Semi-empirical kernel-driven models have been widely used to characterize anisotropic reflectance due to their simple form and physically meaningful approximation. Recently, several kernel-driven models have been coupled with topographic effects to improve the fitting of bidirectional reflectance over rugged terrains. However, extensive evaluations of the various models’ performances are required before their subsequent application in remote sensing. Three typical kernel-driven BRDF models over snow-free rugged terrains such as the RTLSR, TCKD, and the KDST-adjusted TCKD (KDST-TCKD) were investigated in this paper using simulated and observed BRFs. Against simulated data, the fitting error (NIR/Red RMSE) of the RTLSR gradually increases from 0.0358/0.0342 to 0.0471/0.0516 with mean slopes (α) increases from 9.13° to 33.40°. However, the TCKD and KDST-TCKD models perform an overall better fitting accuracy: the fitting errors of TCKD gradually decreased from 0.0366/0.0337 to 0.0252/0.0292, and the best fit from the KDST-TCDK model with NIR/Red RMSE decreased from 0.0192/0.0269 to 0.0169/0.0180. When compared to the sandbox data (α from 8.4° to 30.36°), the NIR/Red RMSE of the RTLSR model ranges from 0.0147/0.0085 to 0.0346/0.0165, for the TCKD model from 0.0144/0.0086 to 0.0298/0.0154, and for the KDST-TCKD model from 0.0137/0.0082 to 0.0234/0.0149. Using MODIS data, the TCKD and KDST-TCKD models show more significant improvements compared to the RTLSR model in rugged terrains. Their RMSE differences are within 0.003 over a relatively flat terrain (α < 10°). When α is large (20°–30° and >30°), the RMSE of the TCKD model has a decrease of around 0.01 compared to that of the RTLSR; for KDST-TCKD, it is approximately 0.02, and can even reach 0.0334 in the savannas. Therefore, the TCKD and KDST-TCKD models have an overall better performance than the RTLSR model in rugged terrains, especially in the case of large mean slopes. Among them, the KDST-TCKD model performs the best due to its consideration of topographic effects, geotropic growth, and component spectra.

Air pollution is a serious problem in China, and the government has taken a series of steps to solve it. However, it is still u2nclear how the situation has improved after years of atmospheric pollution control. Shandong Province, which has the second largest population and the highest coal consumption in China, was chosen to analyze the spatiotemporal air quality trends. We obtained daily air quality index (AQI) values from 91 stations in the province from 1 January 2014, to 31 December 2019, based on an adaptive data analysis method, empirical mode decomposition (EMD). The distribution of AQI in Shandong Province was heterogeneous at both spatial and temporal scales. All the stations could be divided into four clusters whose AQI trends decreased from 75 to 53, 95 to 68, 128 to 82, and 148 to 82, respectively. The overall trend of pollution became more serious from east to west in the province. The AQI is the largest in winter, followed by spring and autumn, and the smallest index occurs in summer. There are four types of annual trends of the AQI of each city. The overall downward trend indicates that the air quality of each city was improving annually.

Characterizing the anisotropic features at the top of atmosphere (TOA) is crucial for vegetation monitoring and retrieval of biophysical parameters. The core challenge lies in modeling the mutual interactions between land surface and atmosphere, particularly in the context of rugged terrains and cloudy conditions. The GOSAILTA is proposed to extend the top-of-canopy (TOC) anisotropic reflectance geometric optical and mutual shadowing and scattering-from-arbitrarily-inclined-leaves model coupled with topography (GOSAILT) model to TOA reflectance/radiance by integrating Santa Barbara DISORT atmospheric radiative transfer (SBDART) model. The interactions between atmosphere and land surface are characterized for the effects of the sloped surface and its surrounding terrains under both clear and cloudy conditions. The model was validated against discrete anisotropic radiative transfer (DART) simulations, airborne observations from wide-angle infrared dual-model line/area array scanner (WIDAS), and satellite observations from HJ-1A/B constellation charge-coupled device (CCD). Results demonstrate high overall accuracy in the red band (coefficient of determination ( $R^{2}$ ) =0.993; root-mean-square error (RMSE) =0.008; and mean absolute percentage error (MAPE) =5.481%) and near-infrared (NIR) band ( $R^{2}=0.933$ , RMSE =0.025; and MAPE =6.227%) compared to DART simulations. The simulations show strong agreement with WIDAS and HJ, achieving an $R^{2}$ of 0.9. However, the accuracy is slightly lower for top-of-cloud reflectance, with an $R^{2}$ and MAPE of 0.311 and 14.972%, respectively, primarily due to limitations in cloud parameterization.

The diurnal variation of land surface albedo (DVLSA) is crucial for understanding energy budgets and climate change. As topography complicates the radiative transfer processes, the estimation of DVLSA over rugged terrain becomes challenging. In this study, the topography-coupled DVLSA model (DVLSA_T) is developed to estimate DVLSA over rugged terrain. DVLSA_T represents DVLSA as a multiplication between the basic albedo and a diurnal variation factor. The basic albedo is the albedo at local noon with topographic effects removed, while the diurnal variation factor extends the albedo from local noon to different times of the day, accounting for topographic effects. Specifically, the diurnal variation factor of black-sky albedo (BSA) changes with the illumination geometry, integrating the topographic effects and U-shaped pattern of DVLSA. In contrast, the diurnal variation factor of white-sky albedo (WSA) is independent of illumination geometry and is solely influenced by topography. DVLSA_T shows good performance when compared with the 3D radiative transfer simulations by the large-scale remote sensing data and image simulation framework (LESS) (BSA: coefficient of determination (R ² ) = 0.977; root-mean-square (RMSE) = 0.013; WSA: R ² = 0.982; RMSE = 0.012) and sandbox measurements (blue-sky albedo: R ² = 0.904; RMSE = 0.012). DVLSA_T also has a good agreement with in situ measurements, with an RMSE of 0.024 and an R ² of 0.738. Our results demonstrate that DVLSA_T can effectively characterize DVLSA over rugged terrain.