Modeling and Device Development for Chlorophyll Estimation in Vegetation
Accurate estimation of leaf chlorophyll level by remote sensing is a challenging problem. Such estimation is especially needed in an ecologically dangerous environment. Our goal is to develop new methods that allow estimating chlorophyll concentration using remote sensing data for multiple kinds of soil and vegetation. The estimation is based on a training data set obtained from the leaf samples collected at various points on the earth’s surface. A laboratory spectrophotometer was used to measure spectral reflectance curves in the visible and near-infrared ranges of the spectrum. The spectrometer was designed to comply with the strict measurement requirements essential for robust estimation. Optical indices related to leaf-level chlorophyll estimation were used as input data to test different modeling assumptions in open canopies where density of vegetation, soil, and chlorophyll content were separately targeted using a laboratory spectrometer. The goal of the research work is to estimate chlorophyll level based on spectrum characteristics of light reflected from the earth’s surface. We have applied pattern recognition techniques as well as linear and nonlinear regression models. Unlike previously suggested approaches, our methods use the shape of the spectral curve obtained from measuring reflected light. The numerical experiments confirmed robustness of the model using input data retrieved from an ecologically dangerous environment.
KeywordsChlorophyll Content Chlorophyll Concentration Spectral Curve Open Canopy Projective Covering
- 1.P. A. Khandriga and V. A. Yatsenko. An application of the principal components method for chlorophyll content estimation in vegetation. 2(31):83–91, 2004.Google Scholar
- 2.S. M. Kochubey. Comparative analysis of information power of multispectral imaging and high-resolution spectrometry in the remote sensing of vegetation cover. Space Sciences and Technology, 5:41–48, 1999.Google Scholar
- 3.S. M. Kochubey. Estimating of the main characteristics of agricultural crops from reflectance spectrum of vegetation in the optical range. Space Sciences and Technology, 9:185–190, 2003.Google Scholar
- 4.S. M. Kochubeyand P. Bidyuk. Novel approach to remote sensing of vegetation. volume 5093, pages 181–188, Orlando, FL, 2003.Google Scholar
- 5.S. M. Kochubey, N. Kobets, and T. M. Shadchina. The quantitative analysis of shape of spectral reflectance curves of plant leaves as a way for testing their status. Physiology and Biochemistry of Cultivar Plants, 20:535–539, 1988.Google Scholar
- 6.S. M. Kochubey, N. Kobets, and T. M. Shadchina. Spectral properties of plants as a base of distant diagnostic methods. Naukova Dumka, 1990.Google Scholar
- 8.S. M. Kochubey, V. A. Yatsenko, and N. V. Gurinovich. www.vegetation.kiev.ua.
- 9.Smola A. J. and Schölkopf B. A tutorial on support vector regression. NeuroCOLT technical report, Royal Holloway University of London, UK, 1998. http://www.kernel-machines.org.
- 13.V. N. Vapnik, and A. Y. Chervonenkis. Theory of Pattern Recognition. Moscow, 1974.Google Scholar
- 14.V. Yatsenko, S. Kochubey, V. Donets, and T. Kazantsev. Hardware-software complex for chlorophyll estimation in phytocenoses under field conditions. 5964:1–6, 2005.Google Scholar
- 15.V. Yatsenko, S. Kochubey, P. Khandriga, V. Donets, and P. Chichik. Optical spectrometer and software for remote sensing of vegetations. volume 2, pages 267–269, Yalta (Crimea, Ukraine), 2005. 2nd International Conference on Advanced Optoelectronics and Laser.Google Scholar