Validation of neural net techniques to estimate canopy biophysical variables from remote sensing data

Agronomie

Volumn 22, Issue 6, 2002, Pages 547-553

  Weiss, Marie ^a   Baret, Frédéric ^a   Leroy, Marc ^a   Hautecœur, Olivier ^a   Bacour, Cédric ^a   Prévot, Laurent ^a   Bruguier, Nadine ^a  

^a Noveltis   (France)

Author keywords

Canopy structure variables; Cover fraction; Leaf area index; Neural networks; Remote sensing

Indexed keywords

BIDIRECTIONAL REFLECTANCE; CANOPY; POLDER; REFLECTANCE; REMOTE SENSING;

HELIANTHUS; MEDICAGO SATIVA; RESEDA; TRITICUM AESTIVUM; ZEA MAYS;

EID: 0036035497     PISSN: 02495627     EISSN: None     Source Type: Journal    
DOI: 10.1051/agro:2002036     Document Type: Article

References (42)

1. Andrieu B., Baret F., Jacquemoud S., Malthus T., Steven M., Evaluation of an improved version of SAIL model to simulate bidirectional reflectance of sugar beet canopies, Remote Sens. Environ. 60 (1997) 247-257.
2. Asrar G., Fuchs M., Kanemasu E.T., Hatfield J.L., Estimating absorbed photosynthetic radiation and leaf area index from spectral reflectance in wheat, Agron. J. 76 (1984) 300-306.
3. Badwahr G.D., Bunnik N.J.J., Verhoef W., Comparative Study of Suits and SAIL canopy reflectance models, Remote Sens. Environ. 17 (1985) 179-195.
4. Baret F., ReSeDA. Assimilation of Multisensor and Multitemporal Remote Sensing Data to Monitor Soil and Vegetation Functioning, Final Report, ENV4CT960326, 2000.
5. Baret F., Andrieu B., Steven M.D., Gap frequency and canopy architecture of sugar beet and wheat crops, Agric. For. Meteorol. 65 (1993) 261-279.
6. Baret F., Guyot G., Potentials and limits of vegetation indices for LAI and APAR assessment, Remote Sens. Environ. 35 (1991) 161-173.
7. Bicheron P., Leroy M., A method of biophysical parameter retrieval at global scale by inversion of a vegetation reflectance model, Remote Sens. Environ. 67 (1999) 251-266.
8. Bishop C.M., Neural Network for Pattern Recognition, 1995.
9. Carlson T.N., Ripley D.A., On the relation between NDVI, fractional vegetation cover and leaf area index, Remote Sens. Environ. 62 (1997) 241-252.
10. Chelle M., Andrieu B., The nested radiosity model for the distribution of light within plant canopies, Ecol. Model. 111 (1998) 75-91.
11. Deschamps P.Y., Bréon F.M., Leroy M., Podaire A., Bricaud A., Buriez J.C., Sèze G., The POLDER mission: Instrument characteristics and scientific objectives, IEEE Trans. Geosci. Remote Sens. 32 (1994) 598-611.
12. Engelsen O., Pinty B., Verstraete M., Martonchik J.V., Parametric Bidirectional Reflectance Factor Models: Evaluation, Improvements and Applications, Ispra, Italy, 1996.
13. Espana M.L., Simulation de la Variation Temporelle, Directionnelle et Spectrale de la Réflectance des Cultures de Maïs à Partir d'un Modèle Dynamique de la Structure 3D du Couvert, Université de Marne-la-Vallée, France, 1997.
14. Gobron N., Pinty B., Verstraete M.M., Goaverts Y.M., MERIS global vegetation index (MVGI): Description and preliminary application, Int. J. Remote Sens. 20 (1999) 1917-1927.
15. Goel N.S., Thompson R.L., Inversion of a vegetation canopy reflectance model for estimating agronomic variables. III. Estimating using only canopy reflectance data as illustrated by the Suits model, Remote Sens. Environ. 15 (1984) 223-236.
16. Hall F.G., Townshend J.R., Engman E.T., Status of remote sensing algorithms for estimation of land surface state parameters, Remote Sens. Environ. 51 (1995) 138-156.
17. Hornik K., Stinchcombe M., White H., Multilayer feed forward networks are universal approximators, Neural Netw. 2 (1989) 359-366.
18. Hosgood B., Jacquemoud S., Andreoli G., Verdebout J., Pedrini G., Schmuck G., Leaf Optical Properties Experiment 93 (LOPEX93), Ispra (Italy), 1995.
19. Jacquemoud S., Inversion of the PROSPECT+SAIL canopy reflectance model from AVIRIS equivalent spectra: Theoretical study, Remote Sens. Environ. 44 (1993) 282-292.
