Efficient hyper-parameter determination for regularised linear BRDF parameter retrieval

Zobitz, J. M., Quaife, T. ORCID: https://orcid.org/0000-0001-6896-4613 and Nichols, N. K. ORCID: https://orcid.org/0000-0003-1133-5220 (2020) Efficient hyper-parameter determination for regularised linear BRDF parameter retrieval. International Journal of Remote Sensing, 41 (4). pp. 1437-1457. ISSN 1366-5901 doi: 10.1080/01431161.2019.1667552

Abstract/Summary

Linear kernel driven models of the surface Bidirectional Reflectance Distribution Function (BRDF) are valuable tools for exploiting Earth observation data acquired at different sun–sensor geometries. Here we present a method that efficiently determines linear BRDF model weights using Tikhonov smoothing where the smoothing parameter λ is determined via a Generalized Singular Value Decomposition with the root mean square error prescribed depending on the MODIS band. We applied this method to twenty-six different deciduous broadleaf sites across an entire year using the MODIS Terra and Aqua reflectance data products. Kernel weights and white sky albedo derived from this GSVD method were generally consistent with those provided by the MCD43 data products. The GSVD derived results had less sample variability compared to the MCD43 data products, attributable to the assumed smoothness between kernel weights in the Tikhonov smoothing method. The GSVD technique consistently outperforms MCD43 in the reconstruction of observed MODIS reflectance data, of which retrievals from this method will do a better job of estimating albedo and normalizing data to specified geometries.

Item Type	Article
URI	https://reading-clone.eprints-hosting.org/id/eprint/86107
Identification Number/DOI	10.1080/01431161.2019.1667552
Refereed	Yes
Divisions	Science > School of Mathematical, Physical and Computational Sciences > National Centre for Earth Observation (NCEO) Science > School of Mathematical, Physical and Computational Sciences > Department of Mathematics and Statistics Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
Publisher	Taylor & Francis
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