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G. Shearer, J. Legg (1975)
Variations in the Natural Abundance of 15N of Wheat Plants in Relation to Fertilizer Nitrogen Applications1Soil Science Society of America Journal, 39
(1990)
SAS/STAT User’s guide, version 6 (4th ed.). Cary, NC, USA: SAS Institute Inc
L.F Johnson, D.E Roczen, S. Youkhana, R. Nemani, D. Bosch (2003)
Mapping vineyard leaf area with multispectral satellite imageryComputers and Electronics in Agriculture, 38
A. Hall, J. Louis, D. Lamb (2008)
Low resolution remotely sensed images of winegrape vineyards map spatial variability in planimetric canopy area instead of leaf area indexAustralian Journal of Grape and Wine Research, 14
(2006)
Configurable multi-spectral active sensor for highspeed plant canopy assessment
S. Stamatiadis, C. Christofides, E. Tsadila, D. Taskos, C. Tsadilas, J. Schepers (2007)
Relationship of Leaf Stable Isotopes (δ13C and δ15N) to Biomass Production in Two Fertilized Merlot VineyardsAmerican Journal of Enology and Viticulture
A. Gitelson, M. Merzlyak (1996)
Signature Analysis of Leaf Reflectance Spectra: Algorithm Development for Remote Sensing of ChlorophyllJournal of Plant Physiology, 148
Precision Agric
DW Lamb (2004)
Remote sensing for agriculture and the environment
W. Bausch, J. Delgado (2003)
Ground-based sensing of plant nitrogen status in irrigated corn to improve nitrogen management.
A. Gitelson, S. Verma, A. Viña, D. Rundquist, G. Keydan, B. Leavitt, T. Arkebauer, G. Burba, A. Suyker (2003)
Novel technique for remote estimation of CO2 flux in maizeGeophysical Research Letters, 30
S. Dobrowski, S. Ustin, J. Wolpert (2003)
Grapevine dormant pruning weight prediction using remotely sensed dataAustralian Journal of Grape and Wine Research, 9
L. Johnson (2003)
Temporal stability of an NDVI-LAI relationship in a Napa Valley vineyardAustralian Journal of Grape and Wine Research, 9
J. Schepers, D. Francis, M. Thompson (1989)
Simultaneous determination of total C, total N, and 15N on soil and plant materialCommunications in Soil Science and Plant Analysis, 20
KH Holland, JS Schepers, JF Shanahan (2006)
Proceedings of the 8th international conference on precision agriculture (CD)
J. Ehleringer, A. Hall, G. Farquhar (1993)
Stable isotopes and plant carbon-water relations.
G. Farquhar, J. Ehleringer, K. Hubick (1989)
Carbon Isotope Discrimination and Photosynthesis, 40
A. Gitelson, A. Viña, T. Arkebauer, D. Rundquist, G. Keydan, B. Leavitt (2003)
Remote estimation of leaf area index and green leaf biomass in maize canopiesGeophysical Research Letters, 30
Anatoly Gitelsona, Yoram Kaufmanb, Robert Starkc, Don Rundquista (2002)
Novel algorithms for remote estimation of vegetation fractionRemote Sensing of Environment, 80
J. Bort, J. Araus, H. Hazzam, S. Grando, S. Ceccarelli (1998)
Relationships between early vigour, grain yield, leaf structure and stable isotope composition in field grown barleyPlant Physiology and Biochemistry, 36
(1990)
SAS/STAT User's guide, version 6
D. Lamb (2004)
Remote sensing technologies for the Grape and Wine Industry
S. Stamatiadis, D. Taskos, C. Tsadilas, C. Christofides, E. Tsadila, J. Schepers (2006)
Relation of Ground-Sensor Canopy Reflectance to Biomass Production and Grape Color in Two Merlot VineyardsAmerican Journal of Enology and Viticulture
J. Schepers, T. Blackmer, W. Wilhelm, M. Resende (1996)
Transmittance and reflectance measurements of corn leaves from plants with different nitrogen and water supplyJournal of Plant Physiology, 148
M. O'Leary (1993)
Biochemical Basis of Carbon Isotope Fractionation
Recent advances in optical designs and electronic circuits have allowed the transition from passive to active proximal sensors. Instead of relying on the reflectance of natural sunlight, the active sensors measure the reflectance of modulated light from the crop and so they can operate under all lighting conditions. This study compared the potential of active and passive canopy sensors for predicting biomass production in 25–32 randomly selected positions of a Merlot vineyard. Both sensors provided estimates of the normalized difference vegetation index (NDVI) from a nadir view of the canopy at veraison that were good predictors of pruning weight. Although the red NDVI of the passive sensors explained more of the variation in biomass (R 2 = 0.82), its relationship to pruning weight was nonlinear and was best described by a quadratic regression (NDVI = 0.55 + 0.50 wt−0.21 wt2). The theoretically greater linearity of the amber NDVI-biomass relationship could not be verified under conditions of high biomass. The linear correlation to stable isotope content in leaves (13C and 15N) provided evidence that canopy reflectance detected plant stresses as a result of water shortage and limited fertilizer N uptake. Thus, the canopy reflectance data provided by these mobile sensors can be used to improve site-specific management practices of vineyards.
Precision Agriculture – Springer Journals
Published: Aug 23, 2009
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