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http://dx.doi.org/10.7780/kjrs.2008.24.6.551

Estimation of Rice Grain Protein Contents Using Ground Optical Remote Sensors  

Kim, Yi-Hyun (National Academy of Agricultural Science, Rural Development Administration)
Hong, Suk-Young (National Academy of Agricultural Science, Rural Development Administration)
Publication Information
Korean Journal of Remote Sensing / v.24, no.6, 2008 , pp. 551-558 More about this Journal
Abstract
It is well known that the protein content of rice grain is an indicator of taste of cooked rice in the countries where people as the staple food. Ground-based optical sensing over the crop canopy would provide information not only on the mass of plant body which reflects the light, but also on the crop nitrogen content which is closely related to the greenness of plant leaves. The vegetation index has been related to crop variables such as biomass, leaf nitrogen, plant cover, and chlorophyll in cereals. The objective of this study was to investigate the correlation between GNDVI and NDVI values, and grain protein content at different dates and to estimate the grain protein content using G(NDVI) values. We measured Green normalized difference vegetation index [$GNDVI=({\rho}0.80{\mu}m-{\rho}0.55{\mu}m)/({\rho}0.80{\mu}m+{\rho}0.55{\mu}m)$] and [$GNDVI=({\rho}0.80{\mu}m-{\rho}0.68{\mu}m)/({\rho}0.80{\mu}m+{\rho}0.68{\mu}m)$] by using two different active sensors. The study was conducted during the rice growing season for three years from 2005 through 2007 at the experimental plots of National Institute of Agricultural Science and Technology. The experiments were carried out by randomized complete block design with the application of four levels of nitrogen fertilizers(0, 70, 100, 130kg N/ha) and the same amount of phosphorous and potassium content of the fertilizers. After heading stage, relationships between GNDVI of rice canopy and grain protein content showed the highly positive correlation at different dates for three years. GNDVI values showed higher correlation coefficients than that of NDVI during growing season in 2005-07. The correlation between GNDVI values at different dates and grain protein contents was highly correlated at early July. We attempted to estimate the grain protein content at harvesting stage using GNDVI values from early July for three years. The determination coefficients of the linear model by GNDVI values were 0.9l and the measured and estimated grain protein content at harvesting stage using GNDVI values highly correlated($R^2=0.96^{***}$). Results from this study show that GNDVI appeared very effective to estimate leaf nitrogen and grain protein content of rice canopy.
Keywords
Vegetation index; GNDVI; NDVI; Correlation coefficients; Grain protein; Heading stage;
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Times Cited By KSCI : 1  (Citation Analysis)
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1 김이현, 홍석영, 이지민, 임상규, 곽한강, 2005. 광학센서를 이용한 식생지수와 쌀 단백질함량 관계. 2006 대한원격탐사학회 춘계학술대회, March 31: 193-198
2 Asaka, D. and H. Shiga, 2003. Estimating rice grain protein contents with SPOT/HRV data acquired at maturing stage, Journal of The Remote Sensing Society of Japan, 23(5): 451-457
3 Barbara, J. Y. and R. E. Pettigrew-Crosby, 1995. Predicting nitrogen and chlorophyll content and concentrations from reflectance spectra at leaf and canopy scales, Remote Sensing of Environment, 53: 199-211   DOI   ScienceOn
4 Baret, F. and G. Guyot, 1991. Potentials and limits of vegetation indices for LAI and APAR assessment, Remote Sensing of Environment, 35: 161-173   DOI   ScienceOn
5 Hong, S. Y., K. A. Sudduth., N. A. Kitchen., C. W. Fraisse., H. L. Palm, and W. J. Wiebold, 2004. Comparison of remote sensing and crop growth models for estimating withinfield LAI variability, Korean Journal of Remote Sensing, 20: 175-188   DOI
6 National Institute of Agricultural Science and Technology, 2000. Methods of soil and crop plant analysis, National Institute of Agricultural Science and Technology, RDA, Suwon, Korea
7 Aparicio, N., D. Villegas, J. Casadesus, J. L. Araus, and C. Royo, 2000. Spectral vegetation indices as non-destructive tools for determining durum wheat yield, Agronomy. J., 92: 83-91   DOI   ScienceOn
8 Ryu, C. S., M. Suguri, and M. Umeda, 2005. Estimation the nitrogen contents and the rice quality using hyperspectral remote sensing technology, European Conference of Precision Agriculture 2005, pp. 325-330
9 Christensen, S. and J. Goudriaan, 1993. Deriving light interception and biomass from spectral reflectance ratio, Remote Sensing of Environment, 43: 87-95   DOI   ScienceOn
10 Anna, P. and B. Abdou, 2001. Application of hyperspectral remote sensing for LAI estimation in precision farming, Canadian Remote Sensing Symposium
11 Richardson, A. J. and C. L. Weigand, 1992. Using Spectral Vegetation Indices to Estimate Rangeland Productivity, Geocarto International, 1: 63-77
12 김이현, 홍석영, 2006. 지상광학센서를 이용한 비파괴 벼 엽 질소함량 추정, 한국토양비료학회지, 40(6): 435-441
13 Scharf, P. C., J. P. Schmidt., N. P. Kitchen., K. A. Sudduth., S. Y. Hong., J. A. Lory, and J. G. Davis, 2002. Remote sensing for nitrogen management, Journal of Soil and Water Conservation, 57(6): 518-524
14 홍석영, 김이현, 최철웅, 이지민, 이재중, 임상규, 곽한강, 2005. 지상센서와 위성영상을 이용한 벼 군락의 엽 질소함량 추정, 2006 대한원격탐사학회 춘계학술대회, March 31: 218-223
15 Best, R. G. and J. C. Harlan, 1985. Spectral estimation of green leaf area index of oats, Remote Sensing of Environment, 17: 27-36   DOI   ScienceOn