Remotely detecting canopy nitrogen concentration and uptake of paddy rice in the Northeast China Plain
Remotely detecting canopy nitrogen concentration and uptake of paddy rice in the Northeast China Plain
复制标题
东北平原水稻冠层氮素浓度及吸收量遥测
DOI：
10.1016/j.isprsjprs.2013.01.008
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发表时间：
2013
影响因子：
12.7
通讯作者：
Bareth
中科院分区：
文献类型：
--
作者：
Bareth
在不同的生长阶段的形态生理变化的影响植被指数的性能估计植物N状态已被证实。然而，解释这种变化如何影响高光谱测量和冠层氮状况的基本机制知之甚少。在这项研究中，涉及不同的氮水平进行了4个田间试验，以优化敏感波段的选择，并评估其性能模拟水稻冠层氮素状况在不同的生长阶段在2007年和2008年。结果表明，生长阶段以不同方式对叶片氮浓度（LNC）、植株氮浓度（PNC）和植株氮吸收（PNU）的高光谱指标产生负面影响。已发表的高光谱指数在估计LNC，PNC和PNU方面存在严重的局限性。新提出的最佳2波段指数通过使用λ波段优化算法显著提高了建模PNU的准确性（R2=0.75-0.85）。然而，新提出的2-波段指数在建模LNC和PNC时仍然存在局限性，因为仅使用2-波段指数不足以提供最大N相关信息。最佳多窄带反射率（OMNBR）模型显著提高了6波段LNC（R2=0.67-0.71）和PNC（R2=0.57-0.78）的估算精度。结果表明，红边中心（735 nm）与较长的红边带（730- 760 nm）的组合对于估计抽穗后的PNC是非常有效的，而蓝色与绿色带的组合对于建模所有阶段的PNC更有效。红边中心（730- 735 nm）与近红外早期波段（775- 808 nm）配对在抽穗前的PNU估算中占主导地位，而较长的红边（750 nm）与“近红外肩”中心（840- 850 nm）配对在抽穗后和整个生育期的PNU估算中占主导地位。OMNBR模型的优势在于能够模拟整个生长期的冠层氮素状况。然而，最好的双波段索引更容易使用。或者，也可以使用最佳2波段索引来监测航向前的PNU和航向后的PNC。本研究系统地解释了N稀释效应对不同N变量高光谱波段组合的影响，并进一步推荐了最佳波段组合，为开发新的高光谱植被指数提供了参考。
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.