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2015 | 29 | 2 |
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Comparative evaluation of inversion approaches of the radiative transfer model for estimation of crop biophysical parameters

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The inversion of canopy reflectance models is widely used for the retrieval of vegetation properties from remote sensing. This study evaluates the retrieval of soybean biophysical variables of leaf area index, leaf chlorophyll content, canopy chlorophyll content, and equivalent leaf water thickness from proximal reflectance data integrated broad bands corresponding to moderate resolution imaging spectroradiometer, thematic mapper, and linear imaging self scanning sensors through inversion of the canopy radiative transfer model, PROSAIL. Three different inversion approaches namely the look-up table, genetic algorithm, and artificial neural network were used and performances were evaluated. Application of the genetic algorithm for crop parameter retrieval is a new attempt among the variety of optimization problems in remote sensing which have been successfully demonstrated in the present study. Its performance was as good as that of the look-up table approach and the artificial neural network was a poor performer. The general order of estimation accuracy for para-meters irrespective of inversion approaches was leaf area index > canopy chlorophyll content > leaf chlorophyll content > equivalentleaf water thickness. Performance of inversion was comparable for broadband reflectances of all three sensors in the optical region with insignificant differences in estimation accuracy among them.
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  • Division of Agricultural Physics, Indian Agricultural Research Institute, New Delhi-110012, India
  • Division of Agricultural Physics, Indian Agricultural Research Institute, New Delhi-110012, India
  • Division of Agricultural Physics, Indian Agricultural Research Institute, New Delhi-110012, India
  • Division of Agricultural Physics, Indian Agricultural Research Institute, New Delhi-110012, India
  • Division of Agricultural Physics, Indian Agricultural Research Institute, New Delhi-110012, India
  • Division of Agricultural Physics, Indian Agricultural Research Institute, New Delhi-110012, India
  • Division of Agricultural Physics, Indian Agricultural Research Institute, New Delhi-110012, India
  • Department of Civil Engineering, Indian Institute of Science, Bangalore 560 012, India
  • Atkinson P.M. and Tatnall A.R.L., 1997. Neural networks in remote sensing. Int. J. Remote Sensing, 18(4), 699-709.
  • Bacour C., Jacquemoud S., Leroy M., Hautecoeur O., Weiss M., and Prevot L., 2002. Reliability of the estimation of vegetation characteristics by inversion of three canopy reflectance models on airborne polder data. Agronomie: Agric. Environ., 22, 555-565.
  • Cohen W.B., Maiersperger T.K., Gower S.T. and Turner D.P., 2003. An improved strategy for regression of biophysical variables and Landsat ETM+ data. Remote Sensing Environ., 84(4), 561-571.
  • Colombo R., Bellingeri D., Fasolini D., and Marino C.M., 2003. Retrieval of leaf area index in different vegetation types using high resolution satellite data. Remote Sensing Environ., 86(1), 120-131.
  • Combal B., Baret F., Weiss M., Trubull A., Macc D., and Pragnere A., 2002. Retrieval of canopy biophysical variables from bi-directional reflectance using prior information to solve the ill-posed inverse problems. Remote Sensing Environ., 84, 1-15.
  • Darvishzadeh R., Skidmore A., Schlerf M., and Atzberger C., 2008. Inversion of a radiative transfer model for estimating vegetation LAIand chlorophyll in heterogeneous grassland. Remote Sensing Environ., 112, 2592-2604.
  • Durbha S.S., King R.L., and Younan N.H., 2007. Support vector machines regression for retrieval of leaf area index from multi-angle imaging spectroradiometer. Remote Sensing Environ., 107, 348-361.
  • Fang H. and Liang S., 2005. A hybrid inversion method for mapping leaf area index from MODIS data: Experiments and application to broadleaf and needle leaf canopies. Remote Sensing Environ., 94, 405-424.
  • Fang H., Liang S., and Kuusk A., 2003. Retrieving leaf area index using a genetic algorithm with a canopy radiative transfer model. Remote Sensing Environ., 85, 257-270.
  • Goldberg D.E., 1989. Genetic algorithms in search, optimization and machine learning. Reading, MA: Addison-Wesley.
  • Hilker T., Lepine L., Coops N.C. Jassal R.S., Black T.A., Wulder M.A., Ollinger S., Tsuia O., and Day M., 2011. Assessing the impact of N-fertilization on biochemical com-position and biomass of a Douglas-fir canopy – A remote sensing approach. Agric. Forest Meteorol. Agric. Forest Meteorol., 153,124-133.
  • Houborg R., Soegaard H., and Boegh E., 2007. Combining vegetation index and model inversion methods for the extraction of key vegetation biophysical parameters using Terra and Aqua MODIS reflectance data. Remote Sensing Environ., 106(1), 39-58.
