从成像光谱数据定量植被生物物理变量：反演方法综述
Quantifying vegetation biophysical variables from imaging spectroscopy data: a review on retrieval methods
摘要：
配备成像分光辐射计的地球观测卫星飞行任务不久将提供前所未有的分光数据流。这一数据流将为量化生物化学和结构植被特性的多样性提供大量机会。这种大数据流的处理要求需要可靠的检索技术，使生物物理变量的时空明确量化。为了迎接地球观测新时代的到来，本文综述了近年来在实验成像光谱学研究中应用的植被生物物理参数反演方法。已确定的反演方法分为：（1）参数回归，包括植被指数、形状指数和光谱变换;（2）非参数回归，包括线性和非线性机器学习回归算法;（3）基于物理的，包括使用数值优化和查找表方法反演辐射传输模型（RTM）;以及（4）混合回归方法，其将RTM模拟与机器学习回归方法相结合。对于每一个类别，广泛应用的方法与应用程序映射植被特性的概述。鉴于处理成像光谱数据，一个关键方面涉及处理光谱多重共线性的挑战。提供鲁棒估计、检索不确定性和可接受的检索处理速度的能力是考虑到操作处理的其他重要方面。对新一代的光谱为基础的处理链的业务生产的生物物理变量的建议。
英文摘要：
An unprecedented spectroscopic data stream will soon become available with forthcoming Earth-observing satellite missions equipped with imaging spectroradiometers. This data stream will open up a vast array of opportunities to quantify a diversity of biochemical and structural vegetation properties. The processing requirements for such large data streams require reliable retrieval techniques enabling the spatiotemporally explicit quantification of biophysical variables. With the aim of preparing for this new era of Earth observation, this review summarizes the state-of-the-art retrieval methods that have been applied in experimental imaging spectroscopy studies inferring all kinds of vegetation biophysical variables. Identified retrieval methods are categorized into: (1) parametric regression, including vegetation indices, shape indices, and spectral transformations; (2) non-parametric regression, including linear and non-linear machine learning regression algorithms; (3) physically-based, including inversion of radiative transfer models (RTMs) using numerical optimization and look-up-table approaches; and (4) hybrid regression methods, which combine RTM simulations with machine learning regression methods. For each of these categories, an overview of widely applied methods with application to mapping vegetation properties is given. In view of processing imaging spectroscopy data, a critical aspect involves the challenge of dealing with spectral multicollinearity. The ability to provide robust estimates, retrieval uncertainties, and acceptable retrieval processing speed are other important aspects in view of operational processing. Recommendations towards new-generation spectroscopy-based processing chains for the operational production of biophysical variables are given.
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。
