1. CAPÍTULO I
1.8. Operacionalización de las variables
2.2.7. Estrategias de promoción
The predictive accuracy of models for deriving Chla, Chlb and Cars from leaf
607
reflectance spectra using different multivariate techniques is summarized in Table 5. For 608
both spectral domains that were tested, PLSR and PCR had similarly high predictive 609
accuracies for Chla and Chlb. For Cars, PLSR and PCR exhibited similar predictive
610
accuracies when applied to the 400-900 nm domain, whereas for the 400-2400 nm domain, 611
PLSR had a higher predictive accuracy than that of PCR. When comparing the predictive 612
ability of the SVMs to the aforementioned methods, it can be observed that the PLSR 613
method produced superior predictive accuracies over SVMs for all leaf pigments for both 614
spectral domains. SVMs only achieved superiority over the PCR method in the estimation 615
of Cars when applied to the 400-900 nm spectral domain. Different methods displayed
616
different levels of effectiveness in the estimation of different biochemical parameters 617
which may be due to variations in the applicability of each algorithm. Similar to the 618
vegetation index results, the estimation accuracies for Chlb were poor for all three 619
methods and two spectral domains. This is likely due to the changes in Chlb during 620
post-thawing being minimal (R2 = 0.18).
621
The estimation accuracy of each technique in the spectral domain 400-900 nm 622
outperformed the full spectrum for the prediction of all three pigments. Although this 623
optimal domain was only proposed for chlorophylls in the study of Huang & Blackburn 624
(2011), this domain is still optimal for all the individual pigments in our study using the 625
three multivariate regression techniques. This confirms that the 400-900 nm region is 626
most informative for the estimation of leaf pigment content. 627
Table 5. Cross-validation statistics in deriving Chla, Chlb and Cars from leaf spectral reflectance
628
using three multivariate analysis techniques and two different spectral domains.
629 Variable Method Spectral domains 400-2400 nm 400-900 nm R2cv RMSEcv (mg/cm2) RRMSEcv R2cv RMSEcv (mg/cm2) RRMSEcv Chla PLSR 0.29 0.0060 0.9836 0.36 0.0056 0.9254 PCR 0.30 0.0062 1.0171 0.36 0.0056 0.9254 SVMs 0.09 0.0070 1.1560 0.33 0.0058 0.9452 Chlb PLSR 0.05 0.0019 2.1668 0.06 0.0018 2.0599 PCR 0.05 0.0019 2.1668 0.06 0.0018 2.0599 SVMs 0.06 0.0021 2.4141 0.01 0.0020 2.2723 Car PLSR 0.41 0.0023 0.4883 0.46 0.0021 0.4508 PCR 0.21 0.0031 0.6638 0.46 0.0021 0.4508 SVMs 0.38 0.0023 0.4824 0.47 0.0021 0.4450 4. Conclusions 630
In this study, we analyzed the changes in the spectral reflectance of oilseed rape 631
leaves that were subjected to freezing and post-thawing processes. We explored the 632
potential for using changes in spectral reflectance to detect the different stages of the 633
freezing and post-thawing processes, and to quantify the biochemical impacts of freezing 634
injury. The main findings are summarized as follows: 635
(1) The reflectance of leaves shows a significant decrease during freezing and then 636
remains constant across the optical spectrum as the freezing period continues. The 637
most significant spectral characteristic is that water absorption features shift towards 638
longer wavelengths, which is caused by the change of state of leaf water from liquid 639
to solid. 640
(2) In the process of post-thawing, the changes in spectral reflectance of leaves can 641
mainly be attributed to the changing water content of the leaf and the subsequent 642
changes in pigment content and cellular structure. 643
(3) SDR spectra exhibited the highest potential for discriminating leaves at different 644
