基于数码相机的玉米冠层SPAD遥感估算

doi:10.3864/j.issn.0578-1752.2018.15.005

摘要/Abstract

摘要： 【目的】叶绿素是植物光合作用中重要的色素。利用作物光谱信息对叶绿素含量进行反演，为作物的实时监测和生长状态诊断提供重要依据。【方法】以大田环境下不同氮肥水平（0，50%和100%）的开花期玉米为研究对象，利用轻小型无人机搭载数码相机，获取试验区RGB影像。使用土壤调整植被指数（soil adjusted vegetation index，SAVIgreen）对图像进行分割，基于分割前后的影像分别提取15种常见的可见光植被指数，综合分析指数与玉米冠层叶绿素相对含量SPAD值的相关关系。采用单变量回归模型、多元逐步回归模型和随机森林（random forest，RF）回归算法构建玉米SPAD值的遥感估算模型，通过模型精度评价指标决定系数（coefficient of determination，R2）、均方根误差（root mean square error，RMSE）、平均相对误差（mean relative error，MRE）和显著性检验水平（P＜0.01），确定最佳指标和最优模型。【结果】基于分割前后的数码影像提取的VIplot和VIplant植被指数与玉米冠层SPAD值之间具有显著的相关关系，其中VIplant中的红光标准化值（NRI）、归一化叶绿素比值植被指数（NPCI）、蓝红比值指数（BRRI）、差值植被指数（DVI）与SPAD值的相关性在0.77以上；以相关性高于0.77的VIplant指数NRI、NPCI、BRRI、DVI构建的线性、指数、对数、二次多项式、幂函数的单变量回归模型中，NRI指数构建的二次多项式模型效果最好，决定系数R2为0.7976，RMSE为4.31，MRE为5.91％。在VIplant指数NRI、NPCI、BRRI、DVI参与建立的多变量SPAD反演模型中，使用随机森林方法的模型精度最高，决定系数R2为0.8682，RMSE为3.92，MRE为4.98%，而多元逐步回归模型的精度高于任意单变量回归模型，决定系数R2为0.819，RMSE为4，MRE为5.67%；对数码影像结合各模型制作的SPAD分布图进行精度分析，使用随机森林回归模型对SPAD的估测值与实测值最为接近，具有最佳的预测效果，R2为0.8247，RMSE为4.3，MRE为5.36％，可以作为玉米冠层叶绿素信息监测的主要方法。【结论】本研究证明将数码相机影像提取的可见光植被指数应用于玉米叶绿素相对含量的估测是可行的，这也为无人机遥感系统在农业方面的应用增添了新的手段和经验。

关键词: 无人机, 数码相机, SPAD值, 随机森林回归算法

Abstract: 【Objective】Chlorophyll is an important pigment in plant photosynthesis. The objective of this study is to investigate the inversion of chlorophyll content using crop spectrum information, so as to provide an important basis for real-time monitoring and diagnosis of crop growth.【Method】Based on the field environment under different nitrogen fertilizer application levels (0, 50% and 100%) of maize, the light and small UAV equipped with consumer level digital camera was used to obtain the RGB image of the test area, and then the soil adjusted vegetation index was used for image segmentation. 15 common visible vegetation indexes were extracted based on images before and after segmentation. Then the correlation between vegetation index and SPAD values were analyzed, besides single variable regression model, multiple regression model and random forest regression model based on visible vegetation indexes were established to estimate the SPAD values. And then, the indicators of accuracy evaluation, coefficient of determination, root mean square error, mean relative error and P<0.01 were used to select the best indicators and the optimal model.【Result】There was a significant correlation between VI_plot and VI_plantvegetation indexes and the SPAD value of maize canopy, for example, the correlation coefficient between normalized redness intensity (NRI), normalized pigment chlorophyll ratio index (NPCI), blue red ratio index (BRRI) and SPAD value of VI_plant was above 0.77. The univariate regression models were built, which took NRI,NPCI, BRRI and DVI as the independent variables and the measured SPAD as dependent variable, including linear, exponential, logarithmic, two degree polynomial and power function models, and among those models, the two polynomial model constructed by the NRI index was the best one with the decision coefficient R² of 0.7976, the RMSE of 4.31, and the MRE of 5.91%; the precision of the model using the random forest regression algorithm was the highest, in which the determining coefficient was 0.8682, the RMSE was 3.92, and the MRE was 4.98%; the multiple regression model had higher accuracy than any single variable regression model, in which the decision coefficient R² was 0.819, RMSE was 4, and MRE was 5.67%. The six inversion models of SPAD were used to make the distribution map of corn canopy SPAD value, and then the map using random forest regression model had the best result which was the closest to real SPAD distribution with R² of 0.8247，RMSE of 4.3，MRE of 5.36％, therefore which could be used as a main method of corn canopy chlorophyll monitoring information.【Conclusion】The results showed that the application of UAV digital imagery in retrieving SPAD of corn was feasible, which also added new means and experience to the application of UAV remote sensing system in agriculture.

