大全 . 光合作用学. 第一版. 北京: 科学出版社, 2013: 3-5.
	XU D Q. The Science of Photosynthesis. 1st edition. Beijing: Science Press, 2013: 3-5. (in Chinese)
[2]	周磊, 何洪林, 孙晓敏, 张黎, 于贵瑞, 任小丽, 闵程程, 赵凤华 . 基于数字相机的冬小麦物候和碳交换监测. 生态学报, 2012,32(16):5146-5153. doi: 10.5846/stxb201110271606
	ZHOU L, HE H L, SUN X M, ZHANG L, YU G R, REN X L, MIN C C, ZHAO F H . Using digital repeat photography to model winter wheat phenology and photosynthetic CO₂ uptake. Acta Ecologica Sinica, 2012,32(16):5146-5153. (in Chinese) doi: 10.5846/stxb201110271606
[3]	李少昆 . 作物光合作用研究方法. 石河子大学学报(自然科学版), 2000,4(4):321-330. doi: 10.3969/j.issn.1007-7383.2000.04.012
	LI S K . Survey of research method of crop photosynthesis. Journal of Shihezi University(Natural Science), 2000,4(4):321-330. (in Chinese) doi: 10.3969/j.issn.1007-7383.2000.04.012
[4]	DECHANT B, CUNTZ M, VOHLAND M, SCHULZ E, DOKTOR D . Estimation of photosynthesis traits from leaf reflectance spectra: Correlation to nitrogen content as the dominant mechanism. Remote Sensing of Environment, 2017,196:279-292. doi: 10.1016/j.rse.2017.05.019
[5]	吕玮, 李玉环, 毛伟兵, 宫雪, 陈世更 . 基于高光谱的小麦旗叶净光合速率的遥感反演模型的比较研究. 农业资源与环境学报, 2017,34(6):582-586.
	LÜ W, LI Y H, MAO W B, GONG X, CHEN S G . Comparison of estimation methods for net photosynthetic rate of wheat’s flag leaves based on hyperspectrum. Journal of Agricultural Resources and Environment, 2017,34(6):582-586. (in Chinese)
[6]	王娣, 佃袁勇, 乐源, 黄春波 . 基于高光谱植被指数的叶片净光合速率Pn反演. 地理与地理信息科学, 2016,32(4):42-48. doi: 10.3969/j.issn.1672-0504.2016.04.008
	WANG D, DIAN Y Y, LE Y, HUANG C B . Net photosynthetic rate inversion based on hyperspectral vegetation indices. Geography and Geo-Information Science, 2016,32(4):42-48. (in Chinese) doi: 10.3969/j.issn.1672-0504.2016.04.008
[7]	张峰, 周广胜 . 玉米农田冠层光合参数的多光谱遥感反演. 植物生态学报, 2014,38(7):710-719. doi: 10.3724/SP.J.1258.2014.00066
	ZHANG F, ZHOU G S . Estimating canopy photosynthetic parameters in maize field based on multi-spectral remote sensing. Chinese Journal of Plant Ecology, 2014,38(7):710-719. (in Chinese) doi: 10.3724/SP.J.1258.2014.00066
[8]	SERBIN S P, SINGH A, DESAI A, DUBOIS S . Remotely estimating photosynthetic capacity, and its response to temperature, in vegetation canopies using imaging spectroscopy. Remote Sensing of Environment, 2015,167:78-87. doi: 10.1016/j.rse.2015.05.024
[9]	FARQUHAR G D, CAEMMERER S V, BERRY J A . A biochemical model of photosynthetic CO₂ assimilation in leaves of C3 species. Planta, 1980,149:78-90. doi: 10.1007/BF00386231
[10]	DEMMIG-ADAMS B . Using chlorophyll fluorescence to assess the fraction of absorbed light allocated to thermal dissipation of excess excitation. Physiologia Plantarum, 1996,98(2):253-264.
[11]	WOLD H . Estimation of principal components and related models by iterative least squares. Multivariate Analysis, 1966,1:391-420.
[12]	WU S, AMARI S I . Conformal transformation of Kernel functions: A data-dependent way to improve support vector machine classifiers. Neural Process Letters, 2002,15(1):59-67. doi: 10.1023/A:1013848912046
