Estimation of Photosynthetic Rate of Plant
from Hyper-spectral Remote Sensing of Biochemical Content
A range of wavelength was selected between 500 nm to 800 nm to remove the effect of water and sensor noise. As the result from these methods, coefficient of determination of the method using spectral reflectance at one single wavelength was low over all ranges of wavelength in the greenhouse chamber. This result is shown in Figure 3 (left). Distribution of coefficient of determination between chlorophyll a concentration per unit area and the ratio of spectral reflectance at two different wavelengths is shown in Figure 3 (right). The ratio became higher than that of spectral reflectance at one single wavelength. Also the ratio became high, when either denominator or numerator was selected as spectral reflectance at absorption band of chlorophyll a. The coefficient of determination between the first derivative spectral reflectance and chlorophyll a concentration per unit area is shown in Figure 4. There were three peaks. The first peak was due to pigments like chlorophylls and carotenoids. The second peak was absorption band of chlorophyll a. The third peak is due to the structure of leaf.
The authors found that two methods were effective to estimate chlorophyll a concentration per unit area using hyperspectral data. Moreover, it is important to select the best method based on the measurement condition. In case of the experiment in the greenhouse chamber, the best method was the first derivative spectral reflectance.
Figure 3. Coefficient of the determination between chlorophyll a concentration per unit area and spectral reflectance at one single wavelength in the greenhouse chamber (left) and distribution of coefficient of determination between chlorophyll a concentration per unit area and ratio of spectral reflectance at two different wavelengths (right) in the greenhouse chamber
Figure 4. Coefficient of determination between the first derivative spectral reflectance and chlorophyll a concentration per unit area
4.3. Evaluation of Net Photosynthetic Rate
In this study, the first derivative spectral reflectance was considered best to estimate chlorophyll a concentration per unit area. This process model was obtained based on both relationship between saturated Amax and chlorophyll a concentration and between chlorophyll a concentration and the first derivative spectral reflectance. Model's parameters were estimated as shown in Eq. (7).
Chlorophyll a concentration per unit area could be estimated with R
2=0.81 using the first derivative spectral reflectance at 678.01 nm and then saturated Amax could be estimated with R
2=0.90 by the estimated chlorophyll a concentration. Also R can be estimated by saturated Amax, and finally, Amax' was calculated by saturated Amax and R. The Light-photosynthetic rate curve was drawn. We estimated net photosynthetic rate at PFD condition that is same as that in greenhouse chamber. The relationship between measured net photosynthetic rate and estimated net photosynthetic rate is shown in Figure 7. Coefficient of determination was 0.74.
Figure 7. Correlation between measured net photosynthetic rate
and estimated net photosynthetic rate
6. Conclusion
It is important to estimate parameters of a terrestrial ecosystem function to solve the global environmental issues. Hyperspectral remote sensing is a new technology, which is most useful to estimate biochemical parameters. This study investigated whether it is possible to estimate net photosynthetic rate from hyperspectral remote sensing. It was found that the net photosynthetic rate could be estimated by chlorophyll a concentration per unit area with hyperspectral data. Also, the net photosynthetic rate could be estimated by nitrogen concentration per unit area a similar equation that is a little bit different in respect of selected wavelength at 732.122 nm. The saturated Amax could be estimated with chlorophyll a or nitrogen concentration per unit area measured by hyperspectral data. Further, the saturated Amax could be used to estimate the respiration. Then our process model could estimate net photosynthetic rate with coefficient of determination of 0.74. We've suggested a new method to estimate CO
2 uptake based on the estimated chlorophyll or nitrogen concentration per unit area.
In future, it will be necessary to examine relationship between physiological variables and model's parameters to net photosynthetic rate at canopy and community scale. Furthermore Blackman type will be modified to accommodate in consider effects of temperature and water stress and so on. Then biochemical variables of a terrestrial ecosystem like lignin and cellulose will be estimate using hyperspectral imager at canopy and community scale.
Acknowledgments
This work was supported by two divisions of NIES - the natural vegetation conservation research team of the global environment division; the information processing and analysis section of the social and environmental system division.
References
-
Lee F. Johnson, Christine A. Hlacka, and David L. Peterson, "Multivariate Analysis of AVIRIS Data for Canopy Biochemical Estimation along the Oregon Transect ", REMOTE SEN. ENVIRON., 47, pp. 216-230. 1994.
- Paul J. Curran, John A. Kupiec, and Geoffrey M. Smith, "Remote Sensing the Biochemical Composition of a Slash Pine Canopy", IEEE TRANSSACTION ON GEOSCIENCE AND REMOTE SENSING, 35, NO. 2, MARCH, pp. 415-420. 1997.
- Blackman FF, "Optima and limiting factors", Annals of Botany, 19, pp. 281-295. 1905.
- R. J. Porra, W. A. Thompson and P. E. Kriedeman, "Determination of accurate extinction coefficients and simultaneous equations for assaying chlorophylls a and b extracted with four different solvents: verification of the concentration of chlorophyll standards by atomic absorption spectroscopy ", Biochi. mica et Biophysica Acta, 975, pp. 384-394. 1989.