Key words: unmanned aerial vehicle, digital camera, SPAD value, random forest regression algorithm

贺英，邓磊，毛智慧，孙杰. 基于数码相机的玉米冠层SPAD遥感估算[J]. 中国农业科学, 2018, 51(15): 2886-2897.

HE Ying, DENG Lei, MAO ZhiHui, SUN Jie. Remote Sensing Estimation of Canopy SPAD Value for Maize Based on Digital Camera[J]. Scientia Agricultura Sinica, 2018, 51(15): 2886-2897.

0
/ / 推荐

导出引用管理器 EndNote|Reference Manager|ProCite|BibTeX|RefWorks

链接本文: https://www.chinaagrisci.com/CN/10.3864/j.issn.0578-1752.2018.15.005

https://www.chinaagrisci.com/CN/Y2018/V51/I15/2886

参考文献

[1] VEROUSTRAETE F, PATYN J, MYNENI R B. Estimating net ecosystem exchange of carbon using the normalized difference vegetation index and an ecosystem model. Remote Sensing of Environment,1996, 58(1): 115-130.

[2] 丁希斌, 刘飞, 张初, 何勇. 基于高光谱成像技术的油菜叶片SPAD值检测. 光谱学与光谱分析, 2015, 35(2): 486-491.

DING X B, LIU F, ZHANG C, HE Y. Prediction of SPAD value in oilseed rape leaves using hyperspectral imaging technique. Spectroscopy and Spectral Analysis (in Chinese), 2015, 35(2): 486-491.

[3] MARENCO R A, ANTEZANAVERA S A, NASCIMENTO H C S. Relationship between specific leaf area, leaf thickness, leaf water content and SPAD-502 readings in six Amazonian tree species. Photosynthetica, 2009, 47(2): 184-190.

[4] PAGOLA M, ORTIZ R, IRIGOYEN I, BUSTINCE H, BARRENECHEA E, APARICIOTEJO P, LAMSFUS C, LASA B. New method to assess barley nitrogen nutrition status based on image colour analysis: comparison with SPAD-502. Computers and Electronics in Agriculture, 2009, 65(2): 213-218.

[5] 李粉玲, 王力, 刘京, 常庆瑞. 基于高分一号卫星数据的冬小麦叶片SPAD值遥感估算. 农业机械学报, 2015, 46(9): 273-281.

LI F L, WANG L, LIU J, CHANG Q R. Remote sensing estimation of SPAD value for wheat leaf based on GF-1 data. Transactions of the Chinese Society for Agricultural Machinery, 2015, 46(9): 273-281. (in Chines)

[6] 夏天, 周清波, 陈仲新, 周勇, 于雷. 基于HJ-1卫星的冬小麦叶片SPAD遥感监测研究. 中国农业资源与区划, 2012, 33(6): 38-44.

XIA T, ZHOU Q B, CHEN Z X, ZHOU Y, YU L. Monitoring winter wheat SPAD based on HJ-1ccd. Chinese Journal of Agricultural Resources and Regional Planning, 2012, 33(6): 38-44. (in Chinese)

[7] ZHANG C, KOVACS J M. The application of small unmanned aerial systems for precision agriculture: a review. Precision Agriculture, 2012, 13(6): 693-712.

[8] PRIMICERIO J, GENNARO S F D, FIORILLO E, GENESIO L, LUGATO E, MATESE A, VACCARI F P. A flexible unmanned aerial vehicle for precision agriculture. Precision Agriculture, 2012, 13(4): 517-523.

[9] CILIA C, PANIGADA C, ROSSINI M, MERONI M, BUSETTO L, AMADUCCI S, BOSCHETTI M, PICCHI V, COLOMBO R. Nitrogen status assessment for variable rate fertilization in maize through hyperspectral imagery. Remote Sensing, 2014, 6(7): 6549-6565.

[10] HUNT E R, DORAISWAMY P C, JAMES E M, CRAIG S T, PERRY E M, AKHMEDOV B. A visible band index for remote sensing leaf chlorophyll content at the canopy scale. International Journal of Applied Earth Observation and Geoinformation, 2013, 21(1): 103-112.

[11] SCHLEMMER M, GITELSON A, SCHEPERS J, FERGUSON R, PENG Y, SHANAHAN J, RUNDQUIST D. Remote estimation of nitrogen and chlorophyll contents in maize at leaf and canopy levels. International Journal of Applied Earth Observation and Geoinformation, 2013, 25(1): 47-54.

[12] 房贤一, 朱西存, 王凌, 赵庚星. 基于高光谱的苹果盛果期冠层叶绿素含量监测研究. 中国农业科学, 2013, 46(16): 3504-3513.