[13]	ZELTERMAN D. Multivariable linear regression, applied multivariate statistics with R. Springer International Publishing, 2015: 231-256.
[14]	PARK D C, ELSHARKAWI M A, MARKS R J I . Electric load forecasting using an artificial neural network. IEEE Transactions on Power Systems, 1991,6(2):442-449.
[15]	YUE J, FENG H, YANG G, LI Z . A comparison of regression techniques for estimation of above-ground winter wheat biomass using near-surface spectroscopy. Remote Sensing, 2018,10(1):66-89. doi: 10.3390/rs10010066
[16]	DECHANT B, CUNTZ M, VOHLAND M, SCHULZ E . Estimation of photosynthesis traits from leaf reflectance spectra: Correlation to nitrogen content as the dominant mechanism. Remote Sensing of Environment, 2017,196:279-292. doi: 10.1016/j.rse.2017.05.019
[17]	李婷, 季宇寒, 张漫, 沙莎, 李民赞 . 基于PLSR和BPNN方法的番茄光合速率预测比较. 农业工程学报, 2015,31(S2):222-229.
	LI T, JI Y H, ZHANG M, SHA S, LI M Z . Comparison of photosynthesis prediction methods with BPNN and PLSR in different growth stages of tomato. Transactions of the Chinese Society of Agricultural Engineering, 2015,31(S2):222-229. (in Chinese)
[18]	陈俊英, 陈硕博, 张智韬, 付秋萍, 边江, 崔婷 . 无人机多光谱遥感反演花蕾期棉花光合参数研究. 农业机械学报, 2018,49(10):230-239.
	CHEN J Y, CHEN S B, ZHANG Z T, FU Q P, BIAN J, CUI T . Investigation on photosynthetic parameters of cotton during budding period by multi-spectral remote sensing of unmanned aerial vehicle. Transactions of the Chinese Society for Agricultural Machinery, 2018,49(10):230-239. (in Chinese)
[19]	DAUGHTRY C S T, WALTHALL C L, KIM M S, MCMURTREY J E . Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance. Remote Sensing of Environment, 2000,74(2):229-239. doi: 10.1016/S0034-4257(00)00113-9
[20]	赵祥, 刘素红, 王培娟, 王锦地, 田振坤 . 基于高光谱数据的小麦叶绿素含量反演. 地理与地理信息科学, 2004,20(3):36-39. doi: 10.3969/j.issn.1672-0504.2004.03.008
	ZHAO X, LIU S H, WANG P J, WANG J D, TIAN Z K . A method for inverting chlorophyll content of wheat using hyperspectral. Geography and Geo-Information Science, 2004,20(3):36-39. doi: 10.3969/j.issn.1672-0504.2004.03.008
[21]	ZHAO D L, BARRY G, MICHAEL I, CHEN J H . Sugarcane genotype variation in leaf photosynthesis properties and yield as affected by mill mud application. Agronomy Journal, 2015,107(2):
	1831-1849.
[22]	裴浩杰, 冯海宽, 李长春, 李振海, 杨贵军, 王衍安, 郭建华 . 基于多元线性回归和随机森林的苹果叶绿素含量高光谱估测方法比较. 江苏农业科学, 2018,46(17):224-230.
	PEI H J, FENG H K, LI C C, LI Z H, YANG G J, WANG Y A, GUO J H . Comparison of hyperspectral estimation methods for chlorophyll content of apple based on multiple linear regression and stochastic forest. Jiangsu Agricultural Sciences, 2018,46(17):224-230. (in Chinese)
[23]	孙少波, 杜华强, 李平衡, 周国模, 徐小军, 高国龙, 李雪建 . 基于小波变换的毛竹叶片净光合速率高光谱遥感反演. 应用生态学报, 2016,27(1):49-58. doi: 10.13287/j.1001-9332.201601.020
	SUN S B, DU H Q, LI P H, ZHOU G M, XU X J, GAO G L, LI X J . Retrieval of leaf net photosynthetic rate of moso bamboo forests using hyperspectral remote sensing based on wavelet transform. Chinese Journal of Applied Ecology, 2016,27(1):49-58. (in Chinese) doi: 10.13287/j.1001-9332.201601.020
