Applying different inversion techniques to retrieve stand variables of summer barley with PROSPECT + SAIL

International Journal of Applied Earth Observation and Geoinformation

Volumn 12, Issue 2, 2010, Pages 71-80

  Vohland, M ^a   Mader, S ^a   Dorigo, W ^b  

^a UNIVERSITY OF TRIER   (Germany)

^b VIENNA UNIVERSITY OF TECHNOLOGY   (Austria)

Author keywords

Artificial neural network; Canopy reflectance modelling; Look up table inversion; Numerical optimization; PROSPECT; SAIL

Indexed keywords

ARTIFICIAL NEURAL NETWORK; BARLEY; CANOPY REFLECTANCE; DATA INVERSION; LEAF AREA INDEX; MODELING; OPTIMIZATION; REMOTE SENSING;

HORDEUM;

EID: 77958465081     PISSN: 15698432     EISSN: 1872826X     Source Type: Journal    
DOI: 10.1016/j.jag.2009.10.005     Document Type: Article

References (55)

1. Bacour, C., Jacquemoud, S., Leroy, M., Hautecœur, O., Weiss, M., Prévot, L., Bruguier, N., Chauki, H., 2002. Reliability of the estimation of vegetation characteristics by inversion of three canopy reflectance models on airborne POLDER data. Agronomie 22, 555-565.
2. ab, from top of canopy MERIS reflectance data: principles and validation. Remote Sensing of Environment 105, 313-325.
3. Baret, F., Buis, S., 2008. Estimating canopy characteristics from remote sensing observations. Review of methods and associated problems. In: Liang, S. (Ed.), Advances in Land Remote Sensing: System, Modeling, Inversion and Application. Springer, Netherlands, pp. 173-202.
4. Baret, F., Fourty, T., 1997. Estimation of leaf water content and specific leaf weight from reflectance and transmittance. Agronomie 17, 455-464.
5. Boegh, E., Soegaard, H., Broge, N., Hasager, C.B., Jensen, N.O., Schelde, K., Thomsen, A., 2002. Airborne multispectral data for quantifying leaf area index, nitrogen concentration, and photosynthetic efficiency in agriculture. Remote Sensing of Environment 81, 179-193.
6. Botha, E.J., Leblon, B., Zebarth, B., Watmough, J., 2007. Non-destructive estimation of potato leaf chlorophyll from canopy hyperspectral reflectance using the inverted PROSAIL model. International Journal of Applied Earth Observation and Geoinformation 9, 360-374.
7. Casa, R., Jones, H.G., 2004. Retrieval of crop canopy properties: a comparison between model inversion from hyperspectral data and image classification. International Journal of Remote Sensing 25, 1119-1130.
8. Combal, B., Baret, F., Weiss, M., Trubuil, A., Macé, D., Pragnère, A., Myneni, R., Knyazikhin, Y., Wang, L., 2002. Retrieval of canopy biophysical variables from bidirectional reflectance using prior information to solve the ill-posed inverse problem. Remote Sensing of Environment 84, 1-15.
9. Combal, B., Baret, F., Weiss, M., 2002. Improving canopy variables estimation from remote sensing data by exploiting ancillary information. Case study on sugar beet canopies. Agronomie 22, 205-215.
10. Doraiswamy, P.C., Moulin, S., Cook, P.W., Stern, A., 2003. Crop yield assessment from remote sensing. Photogrammetric Engineering and Remote Sensing 69, 665-674.
11. Dorigo, W., 2008. Retrieving canopy variables by radiative transfer model inversion-a regional approach for imaging spectrometer data. Ph.D. Thesis, Faculty of Life Sciences, TU München Germany.
12. Dorigo, W.A., Zurita-Milla, R., de Wit, A.J.W., Brazile, J., Singh, R., Schaepman, M.E., 2007. A review on reflective remote sensing and data assimilation techniques for enhanced agroecosystem modeling. International Journal of Applied Earth Observation and Geoinformation 9, 165-193.
13. Duke, C., Guérif, M., 1998. Crop reflectance estimate errors from the SAIL model due to spatial and temporal variability of canopy and soil characteristics. Remote Sensing of Environment 66, 286-297.
14. Durbha, S.S., King, R.L., Younan, N.H., 2007. Support vector machines regression for retrieval of leaf area index from multiangle imaging spectroradiometer. Remote Sensing of Environment 107, 348-361.
