Retrieving soybean leaf area index from unmanned aerial vehicle hyperspectral remote sensing: Analysis of RF, ANN, and SVM regression models

Remote Sensing

Volumn 9, Issue 4, 2017, Pages

  Yuan, Huanhuan ^a,b,c   Yang, Guijun ^a,c,d   Li, Changchun ^b   Wang, Yanjie ^a,b   Liu, Jiangang ^a,c   Yu, Haiyang ^a,d   Feng, Haikuan ^a,c   Xu, Bo ^a,d   Zhao, Xiaoqing ^a,d   Yang, Xiaodong ^a,c,d  

^a AGRO ENVIRONMENTAL PROTECTION INSTITUTE   (China)

^b HENAN POLYTECHNIC UNIVERSITY   (China)

^c NATIONAL ENGINEERING RESEARCH CENTER FOR INFORMATION TECHNOLOGY IN AGRICULTURE   (China)

^d Beijing Engineering Research Center of Agricultural Internet of Things   (China)

Author keywords

Artificial neural networks; Hyperspectral remote sensing; LAI retrieval; Random forests; Sampling method; Support vector machine

Indexed keywords

DECISION TREES; LEAST SQUARES APPROXIMATIONS; NEURAL NETWORKS; PRINCIPAL COMPONENT ANALYSIS; REGRESSION ANALYSIS; REMOTE SENSING; SUPPORT VECTOR MACHINES; UNMANNED AERIAL VEHICLES (UAV);

HYPERSPECTRAL REMOTE SENSING; LAI RETRIEVAL; MULTIPLE STRAINS; PARTIAL LEAST SQUARES REGRESSION; RANDOM FORESTS; REGRESSION MODEL; SAMPLING METHOD; SUPPORT VECTOR MACHINE REGRESSIONS;

DEEP NEURAL NETWORKS;

EID: 85017612753     PISSN: None     EISSN: 20724292     Source Type: Journal    
DOI: 10.3390/rs9040309     Document Type: Article

References (55)