stages of post-thawing from normal leaves. 645
(4) Derivative spectral indices formulated using optimized narrow wavebands were 646
most effective in quantifying the changes in pigment and water contents of leaves 647
subjected to freezing injury. 648
(5) In freezing injured leaves, the spectral domain 400-900 nm is optimal for 649
developing predictive models of pigment contents. Therefore, selection of this 650
spectral domain for analysis could reduce redundancy and increase computational 651
efficiency in future operational remote sensing scenarios. 652
This study focused on oilseed rape at the leaf scale in a laboratory setting. The results 653
provide evidence to establish the use of spectral reflectance for identifying different 654
stages of freezing injury in crops and quantifying the biochemical impacts of the process. 655
Further work is now needed to develop this capability by investigating freezing injury in 656
other crop species at the canopy and field scales using airborne and spaceborne 657
hyperspectral remotely-sensed data. Such developments have a considerable potential to 658
improve the monitoring and loss evaluation of freezing injury in crops when remotely 659
sensed data of sufficiently high spatial, temporal and spectral resolution are available. 660
661
Acknowledgements
662
This study was supported by the National Natural Science Foundation of China 663
(41171276) and the special fund for industrial and scientific research in the public interest 664
(Meteorology) (GYHY201406028). The authors appreciate the valuable comments from 665 anonymous reviewers. 666 667 References 668
Adjorlolo, C., Mutanga, O., & Cho, M.A. (2015). Predicting C3 and C4 grass nutrient variability 669
using in situ canopy reflectance and partial least squares regression. International Journal of Remote
670
Sensing, 36, 1743-1761. 671
Bak, J., Kim, J.H., Liu, X., Chance, K., & Kim, J. (2013). Evaluation of ozone profile and tropospheric 672
ozone retrievals from GEMS and OMI spectra. Atmospheric Measurement Techniques, 6, 239-249. 673
Behmann, J., Steinruecken, J., & Pluemer, L. (2014). Detection of early plant stress responses in 674
hyperspectral images. ISPRS Journal of Photogrammetry and Remote Sensing, 93, 98-111. 675
Berger, M., Moreno, J., Johannessen, J.A., Levelt, P.F., & Hanssen, R.F. (2012). ESA's sentinel 676
missions in support of earth system science. Remote Sensing of Environment, 120, 84-90. 677
Blackburn, G.A. (1998). Spectral indices for estimating photosynthetic pigment concentrations: A test 678
using senescent tree leaves. International Journal of Remote Sensing, 19, 657-675. 679
Board, S.S. (2007). Earth science and applications from space: National imperatives for the next 680
decade and beyond. Washington, DC: National Academies Press. 681
Boser, B.E., Guyon, I.M., & Vapnik, V.N. (1992). A training algorithm for optimal margin classifiers. 682
Proceedings of the Fifth Annual Workshop on Computational LearningTheory, p.144-152, Pittsburgh, 683
Pennsylvania, USA. 684
Brown, M.S., Pereira, E.S.B., & Finkle, B.J. (1974). Freezing of nonwoody plant tissues Ⅱ. Cell 685
damage and fine-structure of freezing curves. Plant physiology, 53, 709-711. 686
Burke, M.J., Gusta, L.V., Quamme, H.A., Weiser, C.J., & Li, P.H. (1976). Freezing and injury in plants. 687
Annual Review of Plant Physiology and Plant Molecular Biology, 27, 507-528. 688
Carter, G.A. (1991). Primary and secondary effects of water content on the spectral reflectance of 689
leaves. American Journal of Botany, 78, 916-924. 690
Ceccato, P., Gobron, N., Flasse, S., Pinty, B., & Tarantola, S. (2002). Designing a spectral index to 691
