Scientia Agricultura Sinica ›› 2019, Vol. 52 ›› Issue (4): 616-628.doi: 10.3864/j.issn.0578-1752.2019.04.004

• TILLAGE & CULTIVATION·PHYSIOLOGY & BIOCHEMISTRY·AGRICULTURE INFORMATION TECHNOLOGY • Previous Articles     Next Articles

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

ZHANG Zhuo1,2,3,4,LONG HuiLing1,2,3(),WANG ChongChang4,YANG GuiJun1,2,3   

  1. 1Beijing Research Center for Information Technology in Agriculture/Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture, Beijing 100097
    2National Engineering Research Center for Information Technology in Agriculture, Beijing 100097
    3Beijing Engineering Research Center for Agriculture Internet of Things, Beijing 100097
    4School of Mapping and Geographical Science, Liaoning Technical University, Fuxin 123000, Liaoning
  • Received:2018-09-25 Accepted:2018-12-20 Online:2019-02-16 Published:2019-02-27
  • Contact: HuiLing LONG E-mail:longhl@nercita.org.cn

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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

Fig. 1

Correlation between original spectrum, reciprocal of spectral, logarithm of spectral and first-order differential of spectrum and maximum photosynthetic rate (Amax) a: Original spectrum, b: Reciprocal of spectral, c: Logarithm of spectral, d: First-order differential of spectrum; Straight line in the figure indicates a level of significance of 0.01. The same as below"

Fig. 2

Correlation between original spectrum, reciprocal of spectral, logarithm of spectral and first-order differential of spectrum and PSⅡ effective photochemical quantum yield (Fv′/Fm′)"

Fig. 3

Correlation between original spectrum, reciprocal of spectral, logarithm of spectral and first-order differential of spectrum and photochemical quenching coefficient (qP)"

Table 1

Sensitivity range and correlation between different types of spectra and various photosynthetic parameters"

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		 —

Fig. 4

Linear fitting of scattered point diagram between simulated and measured values of Amax using four models Black line in picture: Y=X; MLR- Reciprocal of spectral: Based on reciprocal of spectra, using MLR method to simulate Amax results, other similarities"

Fig. 5

Linear fitting of scattered point diagram between simulated and measured values of Fv′/Fm′ using four models Black line in picture: Y=X; MLR-First derivative of spectrum: Based on first derivative of spectrum, using MLR method to simulate Fv′/Fm′ results, other similarities"

Fig. 6

Linear fitting of scattered point diagram between simulated and measured values of qP by using four models Black line in picture: Y=X; MLR- Original spectrum: Based on original spectrum, using MLR method to simulate qP results, other similarities"

Table 3

Simulation results of Amax based on PLS model based on original spectra (different leaf position)"

叶位 Leaf position 建模精度R2 Modeling 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

Table 4

Simulation results of Amax based on PLS model based on original spectra (different growth period)"

生育期 Growth period 建模精度R2 Modeling 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

Table 2

Simulation results summary of photosynthetic characteristic parameters by using MLR, ANN, PLS, and SVM methods"

光谱类型
Type of spectrum		 模拟结果
 Amax Fv′/Fm′ qP
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
[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
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