[24]	王惠文, 孟洁 . 多元线性回归的预测建模方法. 北京航空航天大学学报, 2007,33(4):500-504. doi: 10.3969/j.issn.1001-5965.2007.04.028
	WANG H W, MENG J . Predictive modeling on multivariate linear regression. Journal of Beijing University of Aeronautics and Astronautics, 2007,33(4):500-504. (in Chinese) doi: 10.3969/j.issn.1001-5965.2007.04.028
[25]	岳继博, 杨贵军, 冯海宽 . 基于随机森林算法的冬小麦生物量遥感估算模型对比. 农业工程学报, 2016,32(18):175-182. doi: 10.11975/j.issn.1002-6819.2016.18.024
	YUE J B, YANG G J, FENG H K . Comparative of remote sensing estimation models of winter wheat biomass based on random forest algorithm. Transactions of the Chinese Society of Agricultural Engineering, 2016,32(18):175-182. (in Chinese) doi: 10.11975/j.issn.1002-6819.2016.18.024
[26]	王奕涵, 石铁柱, 刘会增, 王竣杰, 邬国锋 . 水稻叶片氮含量反演偏最小二乘模型设计. 遥感信息, 2015,30(6):42-47. doi: 10.3969/j.issn.1000-3177.2015.06.008
	WANG Y H, SHI T Z, LIU H Z, WANG J J, WU G F . Partial least squares regression model for retrieving paddy rice nitrogen content with band depth analysis and genetic algorithm. Remote Sensing Information, 2015,30(6):42-47. (in Chinese) doi: 10.3969/j.issn.1000-3177.2015.06.008
[27]	宋丰萍, 蒙祖庆 . 干旱胁迫下作物光合参数研究进展. 高原农业, 2018,2(2):138-144, 212.
	SONG F P, MENG Z Q . Research progress on photosynthetic parameters of crops in drought stress. Journal of Plateau Agriculture, 2018,2(2):138-144, 212. (in Chinese)
[28]	丁世飞, 齐丙娟, 谭红艳 . 支持向量机理论与算法研究综述. 电子科技大学学报, 2011,40(1):2-10. doi: 10.3969/j.issn.1001-0548.2011.01.001
	DING S F, QI B J, TAN H Y . An overview on theory and algorithm of support vector machines. Journal of University of Electronic Science and Technology of China, 2011,40(1):2-10. (in Chinese) doi: 10.3969/j.issn.1001-0548.2011.01.001
[29]	高林, 杨贵军, 于海洋, 徐波, 赵晓庆, 董锦绘, 马亚斌 . 基于无人机高光谱遥感的冬小麦叶面积指数反演. 农业工程学报, 2016,32(22):113-120. doi: 10.11975/j.issn.1002-6819.2016.22.016
	GAO L, YANG G J, YU H Y, XU B, ZHAO X Q, DONG J H, MA Y B . Retrieving winter wheat leaf area index based on unmanned aerial vehicle hyperspectral remote sensing. Transactions of the Chinese Society of Agricultural Engineering, 2016,32(22):113-120. (in Chinese) doi: 10.11975/j.issn.1002-6819.2016.22.016
[30]	LI D, WANG C, LIU W, WEI L, SHUI S C . Estimation of litchi (Litchi chinensis Sonn.) leaf nitrogen content at different growth stages using canopy reflectance spectra. European Journal of Agronomy, 2016,80:182-194. doi: 10.1016/j.eja.2016.08.001
[31]	李粉玲, 常庆瑞 . 基于连续统去除法的冬小麦叶片全氮含量估算. 农业机械学报, 2017,48(7):174-179. doi: 10.6041/j.issn.1000-1298.2017.07.022
	LI F L, CHANG Q R . Estimation of winter wheat leaf nitrogen content based on continuum removed spectra. Transactions of the Chinese Society for Agricultural Machinery, 2017,48(7):174-179. (in Chinese) doi: 10.6041/j.issn.1000-1298.2017.07.022
[32]	YUAN H H, YANG G J, LI C C, WANG Y J, LIU J G, YU H Y, FENG H K . Retrieving soybean leaf area index from unmanned aerial vehicle hyperspectral remote sensing: Analysis of RF, ANN, and SVM regression models. Remote Sensing, 2017,9(4):309-325. doi: 10.3390/rs9040309