estimate vegetation water content from remote sensing data: Part 1 - theoretical approach. Remote Sensing
of Environment, 82, 188-197. 693
Chang, C.C., & Lin, C.J. (2011). Libsvm: A library for support vector machines. 694
http://www.csie.ntu.edu.tw/~cjlin/libsvm/. 695
Clark, R.N., & Roush, T.L. (1984). Reflectance spectroscopy quantitative analysis techniques for 696
remote sensing applications. Journal of Geophysical Research, 89, 6329-6340. 697
Cortes, C., & Vapnik, V. (1995). Support vector networks. Machine Learning, 20, 273-297. 698
Cromey, M.G., Wright, D.S.C., & Boddington, H.J. (1998). Effects of frost during grain filling on 699
wheat yield and grain structure. New Zealand Journal of Crop and Horticultural Science, 26, 279-290. 700
Croser, J.S., Clarke, H.J., Siddique, K.H.M., & Khan, T.N. (2003). Low temperature stress: 701
Implications for chickpea (cicer arietinum l.) improvement. Critical Reviews in Plant Sciences, 22, 185-219. 702
Curran, P.J. (1989). Remote sensing of foliar chemistry. Remote Sensing of Environment, 30, 271-278. 703
Danson, F.M., Steven, M.D., Malthus, T.J., & Clark, J.A. (1992). High-spectral resolution data for 704
determining leaf water content. International Journal of Remote Sensing, 13, 461-470. 705
Darvishzadeh, R., Skidmore, A., Schlerf, M., & Atzberger, C. (2008). Inversion of a radiative transfer 706
model for estimating vegetation LAI and chlorophyll in a heterogeneous grassland. Remote Sensing of
707
Environment, 112, 2592-2604. 708
Foody, G.M. (2002). Status of land cover classification accuracy assessment. Remote Sensing of
709
Environment, 80, 185-201. 710
Foody, G.M., & Mathur, A. (2004). A relative evaluation of multiclass image classification by support 711
vector machines. IEEE Transactions on Geoscience and Remote Sensing, 42, 1335-1343. 712
Galvao, L.S., dos Santos, J.R., Roberts, D.A., Breunig, F.M., Toomey, M., & de Moura, Y.M. (2011). 713
On intra-annual EVI variability in the dry season of tropical forest: A case study with MODIS and 714
hyperspectral data. Remote Sensing of Environment, 115, 2350-2359. 715
Gamon, J.A., Field, C.B., Bilger, W., Bjorkman, O., Fredeen, A.L., & Penuelas, J. (1990). Remote 716
sensing of the xanthophyll cycle and chlorophyll fluorecence in sunflower leaves and canopies. Oecologia,
717
85, 1-7. 718
Gamon, J.A., & Surfus, J.S. (1999). Assessing leaf pigment content and activity with a reflectometer. 719
New Phytologist, 143, 105-117. 720
Gao, L., Li, J., Khodadadzadeh, M., Plaza, A., Zhang, B., He, Z., & Yan, H. (2015). Subspace-based 721
support vector machines for hyperspectral image classification. IEEE Geoscience and Remote Sensing
722
Letters, 12, 349-353. 723
Gausman, H.W., Burke, J.J., & Quisenberry, J.E. (1984). Use of leaf optical properties in plant stress 724
research. ACS Symposium Series, 257, 215-233. 725
Gleason, C.J., & Im, J. (2012). Forest biomass estimation from airborne LiDAR data using machine 726
learning approaches. Remote Sensing of Environment, 125, 80-91. 727
Gonzalez-Fernandez, A.B., Rodriguez-Perez, J.R., Marabel, M., & Alvarez-Taboada, F. (2015). 728
Spectroscopic estimation of leaf water content in commercial vineyards using continuum removal and 729
partial least squares regression. Scientia Horticulturae, 188, 15-22. 730
Gowen, A.A., Taghizadeh, M., & O'Donnell, C.P. (2009). Identification of mushrooms subjected to 731
freeze damage using hyperspectral imaging. Journal of Food Engineering, 93, 7-12. 732
Green, R.O., Painter, T.H., Roberts, D.A., & Dozier, J. (2006). Measuring the expressed abundance of 733
the three phases of water with an imaging spectrometer over melting snow. Water Resources Research, 42.
