Abstract
Motivated by the increasingly availability and importance of hyperspectral remote sensing data, this study aims to determine whether current generation narrowband hyperspectral remote sensing data could be used to estimate vegetation Leaf Area Index (LAI) accurately than the traditional broadband multispectral data. A comparative study has been carried out to evaluate the performance of the narrowband Normalized Difference Vegetation Index (NDV1) derived from Hyperion hyperspectral sensor with that of derived from IRS LISS-III for the estimation of LAI of some major agricultural crops (e.g. cotton, sugarcane and rice) in part of Guntur district, India. It has been found that the narrowband NDVI derived from Hyperion has shown better results over its counterpart derived from broadband LISS-III. Linear regression models have been used which with selected subsets of individual Hyperion bands performed better to predict LAI than those based on the broadband datasets, although the potential to overfit models using the large number of available Hyperion bands is a concern for further research.
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References
Baret, R, Guyot, G and Major, D.J. (1989). TSAVI: a vegetation index which minimizes soil brightness effects on LAI and APAR estimation, Proc. 12th Canadian Symposium on Remote Sensing, Vancouver, BC, Canada, 10–14 July, 1990, pp. 1355–1358.
Blackburn, G.A. (1998). Quantifying chlorophylls and caroteniods at leaf and canopy scales: an evaluation of some hyperspectral approaches.Remote Sens. Environ.,66: 273–285.
Carter, GA. (1998). Reflectance bands and indices for remote estimation of photosynthesis and stomatal conductance in pine canopies.Remote Sens. Environ.,63:61–72.
Chen, J. and Cihlar, J. (1996). Retrieving leaf area index of boreal conifer forests using Landsat TM images.Remote Sens. Environ.,55: 153–162.
Chen, Z., Elvidge, C.D. and Groeneveld, D.P. (1998). Monitoring seasonal dynamics of arid land vegetation using AVIRIS data.Remote Sens. Environ.,65: 255- 266.
Collins, W. (1978). Remote sensing of crop type and maturity.Photogramm. Eng. Remote Sens.,44: 43- 55.
Curran, P.J., Dungan, J.L. and Gholz, H.L. (1990). Exploring the relationship between reflectance red edge and chlorophyll content in slash pine.Tree Phys.,7: 33–48.
Curran, P.J., Dungan, J. and Gholz, H.L. (1992). Seasonal LAI measurements in slash pine using Landsat TM.Remote Sens. Environ.,39: 3–13.
Elvidge, C.D. and Lyon, R.J.P. (1985). Influence of rock- soil spectral variation on the assessment of green biomass.Remote Sens. Environ.,17: 265–269.
Elvidge, CD., Chen, Z.K. and Groeneveld, D.P. (1993). Detection of trace quantities of green vegetation using 1990 AVIRIS data.Remote Sens. Environ.,44: 271–279.
Farrand, W.H., Singer, R.B. and Merenyi, E. (1994). Retrieval of apparent surface reflectance from AVIRIS data: a comparison of empirical line, radiative transfer, and spectral mixture methods.Remote Sens. Environ.,47: 311–321.
Gong, P., Pu, R. and Miller, J.R. (1995). Coniferous forest leaf area index estimation along the Oregon transact using compact airborne spectrographic imager data.Photogramm. Eng. Remote Sens.,61: 1107–1117.
Huete, A.R. (1988). A soil-adjusted vegetation index (SAVl).Remote Sens. Environ.,25: 295–309.
Huete, A.R., Jackson, R.D. and Post, D.F. (1985). Spectral response of a plant canopy with different soil backgrounds.Remote Sens. Environ.,17: 37–53.
Kuusk, A. (1998). Monitoring of vegetation parameters on large areas by the inversion of a canopy reflectance model.Int. J. Remote Sensing,19: 2893- 2905.
Mardia, D., Kent, J. and Bibby, J. (1979). Multivariate Analysis. London, U.K.: Academic, pp. 79–82.
Martin M.E. and Aber, J.D. (1997). High spectral resolution remote sensing of forest canopy lignin, nitrogen, and ecosystem processes.Ecol. Appl,7: 431–443.
