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http://dx.doi.org/10.7780/kjrs.2012.28.6.1

Comparing LAI Estimates of Corn and Soybean from Vegetation Indices of Multi-resolution Satellite Images  

Kim, Sun-Hwa (National Academy of Agricultural Science, Rural Development Administration (RDA))
Hong, Suk Young (National Academy of Agricultural Science, Rural Development Administration (RDA))
Sudduth, Kenneth A. (Cropping Systems and Water Quality Research Unit, USDA-ARS)
Kim, Yihyun (National Academy of Agricultural Science, Rural Development Administration (RDA))
Lee, Kyungdo (National Academy of Agricultural Science, Rural Development Administration (RDA))
Publication Information
Korean Journal of Remote Sensing / v.28, no.6, 2012 , pp. 597-609 More about this Journal
Abstract
Leaf area index (LAI) is important in explaining the ability of the crop to intercept solar energy for biomass production and in understanding the impact of crop management practices. This paper describes a procedure for estimating LAI as a function of image-derived vegetation indices from temporal series of IKONOS, Landsat TM, and MODIS satellite images using empirical models and demonstrates its use with data collected at Missouri field sites. LAI data were obtained several times during the 2002 growing season at monitoring sites established in two central Missouri experimental fields, one planted to soybean (Glycine max L.) and the other planted to corn (Zea mays L.). Satellite images at varying spatial and spectral resolutions were acquired and the data were extracted to calculate normalized difference vegetation index (NDVI) after geometric and atmospheric correction. Linear, exponential, and expolinear models were developed to relate temporal NDVI to measured LAI data. Models using IKONOS NDVI estimated LAI of both soybean and corn better than those using Landsat TM or MODIS NDVI. Expolinear models provided more accurate results than linear or exponential models.
Keywords
leaf area index; NDVI; IKONOS; Landsat TM; MODIS; Missouri;
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Times Cited By KSCI : 3  (Citation Analysis)
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1 Jongschaap, R.E.E., 2006. Run-time calibration of simulation models by integrating remote sensing estimates of leaf area index and canopy nitrogen, European Journal of Agronomy, 24: 328-336.
2 Kim, S.H. and K.S. Lee, 2003. Local validation of MODIS global Leaf Area Index (LAI) product over temperate forest, Korean Journal of Remote Sensing, 19(1): 1-9.   과학기술학회마을   DOI
3 Kitchen, N.R., Sudduth, K.A., and Drummond, S.T., 1999. Soil electrical conductivity as a crop productivity measure for claypan soils, Journal of Production Agriculture, 12: 607- 617.   DOI   ScienceOn
4 Launay, M. and M. Guerif, 2005. Assimilating remote sensing data into a crop model to improve predictive performance for spatial applications, Agriculture, Ecosystems & Environment, 111: 321-339.   DOI
5 Lee, J.H., J. Goudriaan, and H. Challa, 2003. Using the expolinear growth equation for modelling crop growth in year-round cut chrysanthemum, Annals of Botany, 92: 697-708.   DOI   ScienceOn
6 Lee, K.S., S.H. Kim, J.H. Park, T.G. Kim, Y.I. Park, and C.S. Woo, 2006. Estimation of forest LAI in close canopy situation using optical remote sensing data, Korean Journal of Remote Sensing, 22(5): 305-311.   과학기술학회마을   DOI
7 Mahiny, A.S. and B.J. Turner, 2007. A comparison of four common atmospheric correction methods, Photogrammetric Engineering and Remote Sensing, 73(4): 361-368.   DOI
8 Pagnutti, M., R.E. Ryan, M. Kelly, K. Holekamp, V. Zanoni, K. Thome, and S. Schiller, 2003. Radiometric characterization of IKONOS multispectral imagery, Remote Sensing of Environment, 88: 53-68.   DOI
9 Anderson, M.C., C.M.U. Neale, F. Li, J.M. Norman, W.P. Kustas, H. Jayanthi, and J. Chavez, 2004. Upscaling ground observations of vegetation water content, canopy height, and leaf area index during SMEX02 using aircraft and Landsat imagery, Remote Sensing of Environment, 92: 447-464.   DOI   ScienceOn
10 Baret, F. and G. Guyot, 1991. Potentials and limits of vegetation indices for LAI and aFAR assessment, Remote Sensing of Environment, 35: 161-173.   DOI
11 Breunig, F.M., L.S. Galvao, A.R. Formaggio, and J.C.N. Epiphanio, 2011. Directional effects on NDVI and LAI retrievals from MODIS: A case study in Brazil with soybean, International Journal of Applied Earth Observation and Geoinformation, 13: 34-42.   DOI
12 Chander, G., B.L. Markham, and J.A. Barsi, 2007. Revised Landsat-5 Thematic Mapper radiometric calibration, IEEE Geoscience and Remote Sensing Letters, 4(3): 490-494.   DOI