734
Guy, C.L. (1990). Cold acclimation and freezing stress tolerance: Role of protein metabolism. Annual
735
Review of Plant Physiology and Plant Molecular Biology, 41, 187-223. 736
Hichri, H., Bazi, Y., Alajlan, N., & Malek, S. (2013). Interactive segmentation for change detection in 737
multispectral remote sensing images. IEEE Geoscience and Remote Sensing Letters, 10, 298-302. 738
Hong, G., Wang, S., Li, J., & Huang, J. (2015). Fully polarimetric synthetic aperture radar (SAR) 739
processing for crop type identification. Photogrammetric Engineering & Remote Sensing, 81, 109-117. 740
Huang, J.F., & Blackburn, G.A. (2011). Optimizing predictive models for leaf chlorophyll 741
concentration based on continuous wavelet analysis of hyperspectral data. International Journal of Remote
742
Sensing, 32, 9375-9396. 743
Hussain, M., Chen, D., Cheng, A., Wei, H., & Stanley, D. (2013). Change detection from remotely 744
sensed images: From pixel-based to object-based approaches. ISPRS Journal of Photogrammetry and
745
Remote Sensing, 80, 91-106. 746
Inoue, Y., Sakaiya, E., Zhu, Y., & Takahashi, W. (2012). Diagnostic mapping of canopy nitrogen 747
content in rice based on hyperspectral measurements. Remote Sensing of Environment, 126, 210-221. 748
Jensen, J.R. (2006). Remote sensing of the environment: An earth resource perspective. (2nd Edition 749
ed.). New Jersey: Prentice Hall, (Chapter 11). 750
Jordan, C.F. (1969). Derivation of leaf area index from quality of light on forest floor. Ecology, 50, 751
663-666. 752
Knipling, E.B. (1970). Physical and physiological basis for the reflectance of visible and near-infrared 753
radiation from vegetation. Remote Sensing of Environment, 155-159. 754
Kooistra, L., Salas, E.A., Clevers, J.G., Wehrens, R., Leuven, R.S., Nienhuis, P.H., et al. (2004). 755
Exploring field vegetation reflectance as an indicator of soil contamination in river floodplains. 756
Environmental Pollution, 127, 281-290. 757
Lardon, A., & TriboiBlondel, A.M. (1995). Cold and freeze stress at flowering: Effects on seed yields 758
in winter rapeseed. Field Crops Research, 44, 95-101. 759
Lichtenthaler, H.K. (1987). Chlorophylls and carotenoids: Pigments of photosynthetic biomembranes. 760
Methods in Enzymology, 148, 350-382. 761
Liu, Z., Huang, F., Li, L., & Wan, E. (2002). Dynamic monitoring and damage evaluation of flood in 762
north-west jilin with remote sensing. International Journal of Remote Sensing, 23, 3669-3679. 763
Mantero, P., Moser, G., & Serpico, S.B. (2005). Partially supervised classification of remote sensing 764
images through SVM-based probability density estimation. IEEE Transactions on Geoscience and Remote
765
Sensing, 43, 559-570. 766
Mevik, B.H., & Wehrens, R. (2007). The pls package: Principal component and partial least squares 767
regression in R. Journal of Statistical Software, 18, 1-23. 768
Mirzaie, M., Darvishzadeh, R., Shakiba, A., Matkan, A.A., Atzberger, C., & Skidmore, A. (2014). 769
Comparative analysis of different uni- and multi-variate methods for estimation of vegetation water content 770
using hyper-spectral measurements. International Journal of Applied Earth Observation and
771
Geoinformation, 26, 1-11. 772
Nemmour, H., & Chibani, Y. (2006). Multiple support vector machines for land cover change 773
detection: An application for mapping urban extensions. ISPRS Journal of Photogrammetry and Remote
774
Sensing, 61, 125-133. 775