Moran, M.S., Inoue, Y. and Barnes, E.M. (1997). Opportunities and limitations for image-based remote sensing in precision crop management.Remote Sens. Environ.,61: 319–346.
Myneni, R.B., Ramakrishna, R.N. and Running, S.W. (1997). Estimation of Global Leaf Area Index and Absorbed Par Using Radiative Transfer Models,IEEE Trans.Geosci. Remote Sensing,35: 380–1393.
Pandya, M.R., Chaudhari, K.N., Singh, R.P., Segal, V.K., Bairagi, GD., Sharma, R. and Dadhwal, V.K. (2003). Leaf area index retrieval using IRS LISS-III sensor data and validation of MODIS LAI product over Madhya Pradesh.Current Science,85(12): 1777–1782.
Pearlman, J., Carman, S., Segal, C, Jarecke, P. and Barry, P. (2001). Overview of the Hyperion imaging spectrometer for the NASA EO-1 mission. Proc. IGARSS 2001 held at Sydney, Australia, Oct. 12– 15, pp. 89–102.
Peterson, D.L., Spanner, M.A., Running, S.W. and Teuber, K. (1987). Relationship of thematic mapper data to leaf area index of temperate coniferous forests.Remote Sens. Environ.,22: 323–341.
Roberts, D.S., Smith, M.O. and Adams, J.B. (1993). Green vegetation, nonphotosynthetic vegetation and soils in AVIRIS data.Remote Sens. Environ.,44: 255–269.
Running, S.W., Nemani, R.R., Peterson, D.L., Band, L.E., Potts, D.F., Pierce, L.L. and Spanner, M.A. (1989). Mapping regional forest evapotranspiration and photosynthesis by coupling satellite data with ecosystem simulation.Ecology,70(4): 1090–1101.
Running, S.W., CO. Justice, V. Salmonson, D. Hall, J. Barker, Y.J. Kaufmann, AH. Strahler, Z.M., Wan P, and Carneggie, D. (1994). Terrestrial remote sensing science and algorithms planned for EOS/ MODIS.Int. J. Remote Sensing,15: 3587–3620.
Smith, J.A. (1993). LAI Inversion using a back- propagation neural network trained with a multiple scattering model.IEEE Trans. Geosc. Remote Sensing,31(5): 1102–1106.
Strachan, I.B., Pattey, E. and Biosvert, J.B. (2002). Impact of nitrogen and environmental conditions on corn as detected by hyperspectral reflectance.Remote Sens. Environ.,80(2): 213–224.
Tucker, C.J. (1979). Red and photographic infrared linear combinations for monitoring vegetation.Remote Sens. Environ.,8: 127–150.
Turner, D., Cohen, W., Kennedy, R., Fassnacht, K. and Briggs, J. (1999). Relationships between leaf area index and Landsat TM spectral vegetation indices across three temperate zone sites.Remote Sens. Environ.,70: 2–68.
Ungar, S.G (2001). Overview of EO-I, the first 120 days, Proc. IGARSS 2001, Sydney, Australia, Oct. 12–15, 2001, pp. 45–62.
Wiegand, C.L., Mass, S.J., Aase, J.K., Hatfield, J.L., Pinter, P.J. and Lapitan, R.L. (1992). Multisite analysis of spectral-biophysical data for wheat.Remote Sens. Environ.,42: 1–21.
Yoder, B.J. (1992). Photosynthesis of conifer: influential factors and potentials for remote sensing. Ph.D. Thesis, Oregon State University.
Zhang, X., Friedl, M.A., Schaaf, C.B., Strahler, A.H., Hodges, J.C.F., Gao, F. and Huete, A. (2003). Monitoring vegetation phenology using MODIS.Remote Sens. Environ.,84: 471–475.
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Rao, N.R., Garg, P.K. & Ghosh, S.K. Estimation and comparison of leaf area index of agricultural crops using irs liss-III and EO-1 hyperion images. J Indian Soc Remote Sens 34, 69–78 (2006). https://doi.org/10.1007/BF02990748
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DOI: https://doi.org/10.1007/BF02990748