Evaluation of vegetation indices and imaging spectroscopy to estimate foliar nitrogen across disparate biomes
Evaluation of vegetation indices and imaging spectroscopy to estimate foliar nitrogen across disparate biomes
复制标题
DOI：
10.1002/ecs2.3992
复制
发表时间：
2022-03
期刊：
影响因子：
2.7
通讯作者：
M. Farella;M. Barnes;D. Breshears;Jessica J. Mitchell;W. V. van Leeuwen;R. Gallery
中科院分区：
文献类型：
--
作者：
M. Farella;M. Barnes;D. Breshears;Jessica J. Mitchell;W. V. van Leeuwen;R. Gallery
植物叶片组织中的氮含量调节着植物的光合作用能力，并对全球生物地球化学循环产生重要影响。尽管它很重要，但目前还不存在一种可靠、及时且经济有效的方法来估计全球代表的陆地系统中叶片氮浓度的变化。尽管遥感数据的进步使景观尺度的叶N预测成为可能，但需要提高精度才能有效地捕捉生态系统中叶N的变化。航空遥感图像与霓虹灯提供的地面采样叶化学数据(n=1692)相结合进行分析，以预测美国各地覆盖各种植物群落和气候类型的叶N。我们从新的双波段组合中开发出了比现有指数更准确地预测叶片N的指数(所有地点的≈改善了8%，干旱地点的改善了45%)。与两波段指数相比，我们利用全光谱反射信息和偏最小二乘回归模型(R2=0.638；均方根误差=0.440)提高了叶片N的预测精度，减少了偏差。有效波长包括红边(720-765 nm)、近红外(1125 nm)反射率和2050年和2095 nm短波红外(SWIR)反射率，这些是指示叶片性状的区域，如生长类型(例如，近红外的叶面积指数)和光合作用参数(例如，叶绿素和Rubisco分别具有红光和短波红外反射率)。随着计算能力的快速增长，几个即将或最近启动的高光谱任务，以及世界各地大型环境研究观测站的发展，我们有一个令人兴奋的机会来估计覆盖比以往任何时候都更多样化的生物群的更大空间区域的叶N。我们预计，这些预测将被证明是非常有价值的，有助于限制全球范围内陆地生态系统的生物地球化学模型的不确定性。
The nitrogen content in plant foliar tissues (foliar N) regulates photosynthetic capacity and has a major impact on global biogeochemical cycles. Despite its importance, a robust, time, and cost‐effective methodology to estimate variation in foliar N concentration across globally represented terrestrial systems does not exist. Although advances in remote sensing data have enabled landscape‐scale foliar N predictions, improved accuracy is needed to effectively capture variation in foliar N across ecosystems. Airborne remote sensing imagery was analyzed in conjunction with ground‐sampled foliar chemistry data (n= 692), provided by the NEON, to predict foliar N at sites across the United States covering a variety of plant communities and climate types. We developed indices from novel two‐band combinations that predicted foliar N more accurately than existing indices (≈8% improvement across all sites and a 45% improvement in arid sites). Compared with two‐band indices, we increased accuracy and decreased bias of foliar N predictions by using full‐spectrum reflectance information and partial least squares regression (PLSR) models (R2= 0.638; root mean square error = 0.440). Significant wavelengths included red edge (720–765 nm), near infrared (NIR) reflectance at 1125 nm, and shortwave infrared (SWIR) reflectance at 2050 and 2095 nm, which are regions indicative of foliar traits such as growth type (e.g., leaf area index with NIR) and photosynthetic parameters (e.g., chlorophyll and Rubisco with red and SWIR reflectance, respectively). With the confluence of rapid increases in computing power, several forthcoming or recently launched hyperspectral missions, and the development of large‐scale environmental research observatories worldwide, we have an exciting opportunity to estimate foliar N across larger spatial areas covering more diverse biomes than ever before. We anticipate that these predictions will prove to be invaluable in helping to constrain biogeochemical model uncertainties across a global range of terrestrial ecosystems.