1. Lugg, D.G.; Sinclair, T.R. Seasonal changes in photosynthesis of field-grown soybean leaflets. 2. Relation to nitrogen content. Photosynthetica 1981, 15, 138-144
2. Burkey, K.O.; Wells, R. Response of soybean photosynthesis and chloroplast membrane function to canopy development and mutual shading. Plant Physiol. 1991, 97, 245-252
3. Boerma, H.R.; Specht, J.E. Soybeans: Improvement, Production and Uses; American Society of Agronomy: Madison, WI, USA, 2004
4. Chen, J.M.; Pavlic, G.; Brown, L.; Cihlar, J.; Leblanc, S.G. Derivation and validation of Canada-wide coarse-resolution leaf area index maps using high-resolution satellite imagery and ground measurements. Remote Sens. Environ. 2002, 80, 165-184
5. Running, S.W.; Nemani, R.R.; Heinsch, F.A.; Zhao, M.; Reeves, M. A continuous satellite-derived measure of global terrestrial primary production. Bioscience 2004, 54, 547-560
6. Breda, N. Ground-based measurements of leaf area index: A review of methods, instruments and current controversies. J. Exp. Bot. 2003, 54, 2403-2417
7. Jonckheere, I.; Fleck, S.; Nackaerts, K.; Muys, B.; Coppin, P.;Weiss, M.; Baret, F. Review of methods for in situ leaf area index determination-Part I. Theories, sensors and hemispherical photography. Agric. For. Meteorol. 2004, 121, 19-35
8. Campos-Taberner, M.; Javier Garcia-Haro, F.; Camps-Valls, G.; Grau-Muedra, G.; Nutini, F.; Crema, A.; Boschetti, M. Multitemporal and multiresolution leaf area index retrieval for operational local rice crop monitoring. Remote Sens. Environ. 2016, 187, 102-118
9. Liu, K.; Zhou, Q.;Wu, W.; Xia, T.; Tang, H. Estimating the crop leaf area index using hyperspectral remote sensing. J. Integr. Agric. 2016, 15, 475-491
10. Li, X.; Zhang, Y.; Bao, Y.; Luo, J.; Jin, X.; Xu, X.; Song, X.; Yang, G. Exploring the best hyperspectral features for LAI estimation using partial least squares regression. Remote Sens. 2014, 6, 6221-6241
11. Liang, L.; Di, L.; Zhang, L.; Deng, M.; Qin, Z.; Zhao, S.; Lin, H. Estimation of crop LAI using hyperspectral vegetation indices and a hybrid inversion method. Remote Sens. Environ. 2015, 165, 123-134
12. Fan, W.J.; Xu, X.R.; Liu, X.C.; Yan, B.Y.; Cui, Y.K. Accurate LAI retrieval method based on PROBA/CHRIS data. Hydrol. Earth Syst. Sci. 2010, 14, 1499-1507
13. Delegido, J.; Verrelst, J.; Rivera, J.P.; Ruiz-Verdu, A.; Moreno, J. Brown and green LAI mapping through spectral indices. Int. J. Appl. Earth Obs. 2015, 35, 350-358
14. Vuolo, F.; Dini, L.; D'Urso, G. Retrieval of leaf area index from CHRIS/PROBA data: An analysis of the directional and spectral information content. Int. J. Remote Sens. 2008, 29, 5063-5072
15. Jihua, M.; Bingfang, W.; Qiangzi, L. Method for estimating crop leaf area index of China using remote sensing. Trans. Chin. Soc. Agric. Eng. 2007, 23, 160-167
16. Verrelst, J.; Camps-Valls, G.; Munoz-Mari, J.; Pablo Rivera, J.; Veroustraete, F.; Clevers, J.G.P.W.; Moreno, J. Optical remote sensing and the retrieval of terrestrial vegetation bio-geophysical properties-A review. ISPRS J. Photogramm. Remote Sens. 2015, 108, 273-290
17. Liang, D.; Guan, Q.; Huang, W.; Huang, L.; Yang, G. Remote sensing inversion of leaf area index based on support vector machine regression in winter wheat. Trans. Chin. Soc. Agric. Eng. 2013, 29, 117-123
18. Meroni, M.; Colombo, R.; Panigada, C. Inversion of a radiative transfer model with hyperspectral observations for LAI mapping in poplar plantations. Remote Sens. Environ. 2004, 92, 195-206
19. Duan, S.; Li, Z.;Wu, H.; Tang, B.; Ma, L.; Zhao, E.; Li, C. Inversion of the PROSAIL model to estimate leaf area index of maize, potato, and sunflower fields from unmanned aerial vehicle hyperspectral data. Int. J. Appl. Earth Obs. 2014, 26, 12-20
20. Herrmann, I.; Pimstein, A.; Karnieli, A.; Cohen, Y.; Alchanatis, V.; Bonfil, D.J. LAI assessment of wheat and potato crops by VENμS and Sentinel-2 bands. Remote Sens. Environ. 2011, 115, 2141-2151
21. Megainduction, J.C. Machine Leaming on Very Large Database; School of Computer Science, University of Technology: Sydney, Australia, 1991
22. Darvishzadeh, R.; Skidmore, A.; Schlerf, M.; Atzberger, C.; Corsi, F.; Cho, M. LAI and chlorophyll estimation for a heterogeneous grassland using hyperspectral measurements. ISPRS J. Photogramm. Remote Sens. 2008, 63, 409-426
23. Han, Z.Y.; Zhu, X.C.; Fang, X.Y.;Wang, Z.Y.;Wang, L.; Zhao, G.X.; Jiang, Y.M. Hyperspectral estimation of apple tree canopy LAI based on SVM and RF regression. Spectrosc. Spectr. Anal. 2016, 36, 800-805
24. Omer, G.; Mutanga, O.; Abdel-Rahman, E.M.; Adam, E. Empirical prediction of Leaf Area Index (LAI) of endangered tree species in intact and fragmented indigenous forests Ecosystems usingWorldView-2 data and two robust machine learning algorithms. Remote Sens. 2016, 8, 324
