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.
|Original language||English (US)|
|Number of pages||14|
|Journal||ISPRS Journal of Photogrammetry and Remote Sensing|
|State||Published - Apr 2013|
Bibliographical noteFunding Information:
The study was funded by China 973 Program (Grant No. 2009CB118606), the innovative group grant of the Natural Science Foundation of China (NSFC Grant No. 31121062) and the German Federal Ministry of Education and Research (BMBF, project No. CHN 08/051). We acknowledge the support of Jiansanjiang Branch Bureau. We thank Lei Gao, Guangming Zhao, Yinkun Yao, Shanyu Huang for their field work contribution and Prof. Tony Fuller for English language editing. The authors are very grateful for the critical comments of both anonymous reviewers, one suggested using the comprehensive multi-band-optimized method and the other one helped us to better explain the N dilution effect. We would also like to thank the editor for putting in extraordinary effort to catch grammatical errors in our manuscript.
- Heading stage
- Hyperspectral index
- Lambda by lambda band-optimized algorithm
- N dilution effect
- Nitrogen status
- Stepwise multiple linear regression