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed

Comments

Recommended 0

No Suggested Reading articles found!

光合参量 Photosynthetic parameters	光谱类型 Types of spectrum	P>0.01置信区 Confidence interval of P > 0.01 (nm)	r值范围 Range of r values	相关类型 Correlation type
Amax	原始光谱 Original spectrum	399-737	0.39-0.7	负相关 Negative correlation
	倒数光谱 Reciprocal of spectral	400-727	0.37-0.73	正相关 Positive correlation
	对数光谱 Logarithm of spectral	399-731	0.38-0.72	负相关 Negative correlation
	一阶导数光谱 First-order differential of spectrum	470-552、678-701、711-764	r值最大为0.65 r maximum:0.65	—
Fv′/Fm′	原始光谱 Original spectrum	431-724	0.36-0.58	负相关 Negative correlation
	倒数光谱 Reciprocal of spectral	442-720	0.36-0.59	正相关 Positive correlation
	对数光谱 Logarithm of spectral	435-722	0.36-0.6	负相关 Negative correlation
	一阶导数光谱 First-order differential of spectrum	480-551、631-673、709-765	r值最大为0.71 r maximum:0.71	—
qP	原始光谱 original spectrum	459-718	0.36-0.46	负相关 Negative correlation
	倒数光谱 Reciprocal of spectral	485-710	0.36-0.59	正相关 Positive correlation
	对数光谱 Logarithm of spectral	466-714	0.36-0.47	负相关 Negative correlation
	一阶导数光谱 First-order differential of spectrum	480-522、632-673、710-758	r值最大为0.52 r maximum:0.52	—

叶位 Leaf position	建模精度R²Modeling precision R²	建模精度RMSE Modeling precision RMSE
第一层叶片 First layer blade	0.75	4.11
第二层叶片 Second layer blade	0.89	3.54
第三层叶片 Third layer blade	0.80	3.91
所有叶片 All blades	0.68	5.81

生育期 Growth period	建模精度R²Modeling precision R²	建模精度RMSE Modeling precision RMSE
拔节期 Jointing stage	0.81	3.13
挑旗期 Flagging stage	0.91	1.73
灌浆期 Filling stage	0.56	3.74
所有生育期 All growth stage	0.65	5.21

