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

Authors :
Hanliang Li
Kai Yan
Si Gao
Wanjuan Song
Xihan Mu
Source :
Forests, Vol 13, Iss 3, p 435 (2022)
Publication Year :
2022
Publisher :
MDPI AG, 2022.

Abstract

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.

Details

Language :
English
ISSN :
19994907
Volume :
13
Issue :
3
Database :
Directory of Open Access Journals
Journal :
Forests
Publication Type :
Academic Journal
Accession number :
edsdoj.739c55708ea3403194fc0c5b28923b1a
Document Type :
article
Full Text :
https://doi.org/10.3390/f13030435