Estimation of citrus yield from canopy spectral features determined by airborne hyperspectral imagery

References

• Abdi , H. 2003 . “ Partial least squares (PLS) regression ” . In Encyclopedia of Social Sciences Research Methods , Edited by: Lewis-Beck , M. , Bryman , A. and Futing , T. 1 – 7 . Thousand Oaks, CA : Sage .
   Google Scholar
• Andersson , C. A. and Bro , R. 2002 . The N-way Toolbox for MATLAB . Chemometrics and Intelligent Laboratory Systems , 52 : 1 – 4 .
   Web of Science ®Google Scholar
• Annamalai , P. , Lee , W. S. and Burks , T. F. 2004 . Color vision system for estimating citrus yield in real-time , St Joseph, MI : ASAE . ASAE Paper No. 043054
   Google Scholar
• Asrar , G. , Fuchs , M. , Kanemasu , E. T. and Hatfield , J. L. 1984 . Estimating absorbed photosynthetic radiation and leaf area index from spectral reflectance in wheat . Agronomy Journal , 76 : 300 – 306 .
   Web of Science ®Google Scholar
• Bergen , K. M. and Dobson , M. C. 1999 . Integration of remotely sensed radar imagery in modeling and mapping of forest biomass and net primary production . Ecological Modeling , 122 : 257 – 274 .
   Web of Science ®Google Scholar
• Chang , K. W. , Shen , Y. and Lo , J. C. 2005 . Predicting rice yield using canopy reflectance measured at booting stage . Agronomy Journal , 97 : 872 – 878 .
   Web of Science ®Google Scholar
• Elwadie , M. E. , Pierce , F. J. and Qi , J. 2005 . Remote sensing of canopy dynamics and biophysical variables estimation of corn in Michigan . Agronomy Journal , 97 : 99 – 105 .
   Web of Science ®Google Scholar
• Esbensen , K.H . 2002 . Multivariate Data Analysis in Practice , 5th , Woodbridge, NJ : CAMO Technologies .
   Google Scholar
• Farrand , W. H. , Singer , R. B. and Merenyi , E. 1994 . Retrieval of apparent surface from AVIRIS data: a comparison of empirical line, radiative transfer and spectral mixture methods . Remote Sensing of Environment , 47 : 311 – 321 .
   Web of Science ®Google Scholar
• Gamon , J. A. , Peñuelas , J. and Field , C. B. 1992 . A narrow-waveband spectral index that tracks diurnal changes in photosynthetic efficiency . Remote Sensing of Environment , 41 : 35 – 44 .
   Web of Science ®Google Scholar
• Gitelson , A. A. , Kaufman , Y. J. , Stark , R. and Rundquist , D. C. 2002 . Novel algorithms for remote estimation of vegetation fraction . Remote Sensing of Environment , 80 : 76 – 87 .
   Web of Science ®Google Scholar
• Goel , P. K. , Prasher , S. O. , Landry , J. A. , Patel , R. M. and Viau , A. A. 2003 . Estimation of crop biophysical parameters through airborne and field hyperspectral remote sensing . Transactions of the American Society for Agricultural Engineers , 46 : 1235 – 1246 .
   Google Scholar
• Isagi , Y , Sugimura , K , Sumida , A. and Ito , H. 1997 . How does masting happen and synchronize? . Journal of Theoretical Biology , 187 : 231 – 239 .
   Web of Science ®Google Scholar
• Iwagaki, I., 1997, Citrus production in Japan: new trends in technology. http://www.agnet.org/library/article/eb440.html
   Google Scholar
• Lee , K. S. , Cohen , W. B. , Kennedy , R. E. , Maiersperger , T. K. and Gower , S. T. 2004 . Hyperspectral versus multispectral data for estimating leaf area index in four different biomes . Remote Sensing of Environment , 91 : 508 – 520 .
   Web of Science ®Google Scholar
• Ministry of Agriculture, Forestry and Fisheries of Japan, 2005, Database on comprehensive statistical information. http://www.tdb.maff.go.jp/toukei/toukei
   Google Scholar
• Noguchi , Y. , Sakai , K. , Asada , S. , Garciano , L. and Sasao , A. 2003 . Modeling of alternate bearing in Satsuma Mandarin—linear dynamic model based on ensemble set for harvested fruits number . Japanese Journal of Agricultural Engineering , 65 : 55 – 61 .
   Google Scholar
• Osborne , S. L. , Schepers , J. S. , Francis , D. D. and Schlemmer , M. R. 2002 . Use of spectral radiance to estimate in-season biomass and grain yield in nitrogen- and water-stressed corn . Crop Science , 42 : 165 – 171 .
   PubMed Web of Science ®Google Scholar
• Ramadan , Z. , Hopke , P. K. , Johnson , M. J. and Scow , K. M. 2004 . Application of PLS and back-propagation neural networks for the estimation of soil properties . Chemometrics and Intelligence Laboratory Systems , 75 : 23 – 30 .