光谱类型 Type of spectrum	模拟结果	Amax				Fv′/Fm′				qP
光谱类型 Type of spectrum	模拟结果	MLR	ANN	SVM	PLS	MLR	ANN	SVM	PLS	MLR	ANN	SVM	PLS
原始光谱 Original spectrum	建模R² Modeling precision R²	0.75	0.66	0.57	0.67	0.58	0.60	0.94	0.48	0.65	0.45	0.49	0.35
	建模RMSE Modeling precision RMSE	5.10	6.13	6.77	5.90	0.03	0.03	0.01	0.04	0.07	0.10	0.09	0.10
	验证R² Validation precision R²	0.50	0.56	0.58	0.66	0.55	0.65	0.44	0.67	0.44	0.21	0.21	0.12
	验证RMSE Validation precision RMSE	7.25	7.18	6.60	5.74	0.03	0.03	0.03	0.02	0.09	0.10	0.10	0.10
倒数光谱 Reciprocal of spectrum	建模R² Modeling precision R²	0.75	0.67	0.57	0.66	0.62	0.57	0.68	0.51	0.54	0.45	0.46	0.44
	建模RMSE Modeling precision RMSE	5.12	6.19	6.75	5.93	0.03	0.03	0.03	0.03	0.09	0.10	0.10	0.09
	验证R² Validation precision R²	0.73	0.66	0.56	0.63	0.43	0.63	0.33	0.72	0.40	0.21	0.14	0.16
	验证RMSE Validation precision RMSE	5.10	6.52	6.79	6.21	0.03	0.03	0.03	0.02	0.09	0.10	0.10	0.10
对数光谱 Logarithm of spectrum	建模R² Modeling precision R²	0.74	0.69	0.56	0.65	0.60	0.58	0.62	0.51	0.48	0.43	0.58	0.41
	建模RMSE Modeling precision RMSE	5.24	6.17	6.84	6.03	0.03	0.03	0.03	0.03	0.09	0.10	0.09	0.10
	验证R² Validation precision R²	0.75	0.68	0.57	0.59	0.55	0.67	0.41	0.75	0.12	0.15	0.24	0.10
	验证RMSE Validation precision RMSE	5.62	6.53	6.74	6.34	0.03	0.03	0.03	0.02	0.10	0.10	0.09	0.10
一阶微分光谱 First derivative of spectrum	建模R² Modeling precision R²	0.64	0.55	0.70	0.69	0.65	0.75	0.50	0.54	0.38	0.52	0.32	0.38
	建模RMSE Modeling precision RMSE	6.11	6.94	5.70	5.70	0.03	0.03	0.04	0.03	0.10	0.10	0.11	0.10
	验证R² Validation precision R²	0.57	0.51	0.50	0.47	0.59	0.51	0.74	0.68	0.02	0.40	0.18	0.11
	验证RMSE Validation precision RMSE	6.47	7.02	7.10	7.35	0.02	0.02	0.02	0.02	0.12	0.09	0.09	0.10

Comparison of Hyperspectral Remote Sensing Estimation Models Based on Photosynthetic Characteristics of Winter Wheat Leaves