25. Verrelst, J.; Munoz, J.; Alonso, L.; Delegido, J.; Pablo Rivera, J.; Camps-Valls, G.; Moreno, J. Machine learning regression algorithms for biophysical parameter retrieval: Opportunities for Sentinel-2 and-3. Remote Sens. Environ. 2012, 118, 127-139
26. Mustafa, Y.T.; Van Laake, P.E.; Stein, A. Bayesian network modeling for improving forest growth estimates. IEEE Trans. Geosci. Remote 2011, 49, 639-649
27. Bo, Q.I.; Zhang, N.; Zhao, T.J.; Xing, G.N.; Zhao, J.M.; Gai, J.Y. Prediction of leaf area index using hyperspectral remote sensing in breeding programs of soybean. Acta Agron. Sin. 2015, 41, 1073
28. Wong, T. Parametric methods for comparing the performance of two classification algorithms evaluated by k-fold cross validation on multiple data sets. Pattern Recogn. 2017, 65, 97-107
29. Mingliang, L.I.; Yang, D.; Chen, J. Probabilistic flood forecasting by a sampling-based Bayesian model. J. Hydroelectr. Eng. 2011, 30, 27-33
30. Cochran, W.G. Sampling Technique; China Statistical Publishing House: Beijing, China, 1985
31. Aasen, H.; Burkart, A.; Bolten, A.; Bareth, G. Generating 3D hyperspectral information with lightweight UAV snapshot cameras for vegetation monitoring: From camera calibration to quality assurance. ISPRS J. Photogramm. 2015, 108, 245-259
32. Li, Z.;Wang, J.; Tang, H.; Huang, C.; Yang, F.; Chen, B.;Wang, X.; Xin, X.; Ge, Y. Predicting grassland leaf area index in the Meadow Steppes of Northern China: A comparative study of regression approaches and hybrid geostatistical methods. Remote Sens. 2016, 8, 632
33. Wang, J.; Zhao, C.; Huang, W. Foundations and Applications of Quantitative Remote Sensing in Agriculture, 1st ed.; Science Press: Beijing, China, 2008; pp. 156-157
34. Demetriades-Shah, T.H.; Steven, M.D.; Clark, J.A. High resolution derivative spectra in remote sensing. Remote Sens. Environ. 1990, 33, 55-64
35. Frigge, M.; Hoaglin, D.C.; Iglewicz, B. Some implementations of the boxplot. Am. Stat. 1989, 43, 50-54. [CrossRef]
36. Benjamini, Y. Opening the box of a boxplot. Am. Stat. 1988, 42, 257-262
37. Cheng, T.; Riaño, D.; Koltunov, A.; Whiting, M.L.; Ustin, S.L.; Rodriguez, J. Detection of diurnal variation in orchard canopy water content using MODIS/ASTER Airborne Simulator (MASTER) data. Remote Sens. Environ. 2013, 132, 1-12
38. Sandelowski, M. Sample size in qualitative research. Res. Nurs. Health 1995, 18, 179-183. [PubMed]
39. Breiman, L. Random forests. Mach. Learn. 2001, 45, 5-32
40. Krishnan, P.; Alexander, J.D.; Butler, B.J.; Hummel, J.W. Reflectance technique for predicting soil organic matter. Soil Sci. Soc. Am. J. 1980, 44, 1282-1285
41. Liaw, A.;Wiener, M. Classification and regression by randomForest. R News 2002, 2, 18-22
42. He, Q. Neural Network and its Application in IR; Graduate School of Library and Information Science, University of Illinois at Urbana-Champaign Spring: Champaign, IL, USA, 1999
43. Kimes, D.S.; Nelson, R.F.; Manry, M.T.; Fung, A.K. Review article: Attributes of neural networks for extracting continuous vegetation variables from optical and radar measurements. Int. J. Remote Sens. 1998, 19, 2639-2663
44. R Core Team. R: A Language and Environment for Statistical Computing; R Foundation for Statistical Computing: Vienna, Austria, 2011
45. Cortes, C.; Vapnik, V. Support-vector networks. Mach. Learn. 1995, 20, 273-297
46. Vapnik, V.N.; Vapnik, V. Statistical Learning Theory; Wiley: New York, NY, USA, 1998; Volume 1
47. Si, W.; Amari, S.I. Conformal transformation of Kernel functions: A data-dependent way to improve support vector machine classifiers. Neural Process. Lett. 2002, 15, 59-67
48. Dimitriadou, E.; Hornik, K.; Leisch, F.; Meyer, D.; Weingessel, A. The R Project for Statistical Computing. Available online: http://www.r-project.org (accessed on 6 April 2005)
49. Geladi, P.; Kowalski, B.R. Partial least-squares regression: A tutorial. Anal. Chim. Acta 1986, 185, 1-17. [CrossRef]
50. Geisser, S. A predictive approach to the random effect model. Biometrika 1974, 61, 101-107
51. Wolter, P.T.; Townsend, P.A.; Sturtevant, B.R.; Kingdon, C.C. Remote sensing of the distribution and abundance of host species for spruce budworm in Northern Minnesota and Ontario. Remote Sens. Environ. 2008, 112, 3971-3982
52. Mevik, B.;Wehrens, R. The pls package: Principal component and partial least squares regression in R. J. Stat. Softw. 2007, 18, 1-24
53. Tedeschi, L.O. Assessment of the adequacy of mathematical models. Agric. Syst. 2006, 89, 225-247
54. Tu, J.V. Advantages and disadvantages of using artificial neural networks versus logistic regression for predicting medical outcomes. J. Clin. Epidemiol. 1996, 49, 1225-1231
55. Deng, H.; Runger, G.; Tuv, E. Bias of importance measures for multi-valued attributes and solutions. In Proceedings of the International Conference on Artificial Neural Networks, Espoo, Finland, 14-17 June 2011; pp. 293-300