Daily mean albedo is a critical variable in surface energy budget and climate change studies. Currently, satellite-based daily mean albedo is typically estimated from the diurnal variation of albedo, derived from multi-angle reflectance observations using a Bidirectional Reflectance Distribution Function (BRDF) kernel-driven model. However, this model assumes flat terrain and neglects topographic effects. This study evaluates the estimation errors of daily mean albedo derived from the BRDF kernel-driven model over rugged terrain. Experiments were conducted for rugged terrains with different mean slopes (10°, 20°, and 30°) and aspects (north and west) at spatial scales of 500 m and 1 km, using large-scale remote sensing data and the image simulation framework (LESS) model. The results demonstrate that topography significantly influences the daily mean albedo derived from the BRDF kernel-driven model, with the largest relative error exceeding 50%. The estimation error increases as the slope of the terrain becomes steeper and is also strongly influenced by the aspect of the terrain. When the solar azimuth angle aligns with the aspect of the rugged terrain, the estimation error becomes particularly pronounced. These findings highlight the necessity of accounting for topographic effects when estimating daily mean albedo.

Quantitative evaluation of errors caused by Digital Elevation Model (DEM) geolocation bias is crucial for the multiscale validation of land surface albedo (LSA) over rugged terrain, as it provides a deeper understanding of topographic effects and helps minimizing validation uncertainties. This letter simulates the near-infrared band (NIR, 850 nm) fine scale albedo maps and coarse scale albedo by (large-scale remote sensing data and image simulation framework) LESS model, and shifts the DEMs along the different directions to aggregate to different coarse scales. The Mountain-Radiation-Transfer-based (MRT-based) albedo upscaling model was used to aggregate to the coarse scale. The results demonstrate that the errors distribution caused by DEM offsets is related to the terrain features. The primary factors influencing these errors are the average slope and coarse scale. Error increases with steeper slopes and decreases with larger coarse scales. Specifically, at 250 m, for terrains with a mean slope of approximately 25°, when the DEM is shifted by 4 pixels in both row and column directions, the errors can exceed 0.08, which is 4.5 times greater than those for gentle slopes (mean slope ≈ 5°). Minor DEM offsets are generally acceptable for gentle slopes and larger scales (>1 km), whereas precise DEM geolocation is essential for steeper slopes (mean slope > 15°), particularly at smaller coarse scales.

The diurnal variation of high spatial resolution albedo is crucial for understanding the energy budget over mountainous areas. Topography significantly affects the diurnal variation of albedo, making its accurate estimation challenging. In this study, we propose a novel algorithm for estimating the diurnal variation of albedo over sloping terrain using high-resolution satellite data. The diurnal variation of albedo is represented as the product of instantaneous albedo at the time of satellite overpass and a diurnal variation factor. Instantaneous albedo is derived from Landsat data and prior BRDF information from the Polarization and Directionality of the Earth’s Reflectances (POLDER) database. The diurnal variation factor is calculated using a fine-scale digital elevation model (DEM) and prior BRDF information, capturing the shape of diurnal variation. Validation against in situ measurements demonstrates the algorithm’s high accuracy ($R^{2} = 0.902$and root-mean-square error (RMSE) = 0.029). In addition, this study examines the differences in the diurnal variation patterns between horizontal/horizontal sloped albedo (HHSA) and inclined/inclined sloping surface albedo (IISA). The results reveal a notable difference between the two: diurnal variation of HHSA is more sensitive to topography, showing a J-shaped pattern, whereas that of IISA consistently follows a U-shaped pattern, better reflecting the sloping surface properties.

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 Wideangle 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 (R2) = 0.993; root-mean-square error (RMSE) = 0.008; mean absolute percentage error (MAPE) = 5.481%) and near-infrared (NIR) band (R2 = 0.933, RMSE = 0.025; MAPE = 6.227%) compared to DART simulations. The simulations show strong agreement with WIDAS and HJ, achieving an R² of 0.9. However, the accuracy is slightly lower for top-of-cloud reflectance, with an R² 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 3-D 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:${R}^{2} =0.982$; and RMSE = 0.012) and sandbox measurements (blue-sky albedo:${R}^{2} = 0.904$and 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.

