ESTONIAN ACADEMY
PUBLISHERS
eesti teaduste
akadeemia kirjastus
PUBLISHED
SINCE 1952
 
Proceeding cover
proceedings
of the estonian academy of sciences
ISSN 1736-7530 (Electronic)
ISSN 1736-6046 (Print)
Impact Factor (2020): 1.045

COMSPECT: a compact model for green vegetation reflectance spectra in the 400–900 nm wavelength range; pp. 277–286

Full article in PDF format | 10.3176/proc.2020.4.01

Authors
Andres Udal, Martin Jürise, Jaanus Kaugerand, Raivo Sell

Abstract

A compact empirical model for approximate description of green vegetation reflectance (GVR) spectra in the visible and near infrared wavelength range from 400 to 900 nm is proposed. The aim is to simplify the development of cyber-physical systems for forestry, agriculture, military, and environmental monitoring where distinguishing of artificial objects from the natural background is needed. Based on hyperspectrally measured spectra and simulations with PROSPECT-D and PROSAIL bio-optical leaf and canopy models, a compact model with only a few setup points at significant wavelength values is stated. After assigning the reference unit value to the chlorophyll-caused 670 nm minimum, only four easily understood tuning parameters will define the overall view of the GVR spectrum. Fermi-Dirac distribution like sigmoid step functions and Gaussian functions are used as building blocks to describe the most important spectrum features: flat or slanted ground level, green apex, red edge step, and infrared plateau. The fitting of the common nine wavelength-related parameters and of the four sample-dependent amplitude parameters was performed on the basis of seven data sets measured by a hyperspectral camera and compact spectrograph. As an application example, assessment of the quality of the military masking colour RAL 6031 is presented. The results obtained show that in the case of maximally compact formulation, a reasonable accuracy can be achieved even if only two parameters characterizing the relative heights of the green apex and the red edge step are used.


References

1. Jacquemoud, S. and Ustin, S. Leaf Optical Properties. Cambridge University Press, Cambridge, 2019.
https://doi.org/10.1017/9781108686457

2. Govender, M., Chetty, K., and Bulcock, H. A. A review of hyperspectral remote sensing and its application in vegetation and water resource studies. Water SA, 2007, 33(2), 145–152.
https://doi.org/10.4314/wsa.v33i2.49049

3. Briottet, X., Boucher, Y., Dimmeler, A., Malaplate, A., Cini, A., Diani, M., Bekman, H., Schwering, P., Skauli, T., Kasen, I., Renhorn, I., Klasén, L., Gilmore, M., and Oxford, D. Military applications of hyperspectral imagery. In Proceedings of SPIE, Targets and Backgrounds XII: Characterization and Representation, 2006, 6239.
https://doi.org/10.1117/12.672030

4. Vagni, F. Survey of Hyperspectral and Multispectral Imaging Technologies. NATO Technical Report TR-SET-065-P3. NATO Research and Technology Organization, Neuilly-sur-Seine Cedex, France, 2007.

5. Pu, R. Hyperspectral Remote Sensing: Fundamentals and Practices. CRC Press, Boca Raton, FL, USA, 2017.
https://doi.org/10.1201/9781315120607

6. Thenkabail, P. S., Lyon, J. G., and Huete, A. (eds). Hyper­spectral Remote Sensing of Vegetation. Second Edition, Four Volume Set. CRC Press, 2018.
https://doi.org/10.1201/9781315164151-1

7. Jacquemoud, S., Verhoef, W., Baret, F., Bacour, C., Zarco-Tejada, P. J., Asner, G. P., François, C., and Ustin, S. L. PROSPECT + SAIL models: a review of use for vegetation characterization. Remote Sens. Environ., 2009, 113 (Supple­ment 1), S56–S66. 
https://doi.org/10.1016/j.rse.2008.01.026

8. Kuusk, A. Canopy radiative transfer modelling. In Comprehensive Remote Sensing (Shunlin Liang, ed.). Elsevier, 2018, 9−22.
https://doi.org/10.1016/B978-0-12-409548-9.10534-2

9. Jacquemoud, S. and Baret, F. PROSPECT: a model of leaf optical properties spectra. Remote Sens. Environ., 1990, 34(2), 75–91.
https://doi.org/10.1016/0034-4257(90)90100-Z