  • Jacquemoud S., Bacour C., Poilve H., and Frangi J.P., 2000. Comparison of four radiative transfer models to simulate plant canopies reflectance: Direct and inverse mode. Remote Sensing Environ., 74(3), 471-481.
  • Jacquemoud S. and Baret F., 1990. PROSPECT: A model of leaf optical properties spectra. Remote Sensing Environ., 34(2), 75-91.
  • Jacquemoud S., Baret F., Andrieu B., Danson F.M., and Jaggard K., 1995. Extraction of vegetation biophysical parameters by inversion of the PROSPECT + SAIL models on sugar beet canopy reflectance data. Application to TM and AVIRIS sensors. Remote Sens. Environ., 52(3), 163-172.
  • Jacquemoud S., Ustin S.L., Verdebout J., Schmuck G., Andreoli G., and Hosgood B., 1996. Estimating leaf bio­chemistry using the PROSPECT leaf optical properties model. Remote Sensing Environ., 56, 194-202.
  • Jacquemoud S., Verhoef W., Baret F., Bacour C., Zarco-Tejada P.J., Asner G.P., François C., and Ustin S.L., 2009. PROSPECT+SAIL models: A review of use for vegetation characterization. Remote Sensing Environ., 113, 56-66.
  • Jin X., Xu C., Zhang Q., and Singh,, V.P., 2010. Parameter and modeling uncertainty simulated by GLUE and a formal Bayesian method for a conceptual hydrological model. J. Hydrol., 383, 147-155.
  • Kimes D.S., Nelson R.F., Manry M.T., and Fung A.K., 1998. Attributes of neural networks for extracting continuous vegetation variables from optical and radar measurements. Int. J. Remote Sens., 19(14), 2639-2662.
  • Koetz B., Baret F., Poilve H., and Hill J., 2005. Use of coupled canopy structure dynamic and radiative transfer models to estimate biophysical canopy characteristics. Remote Sensing Environ., 95, 115-124.
  • Liang S., 2007. Recent developments in estimating land surface bio geophysical variables from optical remote sensing. Progress Physical Geography, 31(5), 501-516.
  • Ngia L.S.H. and Sjoberg J., 2000. Efficient training of neural nets for nonlinear a adaptive filtering using a recursive Levenberg-Marquardt algorithm. IEEE Trans. Signal Proc., 48, 1915-1927.
  • Rasmussen M., 1997. Operational yield forecast using AVHRR NDVI data: Reduction of environmental and inter-annual variability. Int. J. Remote Sensing, 18(5), 1059-1077.
  • Renders J-M., Flasse S.P., Verstraete M.M., and Nordvik J-P., 1992. A comparative study of optimization methods for the retrieval of quantitative information from satellite data. Joint Research Center Report EUR 14851, Brussels.
  • Roman M.O., Gatebe C.K., Schaaf C.B., Poudyal R., Wang Z., and King M.D., 2011. Variability in surface BRDF at different spatial scales (30 m – 500 m) over a mixed agricultural landscape as retrieved from airborne and satellite spectral measurements. Remote Sensing Environ., 115, 2184-2203.
  • Tripathi R., Sahoo R.N., Sehgal V.K., and Tomar R.K., and Chakraborty Nagarajan S., 2012. Inversion of PROSAIL Model for Retrieval of Plant Biophysical Parameters J. Indian Soc. Remote Sensing, 40(1), 19-28.
  • Verger A., Baret F., and Camacho F., 2011. Optimal modalities for radiative transfer neural network estimation of canopy biophysical characteristics: Evaluation over an agricultural area with CHRIS/PROBA observations. Remote Sensing Environ.t, 115, 415-426.
  • Verhoef W., 1984. Light scattering by leaf layers with application to canopy reflectance modelling: The SAIL model. Remote Sensing Environ., 16(2), 125-141.
  • Vohland M., Mader S., and Dorigo W., 2010. Applying different inversion techniques to retrieve stand variables of summer barley with PROSPECT + SAIL. Int. J. Appl. Earth Observation Geoinformation, 12, 71-80.
  • Walthall C., Dulaney W., Anderson M., Norman J., Fang H., and Liang S., 2004. A comparison of empirical and neural network approaches for estimating corn and soybean leaf area index from Landsat ETM+ imagery. Remote Sensing Environ., 92(4), 465-474.
  • Wang Y. and Jin Y., 2000. A genetic algorithm to simultaneously retrieve land surface roughness and soil moisture. J. Remote Sensing (Chinese), 4(2), 90-94.
  • Zarco-Tejada P.J., Miller J.R., Mohammed G.H., Noland T.L., and Simpsom P.H., 2001. Scaling-up and model inversion methods with narrow-band optical indices for chlorophyll content estimation in closed forest canopies with hyperspectral data. IEEE Trans. Geoscience Remote Sensing, 39(7), 1491-1507.
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