In this study, a novel algorithm for high spatial resolution leaf area index (LAI) retrieval, specifically tailored for mountain forests, has been developed. As an essential climate variable, LAI has been incorporated into many ecohydrological process simulation models; however, the majority of the algorithms are developed on the assumption of flat terrain. Previous studies have proved that neglecting the influence of topography may introduce significant biases and uncertainties into LAI estimates particularly in rugged areas. As an important species in the mountain area, forests occupy a large land area worldwide; nevertheless, it is still challenging to obtain high-quality LAIs from satellite images due to their complex canopy structures. In spite of numerous attempts having been made to address such issues with topographic correction (TC) or mountain canopy reflectance models, few algorithms were actually available for LAI estimation of mountain forests. Here, we try to employ the geometric optical and mutual shadowing and scattering from the arbitrarily inclined-leaves model coupled with the topography (GOSAILT) model to retrieve forest LAI over complex terrain. GOSAILT is a combined model that incorporates the radiative transfer model (RTM) into the geometrical optical model (GOM) on the slope surface. It is capable of characterizing the bidirectional reflectance of both discrete and continuous canopies. The validations against computer-simulated LAIs reveal root-mean square errors (RMSEs) being 1.7160 and 0.6260, corresponding to terrain-ignored scenario and terrain-considered scenario, respectively. Besides, the validation against in situ LAIs demonstrated that the RMSE is 0.9262 over flat terrain and 0.6402 over sloped terrain. This evidence underscores the robust performance of the newly developed algorithm.

Land surface albedo is a crucial variable of earth energy budget and global climate change. Rugged terrain significantly impacts surface bidirectional reflectance distribution function (BRDF) and the subsequent albedo retrieval using satellite remote sensing. Existing studies of estimating surface BRDF/albedo from satellite observations are limited to neglecting topographic impacts, resulting in large uncertainty in satellite albedo product, especially for low spatial resolution satellite sensors that are primarily regulated by subpixel-scale topographic effects. To fill this knowledge gap, we proposed an extended multisensor combined BRDF inversion (EMCBI) model to characterize subpixel-scale topographic effects, and applied this model to estimate BRDF/albedo from the Himawari-8 Advanced Himawari Imager (AHI) and Terra/Aqua moderate resolution imaging spectroradiometer (MODIS) data and finally validated the satellite-derived albedo with ground measurements of two stations located in Tibet plateau. Our results show that: 1) EMCBI can generate a daily BRDF/albedo dataset with more than 90% spatial coverage and 2) EMCBI-derived albedo agrees well with the referenced albedo corrected from ground measurement, with a root-mean-square-error (RMSE) of 0.0537 and 0.0608 for black-sky albedo (BSA) and white-sky albedo (WSA), and a mean absolute percentage error (MAPE) of 21.93% and 25.13% for BSA and WSA, respectively. These results demonstrate EMCBI has great potential for mapping large-scale high temporal resolution BRDF/albedo product over rugged terrain.

A comprehensive assessment of satellite-derived albedo products is undeniably essential for better use consideration and the further refinement of the retrieval algorithm. Although satellite albedo products have been extensively validated over spatially homogeneous areas, it remains a challenge to validate them over rugged terrain. Consequently, the accuracy of satellite albedo products over rugged terrain is still unknown. This study for the first time systematically evaluated two widely used satellite albedo products (i.e., MCD43A3 V006 and GLASS V04 albedo) over mountainous areas with a Mountain Radiation Transfer (MRT) coupled multi-scale validation strategy. Fine-scale albedo was first generated with a root mean square error (RMSE) smaller than 0.0317. Then they were upscaled to the coarse pixel and as the reference data for validation. The validation results indicated that the accuracy of the two products tends to decrease with the increase of means slopes. The RMSE and relative RMSE (RMSER) of full retrieval MCD43A3 C6 black-sky albedo (BSA) and white-sky albedo (WSA) over abrupt slopes (mean slope >10°) increase to 0.0432 and 31.87% and to 0.0436 and 32.21%, respectively. The RMSE and RMSERof high quality GLASS V04 were 0.0452 and 33.71% of BSA and 0.0458 and 33.92 % of WSA respectively over abrupt slopes. Particularly, if the backup retrievals were included over the abrupt slopes, the RMSE and RMSER of MCD43A3 C6 can reach to 0.0600 and 36.92% for BSA and 0.0613 and 37.67% for WSA, and those of GLASS V04 can reach to 0.0567 and 36.28% for BSA and 0.0540 and 35.72% respectively.

