Improving kernel-driven BRDF model for capturing vegetation canopy reflectance with large leaf inclinations

Abstract

Semiempirical, kernel-driven linear bidirectional reflectance distribution function (BRDF) models are widely used to characterize vegetation reflectance anisotropy and provide land surface bidirectional reflectance factor (BRF) products at the regional and global scales. However, these models usually imply an assumption of spherical leaf inclination. The effects of such an ideal assumption on simulating surface BRF remain inadequately quantified. In this article, we first evaluated the effects of leaf inclination on the most commonly used kernel-driven RossThick-LiSparse-Reciprocal (RTLSR) model by using the reflectance benchmark simulated by the mature PROSAIL (PROSAIL+SAIL) radiative transfer model. Subsequently, we improved the RTLSR model into a four-parameter version (RTLSRV4p) with a new volumetric scattering kernel derived from the assumption of vertical leaf inclination. Finally, the proposed RTLSRV4p model was validated by PROSAIL canopy BRF simulations, in situ canopy BRF measurements, and wide-angle infrared dual-mode line/area Aarray scanner (WIDAS) airborne observations. Validation results demonstrate that RTLSRV4p improves vegetation reflectance characterization for large leaf inclinations compared to the original RTLSR model, especially for the near-infrared (NIR) spectral domain. When validated against the simulated canopy BRFs, the mean root-mean-square error (RMSE), mean absolute percentage error (MAPE), bias, and coefficient of determination (R2) were improved from 0.0810, 31.63%, 0.0651, 0.6578 to 0.0453, 13.38%, 0.0326, 0.8734. Using the in situ BRF measurement, the fitted RMSE, MAPE, bias, and R2 were improved from 0.0917, 14.31%, 0.0728, and 0.5776 to 0.0226, 3.35%, 0.0166, and 0.9744. These validation metrics were improved from 0.0423, 10.85%, 0.0347, and 0.6598 to 0.0258, 5.85%, 0.0181, and 0.8732 when compared against the WIDAS observations. The RTLSRV4p model also shows good performance in calculating the reflectance of vegetation. These extensive validations suggest that RTLSRV4p is promising for capturing vegetation reflectance under large leaf inclinations.