15. Fang, H., Liang, S., Kuusk, A., 2003. Retrieving leaf area index using a generic algorithm with a canopy reflectance model. Remote Sensing of Environment 85, 257-270.
16. Fourty, T., Baret, F., 1997. Vegetation water and dry matter contents estimated from top-of-the-atmosphere reflectance data: a simulation study. Remote Sensing of Environment 61, 34-45.
17. González-Sanpedro, M.C., Le Toan, T., Moreno, J., Kergoat, L., Rubio, E., 2008. Seasonal variations of leaf area index of agricultural fields retrieved from Landsat data. Remote Sensing of Environment 112, 810-824.
18. Hornik, K., Strinchombe, M., White, H., 1989. Multilayer feedforward networks are universal approximators. Neural Networks 2, 359-366.
19. Houborg, R., Boegh, E., 2008. Mapping leaf chlorophyll and leaf area index using inverse and forward canopy reflectance modeling and SPOT reflectance data. Remote Sensing of Environment 112, 186-202.
20. Houborg, R., Soegaard, H., 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 of Environment 106, 39-58.
21. Igel, C., Hüsken, M., 2000. Improving the Rprop learning algorithm. In: Proc. Second International ICSC Symposium on Neural Computation. pp. 115-121.
22. Jacquemoud, S., Baret, F., 1990. PROSPECT: a model of leaf optical properties spectra. Remote Sensing of Environment 34, 75-91.
23. Jacquemoud, S., Baret, F., 1993. Estimating vegetation biophysical parameters by inversion of a reflectance model on high spectral resolution data. In: Varlet-Grancher, C., Bonhomme, R., Sinoquet, H. (Eds.), Crop Structure and light Microclimate: Characterization and Applications. INRA, Paris, pp. 339-350.
24. Jacquemoud, S., Flasse, S., Verdebout, J., Schmuck, G., 1994. Comparison of several optimization methods to extract canopy biophysical parameters-application to CAESAR data. In: Proc. 6th Int. Symposium Mesures Physiques et Signatures en Télédétection, Noordwijk, pp. 291-298.
25. Jacquemoud, S., Baret, F., Andrieu, B., Danson, F.N., 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 Sensing of Environment 52, 163-172.
26. Jacquemoud, S., Bacour, C., Poilvé, H., Frangi, J.-P., 2000. Comparison of four radiative transfer models to simulate plant canopies reflectance: direct and inverse mode. Remote Sensing of Environment 74, 471-481.
27. Kalman, B.L., Kwasny, S.C., 1992. Why tanh: choosing a sigmoidal function. In: Proc. International Joint Conference on Neural Networks, vol. IV, Baltimore, pp. 578-581.
28. Kimes, D.S., Knyazikhin, Y., Privette, J.L., Abuelgasim, A.A., Gao, F., 2000. Inversion of physically-based models. Remote Sensing Reviews 18, 381-439.
29. Kneubühler, M., 2002. Spectral assessment of crop phenology based on spring wheat and winter barley. Ph.D. Thesis, University of Zürich, Switzerland.
30. Koetz, B., Baret, F., Poilvé, H., Hill, J., 2005. Use of coupled canopy structure dynamic and radiative transfer models to estimate biophysical canopy characteristics. Remote Sensing of Environment 95, 115-124.
31. Le Maire, G., François, C., Dufrêne, E., 2004. Towards universal broad leaf chlorophyll indices using PROSPECT simulated database and hyperspectral reflectance measurements. Remote Sensing of Environment 89, 1-21.
32. Lewis, R., Torczon, V., Trosset, M., 2000. Direct search methods: then and now. Journal of Computational and Applied Mathematics 124, 191-207.
33. Liang, N.S., 2004. Quantitative Remote Sensing of Land Surfaces. Wiley & Sons, Hoboken, NJ.
34. Major, D.J., Schaalje, G.B., Wiegand, C., Blad, B.L., 1992. Accuracy and sensitivity analyses of SAIL model-predicted reflectance of maize. Remote Sensing of Environment 41, 61-70.
35. Malley, D.F., Martin, P.D., Ben-Dor, E., 2004. Application in analysis of soils. In: Roberts, C.A., Workman, Jr., J., Reeves, III, J.B. (Eds.), Near-infrared Spectroscopy in Agriculture, vol. 44. Agronomy, Madison, pp. 729-783.