FANG X Y, ZHU X C, WANG L, ZHAO G X. Hyperspectral monitoring of the canopy chlorophyll content at apple tree prosperous fruit stage . Scientia Agricultura Sinica, 2013, 46(16): 3504-3513. (in Chinese)

[13] 张潇元, 张立福, 张霞, 王树东, 田静国, 翟涌光. 不同光谱植被指数反演冬小麦叶氮含量的敏感性研究. 中国农业科学, 2017, 50(3): 474-485.

ZHANG X Y, ZHANG L F, ZHANG X, WANG S D, TIAN J G, ZHAI Y G. Sensitivity of different spectral vegetation index for estimating winter wheat leaf nitrogen. Scientia Agricultura Sinica, 2017, 50(3): 474-485. (in Chinese)

[14] THOMAS J R, GAUSMAN H W. Leaf reflectance vs. Leaf chlorophyll and carotenoid concentrations for eight crops. Agronomy Journal, 1977, 69(5): 799-802.

[15] MADEIRA A C, FERREIRA A, VARENNES A, VIEIRA M I. SPAD meter versus tristimulus colorimeter to estimate chlorophyll content and leaf color in sweet pepper. Communications in Soil Science and Plant Analysis, 2003, 34(17/18): 2461-2470.

[16] JIA L L, CHEN X P, ZHANG F, BUERKERT A, ROMHELD V. Use of digital camera to assess nitrogen status of winter wheat in the northern China plain. Journal of Plant Nutrition, 2004, 27(3): 441-450.

[17] PAGOLA M, IRIGOYEN I, BUSTINCE H, BARRENECHEA E, APARICIO-TEJO P, LAMSFUS C, LASA B. New method to assess barley nitrogen nutrition status based on image colour analysis. Computers and Electronics in Agriculture, 2009, 65(2): 213-218.

[18] 王方永, 王克如, 李少昆, 高世菊, 肖春华, 陈兵, 陈江鲁, 吕银亮, 刁万英. 应用两种近地可见光成像传感器估测棉花冠层叶片氮素状况. 作物学报, 2011, 37(6): 1039-1048.

WANG F Y, WANG K R, LI S K, GAO S J, XIAO C H, CHEN B, CHEN J L, LÜ Y L, DIAO W Y. Estimation of canopy leaf nitrogen status using imaging spectrometer and digital camera in cotton. Acta Agronomica Sinica, 2011, 37(6): 1039-1048. (in Chinese)

[19] YUE J, YANG G, LI C, LI Z, WANG Y, FENG H, XU B. Estimation of winter wheat above-ground biomass using unmanned aerial vehicle-based snapshot hyperspectral sensor and crop height improved models. Remote Sensing, 2017, 9(7): 708-727.

[20] ATZBERGER C. Object-based retrieval of biophysical canopy variables using artificial neural nets and radiative transfer models. Remote Sensing of Environment, 2004, 93(1/2): 53-67.

[21] KARIMI Y, PRASHER S O, MADANI A, KIM S. Application of support vector machine technology for the estimation of crop biophysical parameters using aerial hyperspectral observations. Canadian Biosystems Engineering, 2008, 50: 7-13.

[22] LIU M L, LIU X N, LI M, FANG M H, CHI W X. Neural-network model for estimating leaf chlorophyll concentration in rice under stress from heavy metals using four spectral indices. Biosystems Engineering, 2010, 106(3): 223-233.

[23] KIRA O, LINKER R, GITELSON A. Non-destructive estimation of foliar chlorophyll and carotenoid contents: Focus on informative spectral bands. International Journal of Applied Earth Observation and Geoinformation, 2015, 38: 251-260.

[24] 王丽爱, 马昌, 周旭东, 訾妍, 朱新开, 郭文善. 基于随机森林回归算法的小麦叶片SPAD值遥感估算. 农业机械学报, 2015, 46(1): 259-265.

WANG L A, MA C, ZHOU X D, ZI Y, ZHU X K, GUO W S. Estimation of wheat leaf SPAD value using RF algorithmic model and remote sensing data. Transactions of the Chinese Society for Agricultural Machinery, 2015, 46(1): 259-265. (in Chinese)

[25] 宋晓宇, 王纪华, 杨贵军, 崔贝, 常红. 基于叶片及冠层叶绿素参数的冬小麦籽粒蛋白质含量预测研究. 光谱学与光谱分析, 2014, 34(7): 1917-1921.

SONG X Y, WANG J H, YANG G J, CUI B, CHANG H. Winter wheat GPC estimation based on leaf and canopy chlorophyll parameters. Spectroscopy and Spectral Analysis, 2014, 34(7): 1917-1921. (in Chinese)

[26] 田永超, 朱艳, 曹卫星, 范雪梅, 刘小军. 利用冠层反射光谱和叶片SPAD值预测小麦籽粒蛋白质和淀粉的积累. 中国农业科学, 2004, 37(6): 808-813.