20. Jacquemoud S., Baret F., PROSPECT: A model of leaf optical properties spectra, Remote Sens. Environ. 34 (1990) 75-91.
21. Jacquemoud S., Baret F., Hanocq J.F., Modeling spectral and directional soil reflectance, Remote Sens. Environ. 41(1992) 123-132.
22. Jin Y.Q., Liu C., Biomass retrieval from high dimensional active/passive remote sensing data by using artificial neural networks, Int. J. Remote Sens. 18 (1997) 4 971-979.
23. Justice C.O., Vermote E., Townshend R.G., The moderate resolution imaging spectroradiometer (MODIS): Land remote sensing for global change research, IEEE Trans. Geosci. Remote Sens. 36 (1998) 1228-1249.
24. Kimes D.S., Knyazikhin Y., Privette J.L., Abuelgasim A.A., Gao F., Inversion methods for physically-based models, Remote Sens. Rev. 18 (2000) 381-439.
25. Kimes D.S., Nelson R.F., Manry M.T., Fung A.K., Attributes of neural networks for extracting continuous vegetation variables from optical and radar measurements, Int. J. Remote Sens. 19 (1998) 2639-2663.
26. Knyazikhin Y., Martonchik J.V., Myneni R.B., Diner D.J., Running S.W., Synergistic algorithm for estimating vegetation canopy leaf area index and fraction of absorbed photosynthetically active radiation from MODIS and MISR data, J. Geophys. Res. 103 (1998) D24 32257-32275.
27. Kuusk A., The hot spot effect in plant canopy reflectance, in: Myneni R.B., Ross J. (Eds.), Photon-Vegetation Interactions, Springer Verlag, 1991, pp. 139-159.
28. Nelder J.A., Mead R., A simplex method for function minimization, Comput. J. 7 (1987) 308-313.
29. Nilson T., A theoretical analysis of the frequency of gaps in plant stands, Agric. Meteorol. 8 (1971) 25-38.
30. Pinty B., Verstraete M.M., Extracting information on surface properties from bidirectional reflectance measurements, J. Geophys. Res. 96 (1991) D2 2865-2874.
31. Rouse J.W., Haas R.H., Schell J.A., Deering D.W., Harlan J.C., Monitoring the Vernal Advancement of Retrogradation of Natural Vegetation, 1974, 371 p.
32. Rummelhart D.E., Hinton G.E., Williams R.J., Learning internal representations by error propagation, in: Rummelhart D., McClelland J. (Eds.), Parallel Data Processing, MIT Press, Cambridge, 1986, pp. 318-362.
33. Sarle W.S., Stopped training and other remedies for overfitting, Proceedings of the 27th Symposium on the Interface of Computing Science and Statistics, 1995, pp. 352-360.
34. Shell G.S.G., Lang A.R.G., Sale P.J.M., Quantitative measures of leaf orientation and heliotropic response in sunflower, bean, pepper and cucumber, Agric. Meteorol. 13 (1974) 24-37.
35. Smith J.A., LAI inversion using a backpropagation neural network trained with multiple scattering model, IEEE Trans. Geosci. Remote Sens. 31 (1993) 1102-1106.
36. Verhoef W., Light scattering by leaf layers with application to canopy reflectance modeling: The SAIL model, Remote Sens. Environ. 16 (1984) 125-141.
37. Verhoef W., Earth observation modeling based on layer scattering matrices, Remote Sens. Environ. 17 (1985) 165-178.
38. Wanner W., Li X., Strahler A.H., On the derivation of kernels for kernel-driven models of bidirectional reflectance, J. Geophys. Res. 100 (1995) 21077-21089.
39. Weiss M., Baret F., Evaluation of canopy biophysical variable retrieval performances from the accumulation of large swath satellite data, Remote Sens. Environ. 70 (1999) 293-306.
40. Weiss M., Baret F., Myneni R.B., Pragnère A., Knyazikhin Y., Investigation of a model inversion technique to estimate canopy biophysical variables from spectral and directional reflectance data, Agronomie 20 (2000) 3-22.
41. Weiss M., Jacob F., Baret F., Pragnère A., Bruchou C., Leroy M., Hautecœur O., Prévot L., Bruguier N., Evaluation of kernel-driven BRDF models for normalization of Alpilles/ReSeDA POLDER data, Agronomie 22 (2002) 531-536.
42. Yang Z.L., Dai Y., Dickinson R.E., Shuttleworth W.J., Sensitivity of ground heat flux to vegetation cover fraction and leaf area index, J. Geophys. Res. 104 (1999) 19505-19514.