Browse > Article

Agricultural Application of Ground Remote Sensing  

Hong, Soon-Dal (Department of Agricultural Chemistry, Chungbuk National University)
Kim, Jai-Joung (Department of Agricultural Chemistry, Chungbuk National University)
Publication Information
Korean Journal of Soil Science and Fertilizer / v.36, no.2, 2003 , pp. 92-103 More about this Journal
Abstract
Research and technological advances in the field of remote sensing have greatly enhanced the ability to detect and quantify physical and biological stresses that affect the productivity of agricultural crops. Reflectance in specific visible and near-infrared regions of the electromagnetic spectrum have proved useful in detection of nutrient deficiencies. Especially crop canopy sensors as a ground remote sensing measure the amount of light reflected from nearby surfaces such as leaf tissue or soil and is in contrast to aircraft or satellite platforms that generate photographs or various types of digital images. Multi-spectral vegetation indices derived from crop canopy reflectance in relatively wide wave band can be used to monitor the growth response of plants in relation to environmental factors. The normalized difference vegetation index (NDVI), where NDVI = (NIR-Red)/(NIR+Red), was originally proposed as a means of estimating green biomass. The basis of this relationship is the strong absorption (low reflectance) of red light by chlorophyll and low absorption (high reflectance and transmittance) in the near infrared (NIR) by green leaves. Thereafter many researchers have proposed the other indices for assessing crop vegetation due to confounding soil background effects in the measurement. The green normalized difference vegetation index (GNDVI), where the green band is substituted for the red band in the NDVI equation, was proved to be more useful for assessing canopy variation in green crop biomass related to nitrogen fertility in soils. Consequently ground remote sensing as a non destructive real-time assessment of nitrogen status in plant was thought to be useful tool for site specific crop nitrogen management providing both spatial and temporal information.
Keywords
Electromagnetic spectrum; Remote sensing; Vegetation index;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Baret, F., G. Guyot, and D.J. Major. 1989. TSAVI: A vegetation index which minimizes soil brightness effectson LAI and APAR estimation, p. 1355-1358. In Proc. IGARRS '89 Can. Symp. Remote Sensing, 12nd, Vancouver, BC, Canada
2 Cater, G.A. and A.K. Knapp. 2001. Leaf optical properties in higher plants: linking spectral characteristics to stress and chlorophyll concentration. Am. J. Bot. 88 : 677-684   DOI   ScienceOn
3 Chappelle, E.W., M.S. Kim, and J.E. McMurtrey III. 1992. Ratio analysis of reflectance spectra(RARS) : An algorithm for the remote estimation of the concentration of chlorophyll a, chlorophyll b, and carotenoids in soybean leaves. Remote Sens. Environ. 39 : 239-247   DOI   ScienceOn
4 Gitelson, A.A., M.N. Merzlyak, and O.B. Chivkunova. 2001. Optical properties and nondestmctive estimation of anthocyanin content in plant leaves. Photochem. Photobiol. 74: 38-45   DOI   ScienceOn
5 Knipling, E.B. 1970. Physical and physiological basis for the reflectance of visible and near-infrared radiation from vegetation. Remote Sens. Environ. 1: 155-159   DOI   ScienceOn
6 Read, JJ., L. Tarpley, J.M. McKinion, and K.R. Reddy. 2002. Narrow-waveband reflectance ratios for remote estimation of nitrogen status in cotton. J. Environ. Qual. 31 : 1442-1452   DOI   ScienceOn
7 Shanahan, J.F., J.S. Schepers, D.D. Francis, G.E. Varvel, W.W. Wilhelm, J.M. Tringe, M.K. Schlemmer, and D.J. Major. 2001. Use of remote-sensing imagery to estimate com grain yield. Agron. J. 93 : 583-589   DOI   ScienceOn
8 Tucker, C.J. 1979. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens. Environ. 8: 127-150   DOI   ScienceOn
9 Avery, T.E. and G.L. Berlin. 1992. Fundamentals of remote sensing and airphoto interpretation. 5th ed. Macmillan, New York
10 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. Agron. J. 93 : 1227-1234   DOI   ScienceOn
11 Carter, G.A. 1993. Responses of leaf spectral reflectance to plant stress. Am. J. Bot. 80: 239-243   DOI   ScienceOn
12 Osbome, S.L., J.S. Schepers, D.D. Francis, and M.R. Schlemmer. 2002. Use of spectral radiance to estimate in-season biomass and grain yield in nitrogen- and water-stressed com. Crop Sci. 42 : 165-171   DOI   ScienceOn
13 Gilabert, M.A., J. Gonzalez-Piqueras, F.J. Garcia-Haro, J. Melia. 2002. A generalized soil-adjusted vegetation index. Remote Sens. Envimn. 82 : 303-310   DOI   ScienceOn
14 Buschmann, C. and H.K. Lichtenthaler. 1998. Phciples and characteristics of multi-colour fluorescence imaging of plants. J. Plant Physiol. 152 : 297-314   DOI
15 Gitelson, A.A., M.N. Merzlyak, and H.K. Lichtenthaler. 1996. Detection of red edge position and chlorophyll content by reflectance measurements near 700 nm. J. Plant Physiol. 148: 501-50
16 Rondeaux, G., M. Steven, and F. Baret. 1996. Optimization of soil-adjusted vegetation indices. Remote Sens. Environ. 55 : 95-107   DOI   ScienceOn
17 Schepers, James. S. 2001. Practical applications of remote sensing. Conference procedings of InfoAg, August 7-9, Indianopolis
18 Huete, A.R. 1988. A soil-adjusted vegetation index (SAVI). Remote Sens. Environ. 25 : 295-309   DOI   ScienceOn
19 Jensen, J.R. 2000. Remote sensing of the environment: An earth resource perspecdve. Prentice-Hall, Upper Saddle River, NJ
20 Tarpley, L., K.R. Reddy, and G.F. Sassenrath-Cole. 2000. Reflectance indices with precision and accuracy in predicting cotton leaf nitrogen concentration. Crop Sci. 40:1814-1819   DOI   ScienceOn
21 Gamon, J.A., L. Serrano, and J.S. Surfus. 1997. The photochernical reflectance index: an optical indicator of photosynthetic radiation use efficiency across species, functional types, and nuthent levels. Oecologia 112: 492-501   DOI   ScienceOn
22 Aparicio, N.D. Villegas, J.L. Araus, J. Casadesus, and C. Royo. 2002. Relationship between growth traits and spectral vegetation indices in durum wheat. Crop Sci. 42 : 1547-1555   DOI   ScienceOn
23 Wiegand, C.L., A.J. Richardson, D.E. Escobar, and A.H. Gerbermann. 1991. Vegetation indices in crop assessments. Remote Sens. Environ. 35 : 105-119   DOI   ScienceOn
24 Cater, G.A. and B.A. Spiering. 2002. Optical properties of intact leaves for estimating chlorophyll concentration. J. Environ.Qual.31 : 1424-1432   DOI   ScienceOn
25 Hatfield, J.L. and P.J. Rnter, Jr. 1993. Remote sensine for crop protection. Crop Prot. 12 : 403-413   DOI   ScienceOn
26 Jackson, R.D. 1986. Remote sensing of biotic and abiodc plant stress. Ann. Rev. Phytopathol. 24 : 265-287   DOI   ScienceOn