Modeling rice growth from characteristics of Reflectance spectra

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Modeling rice growth from characteristics of Reflectance spectra

Results and Discussion
Typical patterns of the measured GC during the growing seasons were shown in Figure 1. The curves were analogous to seasonal trends of NDVI in both crops (Figure 2), suggesting that NDVI was sensitive to progression of vegetative cover and hence be a promising parameter in estimating plant growth. The time-sequential changes of NDVI and reflectance spectra were caused by the ‘environmental’ and ‘growth’ effects inducing significant changes in plant structure and morphology during crop growth (Curran, 1983; Masoni et al., 1996; Sinclair et al., 1971). The turning period of NDVI curve corresponded to the phase transition, from vegetative growth to reproductive growth (Su and Yang, 1999). Because of the strong background effect and senescence, it would be better not to use NDVI in the early and the late plant development.
Table 3. Correlation coefficients for growth characters of rice and characteristics wavelengths (CW) and the simulated wavebands of different satellite sensors.

Waveband		Leaf number	Plant height	Leaf area index	Leaf dry weight	Aboveground dry weight
Landsat-TM	TM1	-0.790	-0.339	-0.797	-0.690	-0.135
	TM2	-0.754	-0.041	-0.608	-0.483	-0.187
	TM3	0.838	-0.015	-0.718	-0567	-0.152
	TM4	0.468	0.600	0.806	0.783	0.523
Landsat-MSS	MSS1	-0.767	-0.073	-0.638	-0.512	-0.155
	MSS2	-0.839	-0.017	-0.697	-0.543	-0.189
	MSS3	0.372	0.599	0.723	0.715	0.555
	MSS4	0.391	0.665	0.762	0.761	0.582
Spot-HRV	HRV1	-0.757	-0.083	-0.632	-0.509	-0.148
	HRV2	-0.839	-0.010	-0.711	-0.560	-0.165
	HRV3	0.460	0.610	0.804	0.784	0.532
CW	GREEN	-0.678	-0.040	-0.530	-0.421	-0.199
	RED	-0.828	-0.052	-0.736	-0.590	-0.107
	NIR	0.503	0.538	0.801	0.764	0.470

Relations of GC and VIs were examined by correlation matrix analysis. Generally LAI showed a better correlation with these VIs among GC in all satellite inputs (Table 2). The relationships were further strengthened with curvilinear functions. For instance, correlation between LAI and NDVI was improved in an exponential relationship (Figure 3) with the determining factors (R² ) greater than 0.76. The predicted values of LAI were linearly correlated to the observed values. Correlation coefficients for GC and the simulated wavebands of different satellite sensors may be positive or negative, whereas LAI had closer linkage in general (Table 3). By stepwise regression to the second order of the spectral parameters, estimation of GC can be formulated to exponential equations with R² greater than 0.53 (data not shown). Results indicate that the normalized difference transformation technique is suitable for the compensation of sun conditions and the estimation of rice growth. It suggests a relative simple approach based on correlation between GC and VI for estimating and monitoring the ground cover and growth performance. This methodology is founded on the close linkage in vegetation spectral characteristics and GC rather than purely empirical curve-fitting and thus may be more universally applicable to a variety of applications. However, such relationships may be changed and their availability will be reduced under situations such as environmental stresses, pests infection, low sun angle, and strong soil background effects.

The simulated results also suggest that broad-band satellite data may be substituted for ground-based narrow-band measurements if spectral characteristics of wavebands are well-selected. Therefore, data bank established by narrow-band ground truth is applicable to spectral inputs collected from satellites in assessing plant growth of a crop. Due to many more interacting variables, however, the spectral data measured from satellite remote sensing are considerable more complicated than those from the ground platform. Many important parameters in relation to atmospheric and geographic calibrations need to be taken into consideration.

References

Curran, P. J., 1983. Multispectral remote sensing for the estimation of green leaf index. Philosophical Transactions of Royal Society, London, Series A, 309, pp.257-270.
Elvidge, C. D., and Z. Chen, 1995. Comparison of broad-band and narrow-band red and near-infrared vegetation indices. Remote Sensing of Environment, 54, pp.38-48.
Kanemasu, E. T., 1974. Seasonal canopy reflectance patterns of wheat, sorghum, and soybean. Remote Sensing of Environment, 3, pp.43-47.
Masoni, A., L. Ercoli, and M. Mariotti, 1996. Spectral properties of leaves deficient in iron, sulfur, magnesium, and manganese. Agronomy Journal, 88, pp.937-943.
Richardson, A. J., C. L. Wiegand, D. F. Wanjura, D. Dusek, and J. L. Steiner, 1992. Multisite analyses of spectral-biophysical data for sorghum. Remote Sensing of Environment, 41, pp.71-82.
Rouse, J. W., R. H. Haas, J. A. Schell, D. W. Deering, and J. C. Harlan, 1974. Monitoring the vernal advancement and retrogradiation (greenwave effect) of natural vegetation. NASA/GSFC Type III final report. Greenbelt, MD., USA, 371pp.
Sinclair, T. R., R. M. Hoffer, and M. M. Schreiber, 1971. Reflectance and internal structure of leaves from several crops during a growing season. Agronomy Journal, 63, pp.864-868.
Su, M.-R., and C.-M. Yang, 1999. Estimation of rice growth from reflectance spectra of vegetative cover. Journal of Photogrammetry and Remote Sensing, 4(3), (in press) Tucker, C. J., 1979. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment, 8, pp.127-150.
Yang, C.-M., and M.-R. Su, 1997. Analysis of reflectance spectrum of rice canopy. Chinese Journal of Agrometeorology, 4, pp.87-95.
Yang, C.-M., and C.-C. Ko, 1998. Seasonal changes in canopy spectra of sweet potato. Journal of Photogrammetry and Remote Sensing, 3(1), pp.13-28.
Yang, C.-M., and M.-R. Su, 1998a. Seasonal variations of reflectance spectrum and vegetation index in rice vegetation cover. In: Proceeding of the 3^rd Asian Crop Science Conference. April 27-May 2, 1998. Chinese Society of Agronomy. Taichung, Taiwan, pp.574-593.
Yang, C.-M., and M.-R. Su, 1998b. Correlation of spectral reflectance to growth of rice vegetation. In: Proceedings of the 19^th Asian Conference on Remote Sensing. November 16-20, 1998. National Mapping and Resource Information Authority and Asian Association on Remote Sensing. Manila, Philippines, pp.A-1-1-A-1-6.

Days after transplanting
Figure 1. Seasonal changes in physical growth characters of leaf area index (LAI), plant height (PH), leaf number (LN), and leaf dry weight (LDW) of rice (Oryza sativa L. cv. Tainung 67) vegetative cover during the 1^st and the 2^nd cropping seasons in 1996-1998.

Days after transplanting
Figure 2. Seasonal changes of normalized difference vegetation index (NDVI) calculated from characteristics wavelengths of rice reflectance spectra.


NDVI	Observed LAI

Figure 3. The exponential relationships between leaf area index (LAI) and normalized difference vegetation index (NDVI) and the linear correlation between the predicted values and the observed values of LAI.

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