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首页> 外文期刊>Surveys in Geophysics: An International Review Journal of Geophysics and Planetary Sciences >Quantifying Vegetation Biophysical Variables from Imaging Spectroscopy Data: A Review on Retrieval Methods
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Quantifying Vegetation Biophysical Variables from Imaging Spectroscopy Data: A Review on Retrieval Methods

机译：从成像光谱数据量化植被生物物理变量：检索方法综述

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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) nonparametric regression, including linear and nonlinear 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 operational production of biophysical variables are given.
机译：前所未有的光谱数据流将很快达到配备有成像分光剂仪的地球观察卫星任务。该数据流将开辟大量机会，以量化生化和结构植被属性的多样性。这种大数据流的处理要求需要可靠的检索技术，从而实现生物物理变量的时空明确量化。旨在为这一新的地球观测的新时代做好准备，本综述总结了已应用于实验成像光谱研究的最先进的检索方法，推断各种植被生物物理变量。确定的检索方法被分类为：（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）使用数值优化和查找表方法，物理基于，包括辐射转移模型（RTMS）的反转; （4）混合回归方法，将RTM模拟与机器学习回归方法相结合。对于这些类别中的每一个，给出了具有应用于绘制植被特性的广泛应用方法的概述。鉴于处理成像光谱数据，关键方面涉及处理光谱多型性的挑战。考虑到操作处理，提供稳健估计，检索不确定性和可接受的检索处理速度的能力是其他重要方面。给出了对新一代光谱的加工链的建议，用于进行生物物理变量的操作生产。

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