Nicotra, A.B., Hofmann, M., Siebke, K., & Ball, M.C. (2003). Spatial patterning of pigmentation in 776
evergreen leaves in response to freezing stress. Plant Cell and Environment, 26, 1893-1904. 777
Penuelas, J., Filella, I., Biel, C., Serrano, L., & Save, R. (1993). The refectance at the 950-970 nm 778
region as an indicator of plant water status. International Journal of Remote Sensing, 14, 1887-1905. 779
Price, J.C. (1994). How unique are spectral signatures?. Remote Sensing of Environment, 49, 181-186. 780
Richardson, A.D., Duigan, S.P., & Berlyn, G.P. (2002). An evaluation of noninvasive methods to 781
estimate foliar chlorophyll content. New Phytologist, 153, 185-194. 782
Rouse, J.W., Haas, R.H., & Schell, J.A. (1974). Monitoring vegetation systems in the great plains with 783
ERTS, NASA SP-351. Third ERTS-1 Symposium, Vol. 1, pp. 309–317, NASA, Washington, DC. 784
Sankaran, S., Mishra, A., Ehsani, R., & Davis, C. (2010). A review of advanced techniques for 785
detecting plant diseases. Computers and Electronics in Agriculture, 72, 1-13. 786
Savitzky, A., & Golay, M.J.E. (1964). Smoothing and differentiation of data by simplified least squares 787
procedures. Analytical Chemistry, 36, 1627-1639. 788
She, B., Huang, J.f., Guo, R.f., Wang, H.b., & Wang, J. (2015). Assessing winter oilseed rape freeze 789
injury based on Chinese HJ remote sensing data. Journal of Zhejiang University Science B, 16, 131-144. 790
Sims, D.A., & Gamon, J.A. (2002). Relationships between leaf pigment content and spectral 791
reflectance across a wide range of species, leaf structures and developmental stages. Remote Sensing of
792
Environment, 81, 337-354. 793
Slaton, M.R., Hunt, E.R., & Smith, W.K. (2001). Estimating near-infrared leaf reflectance from leaf 794
structural characteristics. American Journal of Botany, 88, 278-284. 795
Smola, A.J., Murata, N., Schölkopf, B., & Müller, K.R. (1998). Asymptotically optimal choice of 796
ε-loss for support vector machines. Proceedings of the International Conference on Artificial Neural 797
Networks, (pp. 105−110). Berlin: Springer. http://citeseer.ist.psu.edu/26614.html. 798
Staggenborg, S.A., & Vanderlip, R.L. (1996). Sorghum grain yield reductions caused by duration and 799
timing of freezing temperatures. Agronomy Journal, 88, 473-477. 800
Strachan, I.B., Pattey, E., & Boisvert, J.B. (2002). Impact of nitrogen and environmental conditions on 801
corn as detected by hyperspectral reflectance. Remote Sensing of Environment, 80, 213-224. 802
Team, R.C. (2014). R: A language and environment for statistical computing. R Foundation for 803
Statistical Computing: Vienna, Austria. 804
Thenkabail, P.S., Smith, R.B., & Pauw, E.D. (2000). Hyperspectral vegetation indices and their 805
relationships with agricultural crop characteristics. Remote Sensing of Environment, 71, 158-182. 806
Thyholt, K., & Isaksson, T. (1997). Differentiation of frozen and unfrozen beef using near-infrared 807
spectroscopy. Journal of the Science of Food and Agriculture, 73, 525-532. 808
Tsai, F., & Philpot, W. (1998). Derivative analysis of hyperspectral data. Remote Sensing of
809
Environment, 66, 41-51. 810
Vapnik, V.N. (1998). Statistical learning theory. New York: Wiley-Interscience. 811
Verrelst, J., Munoz, J., Alonso, L., Delegido, J., Pablo Rivera, J., Camps-Valls, G., et al. (2012). 812
Machine learning regression algorithms for biophysical parameter retrieval: Opportunities for Sentinel-2 813
