Training a neural network with a canopy reflectance model to estimate crop leaf area index

References

• Abuelgasim , A. A , Gopal , S and Strahler , A. H . 1998 . Forward and inverse modelling of canopy directional reflectance using a neural network . International Journal of Remote Sensing , 19 : 453 – 471 .
   Web of Science ®Google Scholar
• Atkinson , P. M and Tatnall , A. R. L . 1997 . Neural networks in remote sensing . International Journal of Remote Sensing , 18 : 699 – 709 .
   Web of Science ®Google Scholar
• Baret F 1995 Use of spectral reflectance variation to retrieve canopy biophysical characteristics In Advances in Environmental Remote Sensing, edited by F. M. Danson and S. E. Plummer (Chichester: Wiley), pp. 33–51
   Google Scholar
• Baret , F and Guyot , G . 1991 . Potentials and limits of vegetation indices for LAI and APAR assessment . Remote Sensing of Environment , 35 : 161 – 173 .
   Web of Science ®Google Scholar
• Baret , F , Clevers , J. G. P. W and Steven , M. D . 1995 . The robustness of canopy gap fraction estimates from red and near-infrared reflectances: a comparison of approaches . Remote Sensing of Environment , 54 : 141 – 151 .
   Web of Science ®Google Scholar
• Baret , F , Weiss , M , Troufleau , D , Precot , L and Combal , B . 2000 . Maximum information exploitation for canopy characterization from remote sensing: radiative transfer model inversion and assimilation into canopy functioning model . Aspects of Applied Biology , 60 : 71 – 82 .
   Google Scholar
• Combal , B , Baret , F and Weiss , M . 2001 . Improving canopy variables estimation from remote sensing data by exploiting ancillary information. Case study on sugar beet canopies . Agronomie , 22 : 2 – 15 .
   Google Scholar
• Danson , F. M and Aldakheel , Y. Y . 2000 . Diurnal water stress in sugar beet: spectral reflectance measurements and modelling . Agronomie , 20 : 31 – 39 .
   Google Scholar
• Dawson , M. S , Fung , A. K and Manry , M. T . 1993 . Surface parameter retrieval using fast learning neural networks . Remote Sensing Reviews , 7 : 1 – 18 .
   Google Scholar
• Fourty , T and Baret , F . 1997 . Vegetation and dry matter contents estimated from top-of-the-atmosphere reflectance data: a simulation study . Remote Sensing of Environment , 61 : 34 – 45 .
   Web of Science ®Google Scholar
• Goel , N. S . 1988 . Models of vegetation canopy reflectance and their use in the estimation of biophysical parameters from reflectance data . Remote Sensing Reviews , 4 : 1 – 212 .
   Google Scholar
• Gong , P , Wang , C. X and Liang , S . 1999 . Inverting a canopy reflectance model using a neural network . International Journal of Remote Sensing , 20 : 111 – 122 .
   Web of Science ®Google Scholar
• Huete , A. R . 1988 . A soil adjusted vegetation index (SAVI) . Remote Sensing of Environment , 34 : 75 – 91 .
   Google Scholar
• Jacquemoud , S and Baret , F . 1990 . PROSPECT: a model of leaf optical properties . Remote Sensing of Environment , 34 : 75 – 91 .
   Web of Science ®Google Scholar
• Jacquemoud , S , Baret , F , Andrieu , B , Danson , F. M and Jaggard , K. W . 1995 . Extraction of vegetation biophysical parameters by inversion of the Prospect+SAIL model on sugar beet canopy reflectance data: application to TM and AVIRIS sensors . Remote Sensing of Environment , 52 : 163 – 172 .
   Web of Science ®Google Scholar
• 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 . International Journal of Remote Sensing , 19 : 2639 – 2663 .
   Web of Science ®Google Scholar
• Malthus , T. J , Andrieu , B , Baret , F , Clark , J. A , Danson , F. M , Jaggard , K. J and Steven , M. D . 1993 . High-spectral resolution infrared indices for the prediction of crop cover . Remote Sensing of Environment , 46 : 204 – 212 .
   Web of Science ®Google Scholar
• Moran , M. S , Inoue , Y and Barnes , E. M . 1997 . Opportunities and limitations for image-based remote sensing in precision crop management . Remote Sensing of Environment , 61 : 319 – 346 .
   Web of Science ®Google Scholar
• Morgan , R. C. P , Quinton , J. N , Smith , R. E , Govers , G , Peosen , J. W. A , Auerswald , K , Chisci , G , Torri , D and Steyczen , M. E . 1998 . The European Soil Erosion Model (EUROSEM): a dynamic approach for predicting sediment transport from fields and small catchments . Earth Surface Processes and Landforms , 23 : 527 – 544 .
   Web of Science ®Google Scholar
• Qi , J , Chehbouni , A , Huete , A. R and Kerr , Y. H . 1994 . A modified soil adjusted vegetation index (MSAVI) . Remote Sensing of Environment , 48 : 119 – 126 .
   Web of Science ®Google Scholar
• Richardson , A. J and Wiegand , C. L . 1977 . Distinguishing vegetation from soil background information . Photogrammetric Engineering and Remote Sensing , 43 : 1541 – 1552 .
   Web of Science ®Google Scholar
• Rowland C. S Danson F. M North P. J Plummer S. E 2000 Representing forest canopy reflectance: comparison of models using large-scale field data In Adding Value to Remotely Sensed Data (CD) (Nottingham: Remote Sensing Society)
   Google Scholar
• Rumelhart D. E Hinton G. E Williams R. J 1986 Learning internal representations by error propagation In Parallel Distributed Processing: Explorations in the Microstructure of Cognition, edited by D. E. Rumelhart and J. McClelland (Cambridge, MA: MIT Press), pp. 318–362
   Google Scholar
• Smith , J. A . 1993 . LAI inversion using a back-propagation neural network trained with a multiple scattering model . IEEE Transactions on Geoscience and Remote Sensing , 31 : 1102 – 1106 .
   Web of Science ®Google Scholar
• Verhoef , W . 1984 . Light scattering by leaf layers with application to canopy reflectance modeling: the SAIL model . Remote Sensing of Environment , 6 : 125 – 184 .
   Web of Science ®Google Scholar
• Verstraete , M. M and Pinty , B . 1996 . Designing optimal spectral indices for remote sensing applications . IEEE Transactions on Geoscience and Remote Sensing , 34 : 1254 – 1265 .
   Web of Science ®Google Scholar
• Weiss , M , Baret , F , Myneni , R , Pragnère , A and Knyazikhin , Y . 2000 . Investigation of a model inversion technique for the estimation of crop characteristics from spectral and directional reflectance data . Agronomie , 20 : 3 – 22 .
   Google Scholar