A comprehensive assessment of satellite-derived albedo products is undeniably essential for better use consideration and the further refinement of the retrieval algorithm. Although satellite albedo products have been extensively validated over spatially homogeneous areas, it remains a challenge to validate them over rugged terrain. Consequently, the accuracy of satellite albedo products over rugged terrain is still unknown. This study for the first time systematically evaluated two widely used satellite albedo products (i.e., MCD43A3 C6 and Global Land Surface Satellite (GLASS) V04 albedo) over mountainous areas with a mountain radiation transfer (MRT) coupled multiscale validation strategy. Fine-scale albedo was first generated with a root-mean-square error (RMSE) smaller than 0.0317. Then, they were upscaled to the coarse pixel and as the reference data for validation. The validation results indicated that the accuracy of the two products tends to decrease with the increase of means slopes. The RMSE and relative RMSE (RMSER) of full retrieval MCD43A3 C6 black-sky albedo (BSA) and white-sky albedo (WSA) over abrupt slopes (mean slope >10°) increase to 0.0432 and 31.87% and to 0.0436 and 32.21%, respectively. The RMSE and RMSERof high-quality GLASS V04 were 0.0452 and 33.71% of BSA and 0.0458 and 33.92% of WSA, respectively, over abrupt slopes. In particular, if the backup retrievals were included over the abrupt slopes, the RMSE and RMSERof MCD43A3 C6 can reach 0.0600 and 36.92% for BSA and 0.0613 and 37.67% for WSA, respectively, and those of GLASS V04 can reach 0.0567 and 36.28% for BSA and 0.0540 and 35.72%, respectively.

The semiempirical kernel-driven model is commonly used for global surface reflectance characterization because of its simplicity and underlying physical meaning. However, the current kernel-driven reflectance models assume that the terrain is flat and homogeneous, and can induce significant errors in the surface reflectance estimation and subsequent parameter retrievals over rugged terrain. In this study, an improved topography-coupled kernel-driven (TCKD) reflectance model with the correction of diffuse skylight effects was proposed based on the diffused-equivalent slope model (dESM) and RossThick-LiTransit (RTLT) kernel-driven model. The TCKD model's accuracy and effectiveness were evaluated using surface reflectance simulated by the radiosity approach and the Moderate Resolution Imaging Spectroradiometer (MODIS) data. Against simulated data, the results show that the TCKD model can accurately capture the distortion of the reflectance shape and hemispherical distribution caused by the topographic effects. Compared to MODIS data, the TCKD model has an overall better performance than the RTLT model across different spatial scales and land cover types. When the mean slope is larger than 35° at the 500-m resolution, the TCKD model's near-infrared (NIR) root-mean-square error (RMSE) and the regression slope of the fitting line are 0.037 and 0.752, respectively, whereas those of the RTLT model are 0.049 and 0.645. Neglecting the diffuse skylight in the TCKD model can also lead to great bias in the reflectance retrievals. When the mean slope is 31°, as the ratio of diffuse skylight varies from 0 to 1, the NIR RMSE of the TCKD model decreases from 0.012 to 0.005, whereas that increases from 0.012 to around 0.02 if the diffuse skylight effects are neglected. These preliminary results demonstrate that the TCKD model is capable of improving the fitting ability of the kernel-driven model over rugged terrain and provides potentials for better retrieving and interpreting land surface parameters such as land surface albedo in mountainous areas.

The angular and spectral kernel-driven (ASK) model distinguishes soil and vegetation spectral features by the component spectra and is a promising model which combines multisensor data for inversion. However, its global application is limited by the component spectra. This article proposes parameterization of the ASK component spectra of soil and leaf from global spectra libraries as ANGERS, GOSPEL, LOPEX, and USGS. A statistical ratio (y) of various leaf to soil spectra is used to capture their spectral differences and variations, with mean (m) + u (0, ±0.5, ±1) standard deviations (σ) [i.e., y (m + uσ)]. Optimization inversion is applied to determine the ratio candidates y(m + uσ), allowing more tolerance for spectral uncertainty, which releases the semiempirical nature of the kernel-driven model. Simulation data analysis proves its feasibility and good capture of vegetation-soil spectral differences. The model's bidirectional reflectance factor (BRF) fitting error [root-mean-square error (RMSE)] of 0.0245 is slightly larger than the true component spectra of 0.0178, and albedo RMSE is 0.0116 in Black Sky Albedo and 0.0182 in White Sky Albedo. The result also shows its good robustness to the noises, where the====level up to 20% noise conducts a 0.0277 error in BRF fitting and an ignorable influence in albedo. The synergistic-retrieved albedo from multisensor satellite data consists of in situ measurements with an RMSE of 0.0171, compared to 0.0131 from true component spectra retrievals. The new parameterization sacrifices some accuracy, but it is simple and operational for global retrieval with a satisfactory precision.

