Hyperspectral remote sensing of foliar nitrogen content
Hyperspectral remote sensing of foliar nitrogen content
复制标题
DOI：
10.1073/pnas.1210196109
复制
发表时间：
2013-01-15
影响因子：
11.1
通讯作者：
Myneni, Ranga B.
中科院分区：
文献类型：
--
作者：
Knyazikhin, Yuri;Schull, Mitchell A.;Myneni, Ranga B.
在一些温带和寒带森林中，据报道植被冠层在近红外（NIR）光谱区域的双向反射率因子（BRF）与基于叶片质量的氮浓度（%N）之间存在强正相关。如果这种关系属实，那么氮通过其对地表反照率的影响，将在气候系统中具有额外的作用，并且可能为利用卫星数据监测叶片氮含量提供一种简单的方法。然而，我们报告称，先前报道的相关性是一种假象——它是冠层结构变化的结果，而非%N的变化所致。这种关系所依据的数据是在叶片含氮量低的针叶树种和含氮量高的阔叶树种比例不同的地点收集的，这些树种的冠层结构差异很大。当对BRF数据进行冠层结构效应校正后，在423 - 855 nm区间内的所有波长上，剩余反射率变化与%N呈负相关。这表明观察到的BRF与%N之间的正相关并不能传达有关%N的信息。我们发现，要从遥感数据推断叶片生化成分，例如氮含量，710 - 790 nm区间的BRF光谱为校正结构影响提供了关键信息。我们的分析还表明，叶片的表面特征会影响对其内部成分的遥感。这进一步降低了遥感冠层叶片氮含量的能力。最后，这里所呈现的分析对于叶片组织成分的遥感问题具有普遍性，因此并不是对支持遥感叶片%N的文章的特定批评。
A strong positive correlation between vegetation canopy bidirectional reflectance factor (BRF) in the near infrared (NIR) spectral region and foliar mass-based nitrogen concentration (%N) has been reported in some temperate and boreal forests. This relationship, if true, would indicate an additional role for nitrogen in the climate system via its influence on surface albedo and may offer a simple approach for monitoring foliar nitrogen using satellite data. We report, however, that the previously reported correlation is an artifact-it is a consequence of variations in canopy structure, rather than of %N. The data underlying this relationship were collected at sites with varying proportions of foliar nitrogen-poor needleleaf and nitrogen-rich broadleaf species, whose canopy structure differs considerably. When the BRF data are corrected for canopy-structure effects, the residual reflectance variations are negatively related to %N at all wavelengths in the interval 423-855 nm. This suggests that the observed positive correlation between BRF and %N conveys no information about %N. We find that to infer leaf biochemical constituents, e.g., N content, from remotely sensed data, BRF spectra in the interval 710-790 nm provide critical information for correction of structural influences. Our analysis also suggests that surface characteristics of leaves impact remote sensing of its internal constituents. This further decreases the ability to remotely sense canopy foliar nitrogen. Finally, the analysis presented here is generic to the problem of remote sensing of leaf-tissue constituents and is therefore not a specific critique of articles espousing remote sensing of foliar %N.