13 Cohen, W.B., T.K. Maiersperger, Z. Yang, S.T. Gower, D.P. Turner, W.D. Ritts, M. Berterretche, and S.W. Running, 2003. Comparisons of land cover and LAI estimates derived from ETM+ and MODIS for four sites in North America: a quality assessment of 2000/2001 provisional MODIS products, Remote Sensing of Environment, 88: 233-255.   DOI   ScienceOn
14 Curran, P.J., J. Dungan, and H.L. Gholz, 1992. Seasonal LAI measurements in slash pine using Landsat TM, Remote Sensing of Environment, 39: 3-13.   DOI
15 Dial, G., H. Bowen, F. Gerlach, J. Grodecki, and R. Oleszczuk, 2003. IKONOS satellite, imagery, and products, Remote Sensing of Environment, 88: 23-36.   DOI   ScienceOn
16 Doraiswamy, P.C., T.R. Sinclair, S. Hollinger, B. Akhmedov, A. Stern, and J. Prueger, 2005. Application of MODIS derived parameters for regional crop yield assessment, Remote Sensing of Environment, 97: 192-202.   DOI
17 Thenkabail, P.S., R.B. Smith, and E. De Pauw, 2000. Hyperspectral vegetation indices for determining agricultural crop characteristics, Remote Sensing of Environment, 71: 158-182.   DOI   ScienceOn
18 Peddle, D.R., F.R. Hall, and E.F. LeDrew, 1999. Spectral mixture analysis and geometricoptical reflectance modeling of boreal forest biophysical structure, Remote Sensing of Environment, 67: 288-297.   DOI
19 Potithep, S., N.K. Nasahara, H. Muraoka, S. Nagai, R. Suzuki, 2010. What is the actual relationship between LAI and VI in a deciduous broadleaf forest?, International Archives of the Photogrammetry, Remote Sensing and Spatial Information Science, 11152(8): 609-614.
20 Tagesson, T., 2006. Indirect estimations and spatial variation in Leaf Area Index of coniferous, deciduous and mixed forest stands in Forsmark and Laxemar, SKB TR-06-29 (Stockholm:Swedish Nuclear Fuel and Waste Management Co.).
21 Turner, D., W. Cohen, R. Kennedy, K. Fassnacht, and J. Briggs, 1999. Relationships between leaf area index and Landsat TM spectral vegetation indices across three temperature zone sites, Remote Sensing of Environment, 70: 52-68.   DOI
22 Wang, Q., S. Adiku, J. Tenhunen, and A. Granier, 2005. On the relationship of NDVI with leaf area index in a deciduous forest site, Remote Sensing of Environment, 94: 244-255.   DOI
23 Wardlow, B.D., S.L. Egbert, and J.H. Kastens, 2007. Analysis of time-series MODIS 250 m vegetation index data for crop classification in the U.S. Central Great Plains, Remote Sensing of Environment, 108: 290-310.   DOI
24 Watson, D.J., 1947. Comparative physiological studies on the growth of field crops: I. Variation in net assimilation rate and leaf area between species and varieties, and with and between years. Annual. Botany, (N.S.), 11: 41-76.   DOI
25 Hong, S.Y., K.A. Sudduth, N.R. Kitchen, C.W. Fraisse, H.L. Palm, and W.J. Wiebold, 2004. Comparison of remote sensing and crop growth models for estimating within-field LAI variability, Korean Journal of Remote Sensing, 20(3): 175-188.   과학기술학회마을   DOI
26 Fraisse, C.W., K.A. Sudduth, N.R. Kitchen, 2001. Calibration of the ceres-maize model for simulating site-specific crop development and yield on claypan soils, Applied Engineering in Agriculture. 17(4): 547-556.
27 Gonzalez-Sanpedro, M.C., T.L. Toan, J. Moreno, L. Kergoat, and E. Rubio, 2008. Seasonal variations of leaf area index of agricultural fields retrieved from Landsat data, Remote Sensing of Environment, 112: 810-824.   DOI
28 Hatfield, J.L., C.D. Stanley, and R.E. Carlson, 1976. Evaluation of an electronic foliometer to measure leaf area in corn and soybean, Agronomy Journal, 68: 434-436.   DOI
29 Huete, A., C. Justice, and H. Liu, 1994. Development of vegetation and soil indices for MODISEOS, Remote Sensing of Environment, 49: 224-234.   DOI
30 Huete, A., K. Didan, T. Miura, E.P. Rodriguez, X. Gao, and L.G. Ferreira, 2002. Overview of the radiometric and biophysical performance of the MODIS vegetation indices, Remote Sensing of Environment, 83: 195-213.   DOI   ScienceOn
31 Iowa State University of Science and Technology Cooperative Extension Service, 1997a. How a corn plant develops, Special Report No. 48.
32 Iowa State University of Science and Technology Cooperative Extension Service, 1997b. How a soybean plant develops, Special Report No. 53.
33 Jensen, J.R., 2000. Remote sensing of the environment; An earth resource perspective, Prentice Hall, p.197
34 Zheng, G. and L.M. Moskal, 2009. Retrieving leaf area index (LAI) using remote sensing: Theories, methods and sensors, Sensors, 9: 2719-2745.   DOI
35 Wiegand, C.L., A.J. Richardson, D.E. Escobar, and A.H. Gerbermann, 1991. Vegetation indices in crop assessments, Remote Sensing of Environment, 35: 105-119.   DOI