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The latest versions of the Ross-Li model include kernels that represent isotropic reflection of the surface, describe backward reflection of soil and vegetation systems, characterize strong forward reflection of snow, and adequately consider the hotspot effect (i.e., RossThick-LiSparseReciprocalChen-Snow, RTLSRCS), theoretically able to effectively characterize BRDF/Albedo/NBAR features for various land surface types. However, a systematic evaluation of the RTLSRCS model is still lacking for various land cover types. In this paper, we conducted a thorough assessment of the RTLSRCS and RossThick-LiSparseReciprocalChen (RTLSRC) models in characterizing BRDF/Albedo/NBAR characteristics by using the global POLDER BRDF database. The primary highlights of this paper include the following: (1) Both models demonstrate high accuracy in characterizing the BRDF characteristics across 16 IGBP types. However, the accuracy of the RTLSRC model is notably reduced for land cover types with high reflectance and strong forward reflection characteristics, such as Snow and Ice (SI), Deciduous Needleleaf Forests (DNF), and Barren or Sparsely Vegetated (BSV). In contrast, the RTLSRCS model shows a significant improvement in accuracy for these land cover types. (2) These two models exhibit highly consistent albedo inversion across various land cover types (R2 > 0.9), particularly in black-sky and blue-sky albedo, except for SI. However, significant differences in white-sky albedo inversion persist between these two models for Evergreen Needleleaf Forests (ENF), Evergreen Broadleaf Forests (EBF), Urban Areas (UA), and SI (p < 0.05). (3) The NBAR values inverted by these two models are nearly identical across the other 15 land cover types. However, the consistency of NBAR results is relatively poor for SI. The RTLSRC model tends to overestimate compared to the RTLSRCS model, with a noticeable bias of approximately 0.024. This study holds significant importance for understanding different versions of Ross-Li models and improving the accuracy of satellite BRDF/Albedo/NBAR products.

The Hapke model has been widely used in the field of soil remote sensing. However, the latest development of the Hapke model (i.e., Hapke-HSR model) adopted a simple hypothesis to consider the influence of the soil moisture content (SMC), which brought great difficulties to SMC parameter inversion. This paper presents a method to improve the Hapke model using the improved multilayer radiative transfer model of soil reflectance (MARMIT-2), which can effectively improve the ability of the Hapke-HSR model to characterize the variation in the SMC. Finally, we used the soil database to comprehensively verify the ability of the improved Hapke model. The results show that the improved Hapke can effectively characterize the spectral characteristics of soil and show a higher fitting accuracy (RMSE = 0.009) compared with the Hapke-HSR model (RMSE = 0.031), especially at a high SMC (≥30%). Therefore, the improved Hapke model can better understand soil physical properties and improve the inversion accuracy of soil–vegetation physical parameters, which can be used to enhance agricultural water use efficiency.