Review of the land surface BRDF inversion methods based on remotely sensed satellite data

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. © 2023 Science Press. All rights reserved.