TIAN Y C, ZHU Y, CAO W X, FAN X M, LIU X J. Monitoring protein and starch accumulation in wheat grains with leaf SPAD and canopy spectral reflectance. Scientia Agricultura Sinica, 2004, 37(6): 808-813. (in Chinese)

[27] HONG G W, LUO M R, RHODES P A. A study of digital camera colorimetric characterization based on polynomial modeling. Color Research and Application, 2001, 26(1): 76-84.

[28] LI Y, CHEN D, WALKER C N, ANGUS J F. Estimating the nitrogen status of crops using a digital camera. Field Crops Research, 2010, 118(3): 221-227.

[29] KAWASHIMA S, NAKATANI M. An algorithm for estimating chlorophyll content in leaves using a video camera. Annals of botany, 1998, 81(1): 49-54.

[30] GAMON J A, SURFUS J S. Assessing leaf pigment content and activity with a reflectometer. New Phytologist, 1999, 143(1): 105-117.

[31] VERRELST J, SCHAEPMAN M E, KOETZ B, KNEUBUEHLER M. Angular sensitivity analysis of vegetation indices derived from CHRIS/PROBA data. Remote Sensing of Environment, 2008, 112(5): 2341-2353.

[32] SELLARO R, CREPY M, TRUPKIN S A, KARAYEKOV E, SABRINA B A, ROSSI C, CASAL J J. Cryptochrome as a sensor of the blue/green ratio of natural radiation in arabidopsis. Plant Physiology, 2010, 154(1): 401-409.

[33] 魏全全, 李岚涛, 任涛, 王振, 王少华, 李小坤, 丛日环, 鲁剑巍. 基于数字图像技术的冬油菜氮素营养诊断. 中国农业科学, 2015, 48(19): 3877-3886.

WEI Q Q, LI L T, REN T, WANG Z, WANG S H, LI X K, CONG R H, LU J W. Diagnosing nitrogen nutrition status of winter rapeseed via digital image processing technique. Scientia Agricultura Sinica,

2015, 48(19): 3877-3886. (in Chinese)

[34] 汪小钦, 王苗苗, 王绍强, 吴云东. 基于可见光波段无人机遥感的植被信息提取. 农业工程学报, 2015, 31(5): 152-159.

WANG X Q, WANG M M, WANG S Q, WU Y D. Extraction of vegetation information from visible unmanned aerial vehicle images. Transactions of the Chinese Society of Agricultural Engineering, 2015, 31(5): 152-159. (in Chinese)

[35] 井然, 邓磊, 赵文吉, 宫兆宁. 基于可见光植被指数的面向对象湿地水生植被提取方法. 应用生态学报, 2016, 27(5): 1427-1436.

JING R, DENG L, ZHAO W J, GONG Z N. Object-oriented aquatic vegetation extracting approach based on visible vegetation indices. Chinese Journal of Applied Ecology, 2016, 27(5): 1427-1436. (in Chinese)

[36] PENUELAS J, GAMON J A, FREDEEN A L, MERINO J, FIELD C B. Reflectance indices associated with physiological changes in nitrogen- and water-limited sunflower leaves. Remote Sensing of Environment, 1994, 48(2): 135-146.

[37] HUNT E R, CAVIGELLI M, CST D, MCMURTREY J I, WALTHALL C L. Evaluation of digital photography from model aircraft for remote sensing of crop biomass and nitrogen status. Precision Agriculture, 2005, 6(4): 359-378.

[38] GITELSON A A, KAUFMAN Y J, STARK R, RUNDQUIST D. Novel algorithms for remote estimation of vegetation fraction. Remote Sensing of Environment, 2002, 80(1): 76-87.

[39] BENDIG J, YU K, AASEN H, BOLTEN A, BENNERTZ S, BROSCHEIT J, GNYP MARTIN L, BARETH G. Combining UAV-based plant height from crop surface models, visible, and near infrared vegetation indices for biomass monitoring in barley. International Journal of Applied Earth Observation and Geoinformation, 2015, 39: 79-87.

[40] VOL N. Color indices for weed identification under various soil, residue, and lighting conditions. Transactions of the Asae, 1995, 38(1): 259-269.

[41] 王方永, 王克如, 李少昆, 陈兵, 陈江鲁. 利用数码相机和成像光谱仪估测棉花叶片叶绿素和氮素含量. 作物学报, 2010, 36(11): 1981-1989.

WANG F Y, WANG K R, LI S K, CHEN B, CHEN J L. Estimation of chlorophyll and nitrogen contents in cotton leaves using digital camera and imaging spectrometer. Acta Agronomica Sinica, 2010, 36(11): 1981-1989. (in Chinese)