Accurately estimating the spatial–temporal distribution of downward surface shortwave radiation (DSSR) is essential for terrestrial ecological modeling and climate change research. The accurate georegistration of digital elevation model (DEM) has become one of the significant bottlenecks for improving the DSSR accuracy over rugged terrain. To clearly understand and quantitatively evaluate the impact of geolocation bias on the DSSR estimation under clear sky, this letter conducts a systematical simulation research in Dayekou Basin of China based on a developed remote sensing satellite-based DSSR estimation scheme over rugged terrain. The results demonstrate that the proposed approach can accurately capture the high temporal and spatial heterogeneities of DSSR, and the DSSR estimations are sensitive to geolocation bias. When the horizontal bias is lower than half a pixel, the deviations of the direct radiation could lead to above 600 W/m2due to the illumination angle effects and shadow effects. The consequence of the bias on the diffuse and reflected radiation from adjacent terrains is little because of their relatively small values and low-spatial heterogeneities under clear sky in general except for the deep valley areas. The trends of the total radiation errors with the geolocation bias are identical in different days (scenes), and the error is related to the solar zenith angle. In addition, the more rugged the terrain, the greater the influence of geolocation bias on the radiation accuracy.

Light use efficiency model is one of the methods to retrieval regional scale Gross Primary Productivity (GPP). In this study, GPP of the Heihe River Basin in China from 2011 to 2015 were inversed by light use efficiency model. In this kind of the models, Photosynthetic Active Radiation (PAR) and the fraction of absorbed photosynthetically-active radiation (FPAR) were the key parameters. Usually, PAR and FPAR were inversed without distinguishing direct and diffuse radiation. However, several researches show that due to the stronger transmission of diffuse radiation in the canopy and avoidance of the light-saturation phenomenon of direct radiation in the canopy leaves, photosynthesis is more efficient under the action of diffuse radiation. So in this article PAR and FPAR were inversed by models which can distinguish direct and diffuse radiation. And the retrieved daily GPP results were validated by field measurements from flux sites of GPP.

Heterogeneous terrain significantly complicates signals received by airborne or satellite sensors. It has been demonstrated that both solar direct beam and diffuse skylight illumination conditions are significant factors influencing the anisotropy of reflectance over mountainous areas. Several models and methods have been developed to account for topographic effects on surface reflectance at the pixel level in remote sensing. However, subtopographic effects are generally neglected for low-spatial-resolution pixels due to the complex law of radiative transfer and the limitations of higher spatial resolution digital elevation models, which can lead to deviations in reflectance estimation. Accurately estimating the subtopographic effects on anisotropic reflectance over composite sloping terrain under different illumination conditions presents a challenge for remote sensing models and applications. In this paper, the diffused equivalent slope model (dESM) was developed, which is an anisotropic reflectance simulation model coupled with diffuse skylight over composite sloping terrain. The corresponding subtopographic impact factor was also proposed to exhibit how microslope topography affects reflectance over composite sloping terrain under different illumination conditions. Simulated reflectance data sets simulated by the radiosity method and Moderate Resolution Imaging Spectroradiometer reflectance data were used to evaluate the performance of the dESM model. The results reveal that the dESM model can accurately capture the reflectance anisotropy over composite sloping terrain under different illumination conditions, and the subtopographic impact factor can account for the effects of microslope topography, shadow, and illumination conditions.

A well understanding of topography effect on the forest reflectance is critical for biophysical parameters retrieval over rugged area. In this paper, a new hybrid bidirectional reflectance distribution function (BRDF) model coupled the geometric optical mutual shadowing (GOMS) and scattering from arbitrarily inclined leaves (SAIL) models with topography consideration (GOSAILT) for sloping forest was proposed.

In this paper, the land surface bidirectional reflectance distribution function (BRDF) and albedo on a small timescale are retrieved by the multisensor combined BRDF inversion (MCBI) model with improved accuracy. The accumulation period for this BRDF/albedo retrieval is shortened to 8 and 4 days with data from four satellite sensors, the Moderate Resolution Imaging Spectraradiometer (MODIS), Advanced Very High Resolution Radiometer (AVHRR), Visible Infrared Imaging Radiometer (VIIRS), and Medium Resolution Spectral Imager (MERSI), to obtain the dynamic features of land surfaces. All the four sensors have high revisit frequencies and dense angular sampling. The MCBI model provides an algorithm to form a virtual MODIS observation network with these four sensors, resulting in a multiband and multiangle sampling reflectance data set. It also provides a multisensor reflectance quality control index, the net information index (NII), for a robust BRDF/albedo retrieval. The performance of the MCBI is assessed by comparisons with MODIS BRDF/albedo product and the in situ measurement. The results show that the highly frequent angular sampling with four sensors allows for a full retrieval of BRDF/albedo with a shorter accumulation period of 8 and 4 days. The NII reduces the uncertainties when using different sensors' reflectance and allows for a high-quality BRDF/albedo retrieval. It reveals that the MCBI has the potential to generate a multisensor-based BRDF/albedo on a small timescale. The MCBI is a key algorithm for the BRDF/albedo product in China's multisource data synergized quantitative remote sensing production system and operationally implemented to generate a global product.

