Revisiting the Performance of the Kernel-Driven BRDF Model Using Filtered High-Quality POLDER Observations

The Bidirectional Reflectance Distribution Function (BRDF) is usually used to describe the reflectance anisotropy of a non-Lambertian surface and estimate surface parameters. Among the BRDF models, the kernel-driven models have been extensively used due to their simple form and powerful fitting ability, and their reliability has been validated in some studies. However, existing validation efforts used in situ measurements or limited satellite data, which may be subject to inadequate observational conditions or quality uncertainties. A recently released high-quality BRDF database from Polarization and Directionality of the Earth's Reflectances (POLDER) provides an opportunity to revisit the performance of the kernel-driven models. Therefore, in order to evaluate the fitting ability of the kernel-driven models under different observational conditions and explore their application direction in the future, we use the filtered high-quality BRDF database to evaluate the fitting ability of the kernel-driven model represented by the RossThick-LiSparseR (RTLSR) kernels in this paper. The results show that the RTLSR model performs well, which shows small fitting residuals under most observational conditions. However, the applicability of the RTLSR model performed differently across land cover types; the RTLSR model exhibited larger fitting residuals, especially over non-vegetated surfaces. Under different sun-sensor geometries, the fitting residuals show a strong positive correlation with the Solar Zenith Angle. The above two factors cause the RTLSR model to exhibit a poorer fitting ability at high latitudes. As an exploration, we designed a model combination strategy that combines the advantages of different models and achieved a better performance at high latitudes. We believe that this study provides a better understanding of the RTLSR model.

Automated summary

This paper evaluates the fitting ability of the kernel-driven model using a high-quality BRDF database and finds that the RTLSR model performs well under most observational conditions, but has poorer fitting ability over different land cover types and at high latitudes. A model combination strategy is also proposed, which achieves better performance at high latitudes.