and -3. Remote Sensing of Environment, 118, 127-139. 814
Wang, H.f., Huo, Z.g., Zhou, G.s., Liao, Q.h., Feng, H.k., & Wu, L. (2016). Estimating leaf spad values 815
of freeze-damaged winter wheat using continuous wavelet analysis. Plant Physiology and Biochemistry, 98, 816
39-45. 817
Wang, H.F., Wang, J.H., Wang, Q., Miao, N.Z., Huang, W.J., Feng, H.K., et al. (2012). Hyperspectral 818
characteristics of winter wheat under freezing injury stress and LWC inversion model. First international 819
conference on agro-geoinformatics. pp. 141-146,Shanghai, China. 820
Xin, Z., & Browse, J. (2000). Cold comfort farm: The acclimation of plants to freezing temperatures. 821
Plant Cell and Environment, 23, 893-902. 822
Yang, X., Huang, J., Wu, Y., Wang, J., Wang, P., Wang, X., et al. (2011). Estimating biophysical 823
parameters of rice with remote sensing data using support vector machines. Science China Life Sciences, 54, 824
272-281. 825
Ye, X., Sakai, K., Sasao, A., & Asada, S.i. (2008). Potential of airborne hyperspectral imagery to 826
estimate fruit yield in citrus. Chemometrics and Intelligent Laboratory Systems, 90, 132-144. 827
Yi, Q.X., Bao, A.M., Wang, Q., & Zhao, J. (2013). Estimation of leaf water content in cotton by means 828
of hyperspectral indices. Computers and Electronics in Agriculture, 90, 144-151. 829
Yu, K., Lenz-Wiedemann, V., Chen, X., & Bareth, G. (2014). Estimating leaf chlorophyll of barley at 830
different growth stages using spectral indices to reduce soil background and canopy structure effects. ISPRS
831
Journal of Photogrammetry and Remote Sensing, 97, 58-77. 832
Zhai, Y.F., Cui, L.J., Zhou, X., Gao, Y., Fei, T., & Gao, W.X. (2013). Estimation of nitrogen, 833
phosphorus, and potassium contents in the leaves of different plants using laboratory-based visible and 834
near-infrared reflectance spectroscopy: Comparison of partial least square regression and support vector 835
machine regression methods. International Journal of Remote Sensing, 34, 2502-2518. 836
Zhang, C., Wang, T., Atkinson, P.M., Pan, X., & Li, H. (2015). A novel multi-parameter support vector 837
machine for image classification. International Journal of Remote Sensing, 36, 1890-1906. 838
Zhang, X., Zhang, C., Liao, X., & Wang, H. (2008). Investigation on 2008 low temperature and freeze 839
injury on winter rape along Yangtze river. Chinese Journal of Oil Crop Sciences, 30, 122-126. 840
841
List of Figure Captions 842
Fig. 1. Minimum and maximum daily temperatures in Hangzhou, China; (a) between 843
October 2013 and December 2013, and (b) between October 2014 and February 2015. 844
The treatment dates are indicated by gray vertical bars. Closed and open circles represent 845
maximum and minimum temperature, respectively. 846
Fig. 2. Changes of leaf temperature during freezing treatment. 847
Fig. 3. Average SPAD values of oilseed rape leaves at the normal, supercooling, 1hr of 848
freezing and at different hours of post-thawing. The bars represent ± 95% confidence 849
interval (CI) (n=26). 850
Fig. 4. Light microscopic images (a, b) and transmission electron micrographs (c, d) 851
showing transverse section of normal (a, c) and freeze-damaged (b, d) oilseed rape leaves. 852
Fig.5. Response of leaf reflectance spectra at different phases of freezing injury: (a) 853
changes in average reflectance with freezing; (b) changes in average reflectance with