The influence of topography on land-surface bidirectional reflectance and albedo should be considered in rugged terrain. However, land-surface albedo algorithms neglect topographic effects, leading to errors in estimating the albedo in rugged terrain. This letter investigates the Angular Bin (AB) algorithm of land-surface albedo and shows that it should be improved when albedo is estimated in rugged terrain. The Terrain AB (TAB), an improved algorithm for albedo estimation with the AB algorithm and digital elevation model (DEM) data set, is presented in this letter. The accuracy and performance of the TAB algorithm was investigated by using the simulated DEM and Bidirectional Reflectance Function as well as the MODIS daily reflectance of the Heihe River Basin. The results show that the TAB algorithm has a better albedo estimation performance in rugged terrain and gives an acceptable accuracy.

Land surface albedo is critical in earth radiation budget and global climate monitoring. At present, models and algorithms for land-surface albedo retrieval from diverse satellites have been well developed. However, limited sampling angles from mono-sensor and coarse temporal resolution restrict the further promotion and application of albedo. Multi-sensor observations offer more information of land surface anisotropy and result in multi-angular, multi-spectral, high temporal resolution measurements. Modeling BRDF and albedo with multi-sensor data presents a unique opportunity to improve the temporal resolution and recover the consistency of surface albedo. This paper proposes a novel Multi-Angular and Multi-Band inversion model for BRDF and albedo retrieval with multiple sensors. The preliminary result based on MODIS and AVHRR band reflectance indicates that the model could obtain higher temporal resolution albedo with the same or even higher accuracy when compared with MCD43B3 albedo product.

The high spatial and high temporal satellite data is necessary in the operational agricultural applications of remote sensing. But till now the advantages of high spatial and high temporal resolution still can not be realized in single sensor. The PSP method (Patch Spectral Purification Method) is capable of retrieving field patch average information from high temporal but moderate spatial resolution satellite data, which meets the requirement of high spatial and high temporal resolution information in the real monitoring applications. In this paper a PSP-based methodology is proposed to retrieve the high spatial and high temporal resolution information for the growth and drought monitoring using multiple satellite data. An application demonstration was made in Xinjiang, China to monitor the cotton growth and drought with MODIS and Landsat/TM data. And the processing software-AgRsis (Agricultural Remote Sensing Inversion System) was realized to generate the daily crop parameters standard maps(e.g. NDVI, TVDI) for the crop growth and drought monitoring.

Beijing and Xianghe sites are selected as study area. MODIS/Terra C005 aerosol product of the whole 2005 year is validated by matching the MODIS product and AERONET within ±30 min of MODIS/Terra overpass times considering the aerosol movement velocity of given sites, then to analyze their applicability. The results show that MODIS C005 aerosol product overestimates the real value greatly at Beijing site, for that the reflectance in the bright surface is not really described, and the urban aerosol characteristic could not be defined exactly. MODIS C005 product is very accurate and reliable at Xinaghe site except in winter, could be used. The results show that the method of the surface reflectance determination used in the new algorithm is feasible, but is not suitable to all types of surface. Otherwise, season characteristics of two sites are analyzed in detail.

Vegetation indices are subjected to many external perturbations such as soil background variations, atmospheric conditions, geometric registration, and especially sensor viewing geometry. Subsequent use of these indices to estimate crop yield and monitor crops growth would result in substantial uncertainties. To reduce the uncertainties due to sun-view angle variations, some methods mere generated by use the reflectance or albedo generated from the BRDF models. MODIS vegetation composition algorithm uses the empirical BRDF model (developed by Walthall et al. to normalize the sun/view angles to certain angle, and then composite the VI by several day's data. In this paper, we present a new method based on prior knowledge to normalise vegetation index on pure pixels of crops, which can be recognized from MODIS image by high resolution land cover map. We simulated different BRDFs of winter wheat in different grow stages by radiative transfer models, using the plant canopy parameters obtained from prior knowledge. Then, we use this BRDF to normalize vegetation indices. The method was tested by the ground based measurements and MODIS Data. It shows our results are good consistent with the ground based measurements. We compare our methods with the algorithm of MODIS vegetation composition, it proved that the result calculated by our method is in better agreement with the surface reflectance characterizations and our method is more effective to monitor the crop growth in regional scale