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Yu K.,China Agricultural University | Yu K.,University of Cologne | Yu K.,International Center for Agroinformatics and Sustainable Development | Li F.,Inner Mongolia Agricultural University | And 9 more authors.
ISPRS Journal of Photogrammetry and Remote Sensing | Year: 2013

The influence of morphophysiological variation at different growth stages on the performance of vegetation indices for estimating plant N status has been confirmed. However, the underlying mechanisms explaining how this variation impacts hyperspectral measures and canopy N status are poorly understood. In this study, four field experiments involving different N rates were conducted to optimize the selection of sensitive bands and evaluate their performance for modeling canopy N status of rice at various growth stages in 2007 and 2008. The results indicate that growth stages negatively affect hyperspectral indices in different ways in modeling leaf N concentration (LNC), plant N concentration (PNC) and plant N uptake (PNU). Published hyperspectral indices showed serious limitations in estimating LNC, PNC and PNU. The newly proposed best 2-band indices significantly improved the accuracy for modeling PNU (R2=0.75-0.85) by using the lambda by lambda band-optimized algorithm. However, the newly proposed 2-band indices still have limitations in modeling LNC and PNC because the use of only 2-band indices is not fully adequate to provide the maximum N-related information. The optimum multiple narrow band reflectance (OMNBR) models significantly increase the accuracy for estimating the LNC (R2=0.67-0.71) and PNC (R2=0.57-0.78) with six bands. Results suggest the combinations of center of red-edge (735nm) with longer red-edge bands (730-760nm) are very efficient for estimating PNC after heading, whereas the combinations of blue with green bands are more efficient for modeling PNC across all stages. The center of red-edge (730-735nm) paired with early NIR bands (775-808nm) are predominant in estimating PNU before heading, whereas the longer red-edge (750nm) paired with the center of " NIR shoulder" (840-850nm) are dominant in estimating PNU after heading and across all stages. The OMNBR models have the advantage of modeling canopy N status for the entire growth period. However, the best 2-band indices are much easier to use. Alternatively, it is also possible to use the best 2-band indices to monitor PNU before heading and PNC after heading. This study systematically explains the influences of N dilution effect on hyperspectral band combinations in relating to the different N variables and further recommends the best band combinations which may provide an insight for developing new hyperspectral vegetation indices. © 2013. International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS) Published by Elsevier B.V.

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