New method for remote sensing estimation of chlorophyll contents in vegetation and its software realization

1Yatsenko, VO, 2Khandriga, PO, 3Kochubey, SM, 4Donets, VV, 1Semeniv, OV
1Space Research Institute of the National Academy of Sciences of Ukraine and the State Space Agency of Ukraine, Kyiv, Ukraine
2Educational-scientific complex «Institute for Applied Systems Analysis» NTUU «Kyiv Polytechnic Institute», Kyiv, Ukraine
3Institute of Plant Physiology and Genetics of the National Academy of Sciences of Ukraine, Kyiv
4Corporation «Research and Production Enterprise «Arsenal», Kyiv, Ukraine
Kosm. nauka tehnol. 2007, 13 ;(3):035-044
https://doi.org/10.15407/knit2007.03.035
Publication Language: Russian
Abstract: 
The problem of remote sensing estimation of vegetation chlorophyll content for condition monitoring by reflectance spectra is considered. We propose a way for the solution of the problem with the use of the principal components analysis method (PCA) and nonlinear regression models. A hardware-software complex is developed to realize the way. Some results of testing the complex and of its comparison with the first derivative method are presented.
Keywords: chlorophyll, monitoring, reflectance spectra
References: 
1. Kochubei S. M. Comparison of the information power of multispectral imaging and high-resolution spectroscopy in the remote sounding of vegetation cover. Kosm. nauka tehnol., 5 (2-3), 41—48 (1999) [in Russian].
2. Kochubei S. M. Equipment and methods for the remote sensing of vegetative cover in the optical range. Kosm. nauka tehnol., 8 (2-3), 271—275 (2002) [in Russian].
https://doi.org/10.15407/knit2002.02.271
3. Kochubei S. M. Estimation of the main characteristics of agricultural crops from reflectance spectrum of vegetation in the optical range. Kosm. nauka tehnol., 9 (5-6), 185—190 (2003) [in Russian].
https://doi.org/10.15407/knit2003.05.185
4. Kochubey S. M., Kobets N. I., Shadchina T. M. The quantitative analysis of shape of spectral reflectance curves of plant leaves as a way for testing their status. Fiziol. i biohim. kul't. rastenij, 20 (6), 535—539 (1988) [in Russian].
5. Kochubey S. M., Kobets N. I., Shadchina T. M. Spectral Properties of Plants as a Basis for the Methods of Remote Diagnostic, 136 p. (Naukova dumka, Kiev, 1990) [in Russian].
6. Faddeev D. K., Faddeeva V. N. Computational methods of linear algebra, 736 p. (Lan', Moscow, 2002) [in Russian].
7. Arnon D. I. Copper enzymes in isolated chloroplasts. Polyphenoloxidase in Beta vulgaris. Plant Physiol., 24, 1 — 15 (1949).
https://doi.org/10.1104/pp.24.1.1
8. Bowyer P., Danson F. M. Sensitivity of spectral reflectance to variatio in live fuel moisture content at leaf and canipy level. Remote Sens. Environ., 92, 297— 308 (2004).
https://doi.org/10.1016/j.rse.2004.05.020
9. Ceccato P., Flasse S., Gregoire J. M. Designing a spectral index to estimate vegetation water content from remote sensing data. 2. Validation and applications. Remote Sens. Environ., 82, 198—207 (2002).
https://doi.org/10.1016/S0034-4257(02)00036-6
10. Ceccato P., Flasse S., Tarantola S., et al. Detecting vegetation leaf water content using reflectance in thew optical domain. Remote Sens. Environ., 77, 22—33 (2001).
https://doi.org/10.1016/S0034-4257(01)00191-2
11. Comon P. Independent component analysis. A new concept? Signal Processing, No. 36 (3), 287—314 (1994).
https://doi.org/10.1016/0165-1684(94)90029-9
12. Davids C., Tyler A. N. Detecting contamination-induced tree stress within the Chernobyl exclusion zone. Remote Sens. Environ., 85, 30—38 (2003).
https://doi.org/10.1016/S0034-4257(02)00184-0
13. Horler D. N. H., Dokray M., Barber J. The red edge of plant leaf reflectance. Int. J. Remote Sens., 4, 273—288 (1983).
https://doi.org/10.1080/01431168308948546
14. Kochubey S. M., Bidyuk P. I. A Novel approach to remote sensing of vegetation. Proc. SPIE, 5093, 181 —188 (SPIE Conf. "AeroSence. Technologies and Systems for Defence & Security", 21—25 April 2003, Orlando USA) (2003).
https://doi.org/10.1117/12.486023
15. Lamb D. W., Steyn-Ross M., Schaare P., et al. Estimating leaf nitrogen concentration in ryegrass (Lolium spp.) pasture using the chlorophyll red-edge: theoretical modelling and experimental observations. Int. J. Remote Sens., 23, 3619—3648 (2002).
https://doi.org/10.1080/01431160110114529
16. Moran J. A., Mitchell A. K., Goodmanson G., Stockburger K. A. Differetiation among effects of nitrogen fertilization treatments on conifer seedlings by foliar reflectance: a comparison of methods. Tree Physiology, 20, 1113—1120 (2000).
https://doi.org/10.1093/treephys/20.16.1113
17. Pinar A., Curran P. J. Grass chlorophyll and the reflectance red edge. Int. J. Remote Sens., 17, 351—357 (1996).
https://doi.org/10.1080/01431169608949010
18. Polischuk V. P., Shadchina T. M., Kompanetz T. I., et al. Changes in reflectance spectrum characteristics of Nicotiana debney plant under the influence of viral infection. Arh. Phytopath. Pflanz, 31, 115—119 (1997).
https://doi.org/10.1080/03235409709383221
19. Riggs G. A., Running S. W. Detection of canopy water stress in conifers using the airborne imaging spectrometer. Remote Sens. Environ., 35, 51—68 (1991).
https://doi.org/10.1016/0034-4257(91)90065-E
20. Smith K. L, Steven M. D., Coll J. J. Use of hyperspectral derivative ratios in the red-edge region to idetify plant stress responses to gas leaks. Remote Sens. Environ., 92, 207—217 (2004).
https://doi.org/10.1016/j.rse.2004.06.002
21. Yatsenko V. A., Kochubey S. M., Pardalos P. M., Zhan L. Estimation of chlorophyll concentration in vegetation using global optimization approach. Proc. SPIE, 5071, 50—59 ("Technologies, Systems, and Architectures for Transnational Defence II", SPIE Conference "AeroSence. Technologies and Systems for Defence & Security", 21—25 April 2003, Orlando USA) (2003).
https://doi.org/10.1117/12.487756
22. Zarko-Tejada P. J., Miller J. R., Mohammed G. H., et al. Vegetation stress detection through chlorophyll a+b estimation and fluorescence effects on hyperspectral imagery. J. Environ. Qual., 31, 1433—1441 (2002).
https://doi.org/10.2134/jeq2002.1433
23. Zarko-Tejada P. J., Pushnik J. C., Dobrowski S., Ustin S. L. Steady-state chlorophyll a fluorescence detection from canopy derivative reflectance and double peak red edge effect. Remote Sens. Environ., 84, 283—294 (2003).
https://doi.org/10.1016/S0034-4257(02)00113-X