陆表二向反射（BRDF）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。经过近四十年的发展，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。

The 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 significance for the research of earth energy balance. The definition, inversion, and observation technology of BRDF have made significant progress over the past 40 years. And with the launch of multi-angular remote sensors, its BRDF products have been generated and released, which are widely used in remote sensing community. On the basis of the principle of BRDF inversion, the most common problems associated with BRDF inversion are firstly 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 that used to solve problems mentioned above are analyzed and summarized. They are classified into three categories: fundamental inversion methods, regularization-constrained inversion methods, and information classification and amplification inversion methods. Fundamental inversion methods are basically suitable for the case where the number of observations is greater than the number to be retrieved variables, and the prior knowledge are not required. They include the least square method, the least variance method, and robust estimation method. The least square method and robust estimation method are only used when there are enough observations, but the least variance method can be used even when observations is insufficient. However, prior knowledge is required for regularization constrained inversion methods, 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 multi-stage target decision-making, Bayesian estimation, Kalman filtering, and multi-sensor joint inversion. Among them, the multi-stage 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 multi-sensor joint inversion method address the issue of insufficient observations by expanding data sources. We also discussed the challenges of how to improve the inversion accuracy of land surface BRDF in the future, they are the 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 will not be suitable for high-resolution pixel scales due to the strong proximity effect and mutual occlusion effect between 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, mountainous surface BRDF inversion also faces challenges due to the complex terrain and a lack of remote sensing data. To solve the problem, we"ll need to create a multi-source, multi-scale joint inversion method as well as the prior knowledge dataset of mountainous surface BRDF. Finally, with the accumulation of remote sensing data over the last few decades, remote sensing has entered the "Big Data Era." It is worthwhile to investigate how to inverse surface BRDF with remote sensing based on artificial intelligence technology.