   Web of Science ®Google Scholar
• Rodriguez , J. C. , Duchemin , B. , Hadria , R. , Watts , C. , Garatuza , J. , Chehbouni , A. , Khabba , S. , Boulet , G. , Palacios , E. and Lahrouni , A. 2004 . Wheat yield estimation using remote sensing and the STICS model in the semiarid Yaqui valley, Mexico . Agronomie , 24 : 295 – 304 .
   Google Scholar
• Sakai , K. 2001 . Nonlinear Dynamics and Chaos in agricultural systems , 59 – 77 . Amsterdam : Elsevier Science .
   Google Scholar
• Sakai , K. , Noguchi , Y. and Asada , S. 2008 . Detecting chaos in a citrus orchard: Reconstruction of nonlinear dynamics from very short ecological time series . Chaos, Solitons & Fractals , 38 : 1274 – 1282 .
   Web of Science ®Google Scholar
• Seaquist , J. W. , Olsson , L. and Ardö , J. 2003 . A remote sensing-based primary production model for grassland biomes . Ecological Modeling , 169 : 131 – 155 .
   Web of Science ®Google Scholar
• Serrano , L. , Filella , I. and Penuelas , J. 2000 . Remote sensing of biomass and yield of winter wheat under different nitrogen supplies . Crop Science , 40 : 723 – 731 .
   Web of Science ®Google Scholar
• SPECIM, 2007, AISA Eagle: Airborne Hyperspectral System VNIR. http://www.specim.fi
   Google Scholar
• Stone , M. and Brooks , R. 1990 . ‘Continuum regression: cross-validated sequentially constructed prediction embracing ordinary least squares, partial least squares, and principal components regression . Journal of the Royal Statistical Society, Series B , 52 : 237 – 269 .
   Google Scholar
• Swierenga , H. , de Weijer , A. P. , van Wijk , R. J. and Buydens , L. M.C. 1999 . Strategy for constructing robust multivariate calibration models . Chemometrics and Intelligent Laboratory Systems , 49 : 1 – 17 .
   Web of Science ®Google Scholar
• Thenkabail , P. S. , Smith , R. B. and de Pauw , E. 2001 . Hyperspectral vegetation indices and their relationships with agricultural crop characteristics . Remote Sensing of Environment , 71 : 158 – 182 .
   Web of Science ®Google Scholar
• Uno , Y. , Prasher , S. O. , Lacroix , R. , Goel , P. K. , Karimi , Y. , Viau , A. A. and Patel , R. M. 2005 . Artificial neural networks to predict corn yield from Compact Airborne Spectrographic Imager data . Computer and Electronics in Agriculture , 47 : 149 – 161 .
   Web of Science ®Google Scholar
• Whitehead , D. , Boelman , N. T. , Turnbull , M. H. , Griffin , K. L. , Tissue , D. T. , Barbour , M. M. , Hunt , J. E. , Richardson , S. J. and Peltzer , D. A. 2005 . Photosynthesis and reflectance indices for rainforest species in ecosystems undergoing progression and retrogression along a soil fertility chronosequence in New Zealand . Oecologia , 144 : 233 – 244 .
   PubMed Web of Science ®Google Scholar
• Wiegand , C. L. , Maas , S. J. , Aase , J. K. , Hatfield , J. L. , Pinter , P. J. , Jackson , R. D. , Kanemasu , E. T. and Lapitan , R. L. 1992 . Multisite analyses of spectral-biophysical data for wheat . Remote Sensing of Environment , 42 : 1 – 21 .
   Web of Science ®Google Scholar
• Wold , H. 1966 . “ Estimation of principal components and related models by iterative least squares ” . In Multivariate Analysis , Edited by: Krishnaiaah , P. R. 391 – 420 . New York : Academic Press .
   Google Scholar
• Yang , C. H. , Everitt , J. , Bradford , J. and Murden , D. 2003 . Airborne hyperspectral imagery and yield monitor data for mapping cotton yield variability . Precision Agriculture , 5 : 445 – 461 .
   Google Scholar
• Yang , C. H. , Everitt , J. and Bradford , J. 2004 . Airborne hyperspectral imagery and yield monitor data for estimating grain sorghum yield variability . Transactions of the American Society for Agricultural Engineers , 47 : 915 – 924 .
   Google Scholar
• Ye , X. , Sakai , K. , Asada , S. , Akita , T. and Sasao , A. 2005 . Airborne hyperspectral imaging for investigating the dynamics of alternate bearing in citrus . Agricultural Information Research , 14 : 261 – 272 .
   Google Scholar
• Ye , X. , Sakai , K. , Garciano , L. O. , Asada , S. and Sasao , A. 2006 . Estimation of citrus yield airborne hyperspectral images using a neural network model . Ecological Modeling , 98 : 426 – 432 .
   Web of Science ®Google Scholar
• Yoder , B. J. and Pettigrew-Crosby , R. E. 1995 . Predicting nitrogen and chlorophyll content and concentrations from reflectance spectral (400–2500 nm) at leaf and canopy scales . Remote Sensing of Environment , 53 : 199 – 211 .
   Web of Science ®Google Scholar