RichHTML

PDF

Abstract

Cite this article

share this article

Figures/Tables 10

References 32

Related Articles 15

Metrics

Comments

Recommended 0

[1]	CAI WeiDi,ZHANG Yu,LIU HaiYan,ZHENG HengBiao,CHENG Tao,TIAN YongChao,ZHU Yan,CAO WeiXing,YAO Xia. Early Detection on Wheat Canopy Powdery Mildew with Hyperspectral Imaging [J]. Scientia Agricultura Sinica, 2022, 55(6): 1110-1126.
[2]	TAN XianMing,ZHANG JiaWei,WANG ZhongLin,CHEN JunXu,YANG Feng,YANG WenYu. Prediction of Maize Yield in Relay Strip Intercropping Under Different Water and Nitrogen Conditions Based on PLS [J]. Scientia Agricultura Sinica, 2022, 55(6): 1127-1138.
[3]	WU Jun,GUO DaQian,LI Guo,GUO Xi,ZHONG Liang,ZHU Qing,GUO JiaXin,YE YingCong. Prediction of Soil Organic Carbon Content in Jiangxi Province by Vis-NIR Spectroscopy Based on the CARS-BPNN Model [J]. Scientia Agricultura Sinica, 2022, 55(19): 3738-3750.
[4]	MA YuYang,GUAN HaiXiang,YANG HaoXuan,SHAO Shuai,SHAO YiQun,LIU HuanJun. A New Method to Improve the Accuracy of Digital Elevation Model in Northeast China by Using Terrain, Soil and Crop Information [J]. Scientia Agricultura Sinica, 2021, 54(8): 1715-1727.
[5]	ZHOU Meng,HAN XiaoXu,ZHENG HengBiao,CHENG Tao,TIAN YongChao,ZHU Yan,CAO WeiXing,YAO Xia. Remote Sensing Estimation of Cotton Biomass Based on Parametric and Nonparametric Methods by Using Hyperspectral Reflectance [J]. Scientia Agricultura Sinica, 2021, 54(20): 4299-4311.
[6]	FEI ShuaiPeng,YU XiaoLong,LAN Ming,LI Lei,XIA XianChun,HE ZhongHu,XIAO YongGui. Research on Winter Wheat Yield Estimation Based on Hyperspectral Remote Sensing and Ensemble Learning Method [J]. Scientia Agricultura Sinica, 2021, 54(16): 3417-3427.
[7]	LI PengLei,ZHANG Xiao,WANG WenHui,ZHENG HengBiao,YAO Xia,ZHU Yan,CAO WeiXing,CHENG Tao. Assessment of Terrestrial Laser Scanning and Hyperspectral Remote Sensing for the Estimation of Rice Grain Yield [J]. Scientia Agricultura Sinica, 2021, 54(14): 2965-2976.
[8]	SHAO ZeZhong,YAO Qing,TANG Jian,LI HanQiong,YANG BaoJun,LÜ Jun,CHEN Yi. Research and Development of the Intelligent Identification System of Agricultural Pests for Mobile Terminals [J]. Scientia Agricultura Sinica, 2020, 53(16): 3257-3268.
[9]	ZHOU LongFei, GU XiaoHe, CHENG Shu, YANG GuiJun, SUN Qian, SHU MeiYan. Spectral Diagnosis of Leaf Area Density of Maize at Heading Stage Under Lodging Stress [J]. Scientia Agricultura Sinica, 2019, 52(9): 1518-1528.
[10]	YanChuan MA,Hao LIU,ZhiFang CHEN,Kai ZHANG,Xuan YU,JingLei WANG,JingSheng SUN. Canopy Equivalent Water Thickness Estimation of Cotton Based on Hyperspectral Index [J]. Scientia Agricultura Sinica, 2019, 52(24): 4470-4483.
[11]	DING YiBo,XU JiaTun,LI Liang,CAI HuanJie,SUN YaNan. Analysis of Drought Characteristics and Its Trend Change in Shaanxi Province Based on SPEI and MI [J]. Scientia Agricultura Sinica, 2019, 52(23): 4296-4308.
[12]	LIU HuiFang,HE Zheng,JIA Biao,LIU Zhi,LI ZhenZhou,FU JiangPeng,MU RuiRui,KANG JianHong. Photosynthetic Response Characteristics of Maize Under Drip Irrigation Based on Machine Learning [J]. Scientia Agricultura Sinica, 2019, 52(17): 2939-2950.
[13]	WANG JunJie,CHEN Ling,WANG HaiGang,CAO XiaoNing,LIU SiChen,TIAN Xiang,QIN HuiBin,QIAO ZhiJun. Effects of Hyperspectral Prediction on Leaf Nitrogen Content and the Grain Protein Content of Broomcorn Millet [J]. Scientia Agricultura Sinica, 2019, 52(15): 2593-2603.
[14]	WU Hao,HUANG YuanYuan,WANG Hui,ZHOU Yu,SHEN JiaCheng,ZHANG Can,DING JinFeng,ZOU Juan,MA ShangYu,GUO WenShan,ZHENG WenYin,YAO DaNian. Comprehensive Analysis on Grain and Processing Quality of Several Wheat Varieties in the Middle and Lower Reaches of Yangtze River [J]. Scientia Agricultura Sinica, 2019, 52(13): 2193-2207.
[15]	ZHANG Biao,LIU Xuan,BI JinFeng,WU XinYe,JIN Xin,LI Xuan,LI Xiao. Suitability Evaluation of Apple for Chips-Processing Based on BP Artificial Neural Network [J]. Scientia Agricultura Sinica, 2019, 52(1): 129-142.