10. Féret, J. B., François C., Asner, G. P., Gitelson, A. A., Martin, R. E., Bidel, L. P. R, le Maire, G., and Jacquemoud, S. PROSPECT-4 and 5: advances in the leaf optical properties model separating photosynthetic pigments. Remote Sens. Environ., 2008, 112(6), 3030–3043.
https://doi.org/10.1016/j.rse.2008.02.012

11. Féret, J. B., Gitelson, A. A., Noble, S. D., and Jacquemoud, S. PROSPECT-D: towards modeling leaf optical properties through a complete lifecycle. Remote Sens. Environ., 2017, 193, 204–215.
https://doi.org/10.1016/j.rse.2017.03.004

12. Verhoef, W. Light scattering by leaf layers with application to canopy reflectance modelling: the SAIL model. Remote Sens. Environ., 1984, 16, 125−141.
https://doi.org/10.1016/0034-4257(84)90057-9

13. Verhoef, W. Earth observation modeling based on layer scattering matrices. Remote Sens. Environ., 1985, 17165−178.
https://doi.org/10.1016/0034-4257(85)90072-0

14. Kuusk, A. The hot spot effect in plant canopy reflectance. In Photon-vegetation Interactions. Applications in Optical Remote Sensing and Plant Ecology (Myneni, R. B. and Ross, J., eds). Springer Verlag, Berlin, 1991, 139−159.
https://doi.org/10.1007/978-3-642-75389-3_5

15. Verhoef, W., Xiao, Q., Jia, L., and Su, Z. Unified optical-thermal four-stream radiative transfer theory for homogeneous vegetation canopies. IEEE Trans. Geosci. Remote Sens., 2007, 45, 1808–1822.
https://doi.org/10.1109/TGRS.2007.895844

16. Baret, F., Jacquemoud, S., Guyot, G., and Leprieur, C. Modeled analysis of the biophysical nature of spectral shifts and comparison with information content of broad bands. Remote Sens. Environ., 1992, 41, 133−142.
https://doi.org/10.1016/0034-4257(92)90073-S

17. Danner, M., Berger, K., Wocher, M., Mauser, W., and Hank, T. Fitted PROSAIL parameterization of leaf inclinations, water content and brown pigment content for winter wheat and maize canopies. Remote Sens., 2019, 11, 1150.
https://doi.org/10.3390/rs11101150

18. Resonon, Inc. [Online]. Available at 
https://resonon.com/ (accessed 2020-01-15).

19. Magnan, P. Detection of visible photons in CCD and CMOS: a comparative view. In Nuclear Instruments and Methods in Physics Research. Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 2003, 504(1–3), 199–212.
https://doi.org/10.1016/S0168-9002(03)00792-7

20. Ocean Optics, Inc. [Online]. Available at 
https://oceanoptics.com/ (accessed 2020-01-15).

21. PROSPECT+SAIL=PROSAIL homepage [Online]. Available at 
http://teledetection.ipgp.jussieu.fr/prosail/ (accessed 2020-03-01).

22. Mishra, P., Asaari, M. S., Herrero-Langreo, A., Lohumi, S., Diezma, B., and Scheunders, P. Close range hyperspectral imaging of plants: a review. Biosyst. Eng., 2017, 164, 49–67.
https://doi.org/10.1016/j.biosystemseng.2017.09.009

23. Carter, G. A. Primary and secondary effects of water content on the spectral reflectance of leaves. Am. J. Bot., 1991, 78(7), 916–924.
https://doi.org/10.1002/j.1537-2197.1991.tb14495.x

24. Jacquemoud, S. and Ustin, L. S. Modeling leaf optical properties [Online]. Available at 
http://photobiology.info/Jacq_Ustin.html (accessed 2020-01-15).
https://doi.org/10.1017/9781108686457

25. Jürise, M., Udal, A., Kaugerand, J., and Sell, R. Hyperspectral camera with polarized filter as modern supersensor device for cyber-physical systems. In Proceedings of 2018 16th Biennial Baltic Electronics Conference (BEC), Tallinn, Estonia, 2018. IEEE, Piscataway.
https://doi.org/10.1109/BEC.2018.8600957

26. Fermi, E. Zur Quantelung des idealen einatomigen Gases. Z. Physik, 1926, 36(11–12), 902–912.
https://doi.org/10.1007/BF01400221

27. Dirac, P. A. M. On the theory of quantum mechanics. Proc. R. Soc., 1926, A112(762), 661–677.
https://doi.org/10.1098/rspa.1926.0133


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