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A method for improving hotspot directional signatures in BRDF models used for...

Jiao, Z., C. B. Schaaf, Y. Dong, M. Román, M. J. Hill, J. M. Chen, Z. Wang, H. Zhang, E. Saenz, R. Poudyal, C. Gatebe, F. Bréon, X. Li, and A. Strahler (2016), A method for improving hotspot directional signatures in BRDF models used for MODIS, Remote Sensing of Environment, 186, 135-151, doi:10.1016/j.rse.2016.08.007.

The semi-empirical, kernel-driven, linear RossThick-LiSparseReciprocal (RTLSR) Bidirectional Reflectance Distribution Function (BRDF) model is used to generate the routine MODIS BRDF/Albedo product due to its global applicability and the underlying physics. A challenge of this model in regard to surface reflectance anisotropy effects comes from its underestimation of the directional reflectance signatures near the Sun illumination direction; also known as the hotspot effect. In this study, a method has been developed for improving the ability of the RTLSR model to simulate the magnitude and width of the hotspot effect. The method corrects the volumetric scattering component of the RTLSR model using an exponential approximation of a physical hotspot kernel, which recreates the hotspot magnitude and width using two free parameters (C1 and C2, respectively). The approach allows one to reconstruct, with reasonable accuracy, the hotspot effect by adjusting or using the prior values of these two hotspot variables. Our results demonstrate that: (1) significant improvements in capturing hotspot effect can be made to this method by using the inverted hotspot parameters; (2) the reciprocal nature allow this method to be more adaptive for simulating the hotspot height and width with high accuracy, especially in cases where hotspot signatures are available; and (3) while the new approach is consistent with the heritage RTLSR model inversion used to estimate intrinsic narrowband and broadband albedos, it presents some differences for vegetation clumping index (CI) retrievals. With the hotspot-related model parameters determined a priori, this method offers improved performance for various ecological remote sensing applications; including the estimation of canopy structure parameters.

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Carbon Cycle & Ecosystems Program (CCEP)
Radiation Science Program (RSP)