利用遥感和气候数据绘制全球叶片特征图的方法
A Methodology to Derive Global Maps of Leaf Traits Using Remote Sensing and Climate Data
摘要：
本文介绍了一个模块化的处理链，以获得全球高分辨率地图的叶片性状。特别是，我们目前的全球地图在500米分辨率的比叶面积，叶干物质含量，叶氮和磷含量每干质量，叶氮/磷比。处理链利用机器学习技术以及光学遥感数据（中分辨率成像光谱仪/陆地卫星）和气候数据填补空白，并扩大现场测量的叶片特征。该链首先使用带有代理的随机森林回归来填补数据库中的空白（缺失条目的$> 45 \% $），并最大化特质数据集的全局代表性。随着估计的全球地图的叶性状，我们提供了相关的不确定性估计来自回归模型。过程链是模块化的，可以很容易地适应新的特征，数据流（特征数据库和遥感数据）和方法。应用的机器学习技术允许将信息增益归因于数据输入，从而提供了在植物和生态系统尺度上理解性状-环境关系的机会。
英文摘要：
This paper introduces a modular processing chain to derive global high-resolution maps of leaf traits. In particular, we present global maps at 500 m resolution of specific leaf area, leaf dry matter content, leaf nitrogen and phosphorus content per dry mass, and leaf nitrogen/phosphorus ratio. The processing chain exploits machine learning techniques along with optical remote sensing data (MODIS/Landsat) and climate data for gap filling and up-scaling of in-situ measured leaf traits. The chain first uses random forests regression with surrogates to fill gaps in the database ($> 45 \% $ of missing entries) and maximize the global representativeness of the trait dataset. Along with the estimated global maps of leaf traits, we provide associated uncertainty estimates derived from the regression models. The process chain is modular, and can easily accommodate new traits, data streams (traits databases and remote sensing data), and methods. The machine learning techniques applied allow attribution of information gain to data input and thus provide the opportunity to understand trait-environment relationships at the plant and ecosystem scales.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
