陆表二向反射（BRDF）反演方法研究进展

韩源; 闻建光; 肖青; 鲍云飞; 陈曦; 刘强; 贺敏

doi:10.11834/jrs.20231188

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陆表二向反射（BRDF）反演方法研究进展
Review of the land surface BRDF inversion methods based on remotely sensed satellite data
2023年27卷第9期页码：2024-2040
收稿：2021-04-06，

纸质出版：2023-09-07
DOI： 10.11834/jrs.20231188
稿件说明：

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韩源，闻建光，肖青，鲍云飞，陈曦，刘强，贺敏.2023.陆表二向反射（BRDF）反演方法研究进展.遥感学报，27（9）： 2024-2040 DOI： 10.11834/jrs.20231188.

Han Y，Wen J G，Xiao Q，Bao Y F，Chen X，Liu Q and He M. 2023. Review of the land surface BRDF inversion methods based on remotely sensed satellite data. National Remote Sensing Bulletin， 27（9）：2024-2040 DOI： 10.11834/jrs.20231188.

摘要

陆表二向反射BRDF（Bidirectional Reflectance Distribution Function）定量刻画了地表目标在不同太阳—目标—传感器方向上的反射能力，是光学定量遥感研究的基础参量。BRDF在地表三维结构表征上起着重要作用，对地表能量平衡研究有重要意义。自20世纪80年代来的发展，BRDF在定义、反演、观测等方面的研究都取得了显著的进展。随着多角度卫星或拟多角度卫星的发射升空，其相应的BRDF产品得到了业务化的生产和发布，被广泛应用到了遥感多个领域。本文从BRDF反演的基本原理出发，分析了BRDF反演的主要问题，在此基础上重点介绍了BRDF反演方法的原理和特点，这些方法可有效缓解BRDF反演过程中的病态（ill-posed）问题，最后指出了未来提高BRDF反演精度的研究方向。

Abstract

Bidirectional Reflectance Distribution Function (BRDF) is a basic variable in optical quantitative remote sensing

which describes the reflection anisotropy of surface targets with different sun-target-sensor geometry. BRDF not only plays an important role in the characterization of land surface structure but also has great relevance for the research of earth energy balance. The definition

inversion

and observation technology of BRDF have made remarkable progress over the past 40 years. Moreover

with the launch of multiangular remote sensors

its BRDF products have been generated and released

which are widely used in remote sensing community.

Based on the principle of BRDF inversion

the most common problems associated with BRDF inversion are first analyzed

including the ill-posed problem caused by insufficient observations

the noise of observation data

and the noise accompanied by the introduced prior knowledge that causes the uncertainties of the inversion.

Then

the current BRDF inversion methods used to solve the problems above are analyzed

summarized

and classified into three: fundamental inversion methods

regularization-constrained inversion methods

and information classification and amplification inversion methods. Fundamental inversion methods are suitable when the number of observations is greater than the number of variables to be retrieved

and prior knowledge is not required. They include the least square method

the least variance method

and the robust estimation method. The least square method and the robust estimation method are only used when observations are sufficient

but the least variance method can be used even when observations are insufficient. However

prior knowledge is required for regularization-constrained inversion

information classification

and information amplification inversion methods

all of which are used to address the ill-posed problem. The regularization-constrained inversion method constrains the inversion results by regularization rules. The information classification and information amplification inversion methods include multistage target decision making

Bayesian estimation

Kalman filtering

and multisensor joint inversion. Among them

the multistage target decision-making method can allocate as much information as possible to the target parameters

and the Bayesian estimation method

the Kalman filter method

and the multisensor joint inversion method address the issue of insufficient observations by expanding data sources.

The challenges of how to improve the inversion accuracy of land surface BRDF in the future were also discussed

namely

high-resolution BRDF inversion

mountainous surface BRDF inversion

and the application of artificial intelligence technology in BRDF inversion. The BRDF model suitable for low- and medium-resolution pixel scales is not suitable for high-resolution pixel scales due to the strong proximity effect and mutual occlusion effect among high-resolution pixels. With the rapid growth in high-resolution satellite data and UAV data

the development of appropriate models for high-resolution pixel-scale BRDF inversion is imminent. The second model

mountainous surface BRDF inversion

also faces challenges due to the complex terrain and a lack of remote sensing data. To solve the problem

a multisource

multiscale joint inversion method as well as the prior knowledge dataset of mountainous surface BRDF need to be created. Finally

with the accumulation of remote sensing data over the last few decades

remote sensing has entered the “Big Data Era.” Investigating how to invert surface BRDF with remote sensing based on artificial intelligence technology is worthwhile.

关键词

Keywords

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