Browse > Article

Applicability of Vegetation Index and SPAD Reading to Nondestructive Diagnosis of Rice Growth and Nitrogen Nutrition Status  

Kim Min-Ho (Dept. of Plant Science, College of Agriculture and Life Sciences, Seoul Nat'l Univ.)
Shin Jin-Chul (Dept. of Crop Physiology and Ecology. National Institute of Crop Science, RDA)
Lee Byun-Woo (Dept. of Plant Science, College of Agriculture and Life Sciences, Seoul Nat'l Univ.)
Publication Information
KOREAN JOURNAL OF CROP SCIENCE / v.50, no.6, 2005 , pp. 369-377 More about this Journal
Abstract
Precise application of topdressing nitrogen (N) fertilizer is indispensible for securing high yield and good quality of rice and minimizing N losses to the environment as well. For precise N management, growth and nitrogen nutrition status (NNS) should be diagnosed rapidly and accurately. The objective of the study was to evaluate the applicability of vegetation index (VI) calculated from hyperspectral canopy reflectance measurement and SPAD reading to nondestructive in situ diagnosis of growth and NNS of rice. Canopy reflectance, SPAD read­ing, growth parameters, and NNS characteristics were measured from various N treatments to evaluate the relationships among them for two cropping seasons from 2001 to 2002. The correlation coefficient of VIs with variables of growth and NNS increased positively as rice canopy became more closed. Regardless of growth stages, VIs had significantly high correlations with LAI, shoot dry weight (DW), shoot N content and nitrogen nutrition index (NNI). Those correlation coefficients increased steadily before heading stage as rice grew up. However, tiller number and leaf N concentration showed significantly high correlations with VIs only at and after panicle initiation stage (PIS). Among the VIs, RVIgreen had significantly higher correlation with the measured parameters than the other VIs: it showed correlation coefficients greater than 0.8 with leaf and shoot N concentration and DW, and much higher coefficients greater than 0.9 with LAI, shoot N content, and NNI. At LAI of below 2.5, VIs had non-significant or low correlations with the growth and NNS indicators due to the background effects. SPAD reading had significantly high correlation with leaf N concentration and NNI at each growth stage. In addition, it had significant correlations with variables of growth and NNS at PIS and booting stage, particularly, at booting stage. Though SPAD reading had a significantly high correlation value at a given growth stage in each year, it showed very weak relationship with variables of growth and NNS when pooled across growth stages and years. In conclusion, RVIgreen was found to be the most reliable VI to estimate the growth and NNS of rice around at PIS, but SPAD reading had much limitations.
Keywords
rice; growth; nitrogen nutrition; spectral reflectance; vegetation index; SPAD;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Danson, F. M. and S. E. Plummer. 1995. Red-edge response to forest leaf area index. International Journal of Remote Sensing 16: 183-188   DOI   ScienceOn
2 Daughtry, C. S. T., C. L. Walthall, M. S. Kim, E. Brown de Colstoun, and J. E. McMurtrey III. 2000. Estimating com leaf chlorophyll concentration from leaf and canopy reflectance. Remote Sensing of Environment 74:229-239   DOI   ScienceOn
3 홍석영. 1999. 원격탐사 자료를 이용한 벼 생육정보 분석 및 재배면적 추정. 경북대학교 박사학위논문
4 김준환. 2001. 벼의 한계질소농도 구명 및 군락반사율에 의한 질소영양상태 추정연구. 서울대학교 석사학위논문
5 Ladha, J. K., A. Tirol-Padre, G. C. Punzalan, E. Castillo, U. Singh, and C. K. Reddy. 1998. Nondestructive estimation of shoot nitrogen in different rice genotypes. Agronomy Journal 90:33-40   DOI   ScienceOn
6 Turner, F. T. and M. F. Jund. 1991. Chlorophyll meter to predict nitrogen topdressing requirement for semidwarf rice. Agronomy Journal 83:926-928   DOI
7 Yoder, B. J. and R. E. Pettigrew-Crosby. 1995. Predicting nitrogen and chlorophyll content and concentrations from reflectance spectra(400-2500nm) at leaf and canopy scales. Remote Sensing of Environment 53:199-211   DOI   ScienceOn
8 Peng. S., R. C. Laza, F. V. Garcia, and K. G. Cassman. 1995. Chlorophyll meter estimates leaf area based nitrogen concentration of rice. Community of Soil Science and Plant Analysis 26:927-935   DOI   ScienceOn
9 Piekielek, W. P. and R. H. Fox. 1992. Use of a chlorophyll meter to predict sidedress N requirements for maize. Agronomy Journal 84:59-65   DOI
10 Diker, K. and W. C. Bausch. 2003. Radiometric field measurements of maize for estimating soil and plant nitrogen. Biosystems Engineering 86(4):411-420   DOI   ScienceOn
11 Fox, R. H., W. P. Piekielek, and K. M. Macneal. 1994. Using a chlorophyll meter to predict nitrogen fertilizer needs of winter wheat. Community of Soil Science and Plant Analysis 25: 171-181   DOI   ScienceOn
12 Gausman, H. W., W. A. Allen, V. I. Myers, and R. Cardenas. 1969. Reflectance and internal structure of cotton leaves, Gossypium birsutum L. Agronomy Journal 61:374-376   DOI
13 Norman, R. J., D. Guindo, B. R. Wells, and C. E. Wilson, Jr. 1992. Seasonal accumulation and partitioning of nitrogen-15 in rice. Soil Science Society of American Journal 56:1521-1527   DOI
14 Hansen, P. M. and J. K. Schjoerring. 2003. Reflectance measurement of canopy biomass and nitrogen status in wheat crops using normalized difference vegetation indices and partial least squares regression. Remote Sensing of Environment 86:542-553   DOI   ScienceOn
15 Pontailler, J. Y., G. J. Hymus, and B. G. Drake. 2003. Estimation ofleaf area index using ground-based remote sensed NDVI measurements: validation and comparison with two indirect techniques. Canadian Journal of Remote Sensing 29(3):381-387   DOI
16 Cui, R. X., M. H. Kim, J. H. Kim, H. S. Nam, and B. W. Lee. 2002. Determination of critical nitrogen concentration and dilution curve for rice growth. Korean Journal of Crop Science. 47:127-131
17 Agren, G. I. 1985. Theory for growth of plants derived from the nitrogen productivity concept. Physiologia Plantarum 64:17-28   DOI
18 Piekielek, W. P., R. H. Fox, J. D. Toth, and K. E. Macneal. 1995. Use of a chlorophyll meter at the early dent stage of com to evaluate nitrogen sufficiency. Agronomy Journal 87:403-408   DOI   ScienceOn
19 Ma, B. L., L. M. Dwyer, C. Costa, E. R. Cober, and M. J. Morrison. 2001. Early prediction of soybean yield from canopy reflectance measurements. Agronomy Journal 93:1227-1234   DOI
20 Yang, Y. K. and L. D. Miller. 1985. Correlations of rice grain yields to radiometric estimates of canopy biomass as a function of growth stage. Journal of Korean Society of Remote Sensing 1:63-86
21 Flowers, M., R. Weisz, and R. Heiniger. 2003. Quantitative approaches for using color infrared photography for assessing inseason nitrogen status in winter wheat. Agronomy Journal 95: 1189-1200   DOI
22 Rondeauz, G, M. Steven, and F. Baret. 1996. Optimization of soiladjusted vegetation indices. Remote Sensing of Environment 55:95-107   DOI   ScienceOn