36. Markwell, J., Ostermann, J.C., Mitchell, J.J., 1995. Calibration of the Minolta SPAD-502 leaf chlorophyll meter. Photosynthesis Research 46, 467-472.
37. Masters, T., 1993. Practical Neural Network Recipes in C++. Academic Press, San Diego - London.
38. Matthew, M. W., Adler-Golden, S. M., Berk, A., Richtsmeier, S. C., Levine, R. Y., Bernstein, L.S., Acharya, P.K., Anderson, G.P., Felde, G.W., Hoke, M.P., Ratkowski, A., Burke, H.-H., Kaiser, R.D., Miller, D. P., 2000.Status of atmospheric correction using a MODTRAN4-based algorithm. Proceedings of the SPIE 4049, 199-207.
39. Miller, J.R., Steven, M.D., Demetriades-Shah, T.H., 1992. Reflection of layered bean leaves over different soil backgrounds: measured and simulated spectra. International Journal of Remote Sensing 13, 3273-3286.
40. Nelder, J.A., Mead, R., 1965. A simplex method for function minimization. The Computer Journal 7, 308-313.
41. Nguyen, D., Widrow, B., 1990. Improving the learning speed of 2-layer neural networks by choosing initial values of the adaptive weights. In: Proc. International Joint Conference on Neural Networks, vol. III. pp. 212-226.
42. Oppelt, N., Mauser, W., 2004. Hyperspectral monitoring of physiological parameters of wheat during a vegetation period using AVIS data. International Journal of Remote Sensing 25, 145-159.
43. Pragnère, A., Baret, F., Weiss, M., Myneni, R., Knyazikhin, Y., Wang, L.B., 1999. Comparison of three radiative transfer model inversion techniques to estimate canopy biophysical variables from remote sensing data. In: Proc. IGARSS 1999, Geoscience and Remote Sensing Symposium, IEEE 1999 International. pp. 1093-1095.
44. Press, W.H., Teukolsky, S.A., Vetterling, W.T., Flannery, B.P., 1992. Numerical Recipes in C. Cambridge University Press, New York.
45. Privette, J.L., Myneni, R.B., Tucker, C.J., Emery, W.J., 1994. Invertibility of a 1-D discrete ordinates canopy reflectance model. Remote Sensing of Environment 48, 89-105.
46. Qu, Y., Wang, J., Wan, H., Li, X., Zhou, G., 2008. A Bayesian network algorithm for retrieving the characterization of land surface vegetation. Remote Sensing of Environment 112, 613-622.
47. Riedmiller, M., Braun, H., 1993. A direct adaptive method for faster backpropagation learning: the RPROP algorithm. In: Proc. of the IEEE Intl. Conf. on Neural Networks. pp. 586-591.
48. Schläpfer, D., Richter, R., 2002. Geoatmospheric processing of airborne imaging spectrometer data. Part 1. Parametric orthorectification. International Journal of Remote Sensing 23, 2609-2630.
49. Thenkabail, P.S., Enclona, E.A., Ashton, M.S., Van Der Meer, B., 2004. Accuracy assessments of hyperspectral waveband performance for vegetation analysis applications. Remote Sensing of Environment 91, 354-376.
50. Verhoef, W., 1984. Light scattering by leaf layers with application to canopy reflectance modeling: the SAIL model. Remote Sensing of Environment 16, 125-141.
51. Vohland, M., Jarmer, T., 2008. Estimating structural and biochemical parameters for grassland from spectroradiometer data by radiative transfer modelling (PROSPECT + SAIL). International Journal of Remote Sensing 29, 191-209.
52. Vuolo, F., Dini, L., D'Urso, G., 2008. Retrieval of leaf area index from CHRIS/PROBA data: an analysis of the directional and spectral information content. International Journal of Remote Sensing 29, 5063-5072.
53. Walthall, C., Dulaney, W., Anderson, M., Horman, J., Fang, H., 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 of Environment 92, 465-474.
54. Weiss, M., Baret, F., Myneni, R.B., Pragnère, A., Knyazikhin, Y., 2000. Investigation of a model inversion technique to estimate canopy biophysical variables from spectral and directional reflectance data. Agronomie 20, 3-22.
55. Wood, C.W., Reeves, D.W., Himelrick, D.G., 1993. Relationships between chlorophyll meter readings and leaf chlorophyll concentration, N status and crop yield. A review. In: Proc. Agronomy Soc., vol. 23, New Zealand, pp. 1-9.