Training a neural network with a canopy reflectance model to estimate crop leaf area index
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Frédéric Baret | F. M. Danson | F. Baret | C. Rowland | Cs Rowland | F. Baret | F. Danson
[1] F. M. Danson,et al. Extraction of vegetation biophysical parameters by inversion of the PROSPECT + SAIL models on sugar beet canopy reflectance data. Application to TM and AVIRIS sensors , 1995 .
[2] F. M. Danson,et al. Diurnal water stress in sugar beet: Spectral reflectance measurements and modelling , 2000 .
[3] Frédéric Baret,et al. Maximum information exploitation for canopy characterization by remote sensing. , 2000 .
[4] Bernard Pinty,et al. Designing optimal spectral indexes for remote sensing applications , 1996, IEEE Trans. Geosci. Remote. Sens..
[5] F. Baret,et al. Potentials and limits of vegetation indices for LAI and APAR assessment , 1991 .
[6] R. Myneni,et al. Investigation of a model inversion technique to estimate canopy biophysical variables from spectral and directional reflectance data , 2000 .
[7] Michael T. Manry,et al. Surface parameter retrieval using fast learning neural networks , 1993 .
[8] N. Goel. Models of vegetation canopy reflectance and their use in estimation of biophysical parameters from reflectance data , 1988 .
[9] James L. McClelland,et al. Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations , 1986 .
[10] A. Huete. A soil-adjusted vegetation index (SAVI) , 1988 .
[11] Michael T. Manry,et al. Attributes of neural networks for extracting continuous vegetation variables from optical and radar , 1998 .
[12] Pinty Bernard,et al. Designing Optimal Spectral Indices for Remote Sensing Applications , 1996 .
[13] Geoffrey E. Hinton,et al. Learning internal representations by error propagation , 1986 .
[14] P. Atkinson,et al. Introduction Neural networks in remote sensing , 1997 .
[15] W. Verhoef. Light scattering by leaf layers with application to canopy reflectance modelling: The SAIL model , 1984 .
[16] M. S. Moran,et al. Opportunities and limitations for image-based remote sensing in precision crop management , 1997 .
[17] F. Baret,et al. PROSPECT: A model of leaf optical properties spectra , 1990 .
[18] A. Strahler,et al. Forward and inverse modelling of canopy directional reflectance using a neural network , 1998 .
[19] Bruno Andrieu,et al. Candidate high spectral resolution infrared indices for crop cover , 1993 .
[20] A. J. Richardsons,et al. DISTINGUISHING VEGETATION FROM SOIL BACKGROUND INFORMATION , 1977 .
[21] F. Baret,et al. Improving canopy variables estimation from remote sensing data by exploiting ancillary information. Case study on sugar beet canopies , 2002 .
[22] J. Poesen,et al. The European Soil Erosion Model (EUROSEM): A dynamic approach for predicting sediment transport from fields and small catchments. , 1998 .
[23] James A. Smith,et al. LAI inversion using a back-propagation neural network trained with a multiple scattering model , 1993, IEEE Trans. Geosci. Remote. Sens..
[24] T. Faurtyot. Vegetation water and dry matter contents estimated from top-of-the-atmosphere reflectance data: A simulation study , 1997 .
[25] A. Huete,et al. A Modified Soil Adjusted Vegetation Index , 1994 .
[26] Peng Gong,et al. Inverting a canopy reflectance model using a neural network , 1999 .
[27] J. Clevers,et al. The robustness of canopy gap fraction estimates from red and near-infrared reflectances: A comparison of approaches , 1995 .