Variations In Surface Reflectance Research Articles

Separating Surface Reflectance from Volume Reflectance in Medical Hyperspectral Imaging.

Hyperspectral imaging has shown great promise for diagnostic applications, particularly in cancer surgery. However, non-bulk tissue-related spectral variations complicate the data analysis. Common techniques, such as standard normal variate normalization, often lead to a loss of amplitude and scattering information. This study investigates a novel approach to address these spectral variations in hyperspectral images of optical phantoms and excised human breast tissue. Our method separates surface and volume reflectance, hypothesizing that spectral variability arises from significant variations in surface reflectance across pixels. An illumination setup was developed to measure samples with a hyperspectral camera from different axial positions but with identical zenith angles. This configuration, combined with a novel data analysis approach, allows for the estimation and separation of surface reflectance for each direction and volume reflectance across all directions. Validated with optical phantoms, our method achieved an 83% reduction in spectral variability. Its functionality was further demonstrated in excised human breast tissue. Our method effectively addresses variations caused by surface reflectance or glare while conserving surface reflectance information, which may enhance sample analysis and evaluation. It benefits samples with unknown refractive index spectra and can be easily adapted and applied across a wide range of fields where hyperspectral imaging is used.

Solar Tracking Control Algorithm Based on Artificial Intelligence Applied to Large-Scale Bifacial Photovoltaic Power Plants

The transition to a low-carbon economy is one of the main challenges of our time. In this context, solar energy, along with many other technologies, has been developed to optimize performance. For example, solar trackers follow the sun’s path to increase the generation capacity of photovoltaic plants. However, several factors need consideration to further optimize this process. Important variables include the distance between panels, surface reflectivity, bifacial panels, and climate variations throughout the day. Thus, this paper proposes an artificial intelligence-based algorithm for solar trackers that takes all these factors into account—mainly weather variations and the distance between solar panels. The methodology can be replicated anywhere in the world, and its effectiveness has been validated in a real solar plant with bifacial panels located in northeastern Brazil. The algorithm achieved gains of up to 7.83% on a cloudy day and obtained an average energy gain of approximately 1.2% when compared to a commercial solar tracker algorithm.

Spectral line confocal sensor signal analysis and correction: Unlocking reflectance difference sample measurements

Spectral line confocal imaging (LCI) technology enables high-resolution, rapid 3D surface morphology analysis of transparent materials and other samples, positioning it as a leading optical measurement tool. Nevertheless, where the sensor measures samples with significant variations in surface reflectance, the peak signals of the acquired images exhibit low signal-to-noise ratios or become distorted, hampering the peak extraction algorithm from accurately locating their peak positions. Consequently, it's arduous to reconstruct their precise surface topography. To enhance the sensor's dynamic measurement range and adaptability, this paper introduces an adaptive correction approach. It utilizes a regularized non-negative matrix to decompose images with various grey levels. This is followed by weighted frequency filtering and Gaussian enhancement of all the grey layers. Eventually, these layers are fused into one image with high contrast and uniformity. Using this approach, the sensor is capable of achieving precise 3D reconstruction with high-quality peak signals. Additionally, this study experimentally demonstrates the efficacy of the suggested approach by measuring the interdigital electrode and printed circuit boards (PCB). It should be noted that the technique presented in this paper is not limited to spectral line confocal instruments and is also effective for line laser and line structured light instruments.

Surface Reflectivity Variations of Global Navigation Satellite System Signals From a Mixed Ice and Water Surface

Surface Reflectivity Variations of Global Navigation Satellite System Signals From a Mixed Ice and Water Surface

A New Methodology for Estimating Surface Albedo in Heterogeneous Areas from Satellite Imagery

Precise mapping and continuous monitoring of fine-scale surface albedo are indispensable for assessing and optimizing renewable energy sources. Understanding the variations in surface reflectivity is crucial in capturing the full potential of renewable technologies, as it directly impacts the efficiency of harnessing solar energy for sustainable power generation. Satellite remote sensing stands out as the sole practical approach for estimating surface albedo at both regional and global scales. Although there are different methods to calculate albedo from satellite data, most satellite products result in low spatial resolution for large heterogeneous areas, such as urban and peri-urban areas. This paper evaluates and compares several methodologies to calculate surface albedo from Landsat 8 imagery. As a result, a new methodology for estimating surface albedo for heterogeneous areas has been proposed. The new methodology has been compared with direct and indirect albedo measurements, improving the original methodologies of Baldinelli and Silva with respect to the Arctic-Boreal Vulnerability Experiment (ABOVE) albedo images by reducing the RMSE by 85% and 52%, respectively.

Accurate measurement of high-reflective surface based on adaptive fringe projection technique

Fringe projection profilometry (FPP) is one of the most effective methods to obtain three-dimensional surface topography information about an object. However, for objects with large variations in surface reflectance, it is difficult to measure the saturated regions with conventional FPP. In this paper, a new adaptive fringe projection (AFP) method is proposed to solve this problem. Compared with existing methods, the proposed method can automatically generate low-intensity projected fringe patterns instead of requiring manual adjustment. First, we built a projection intensity model for the captured pixels and defined the surface reflection factor and environmental factor for saturated pixels. Then, a small number of uniform grayscale pattern sequences were projected to mark the saturated areas, while the surface reflection factor and the environment factor of individual pixels were calculated and a surface coefficient look-up table (SCLUT) was created. The low-intensity projected fringe patterns were automatically calculated and generated based on the captured uniform grayscale sequences of images to establish the mapping relationship between the camera image plane and the projector image plane. Finally, the optimal projection intensities of pixels in the saturated regions were calculated according to the SCLUT, and adaptive fringe patterns were generated. Experiments show that using the adaptive fringe patterns generated by the proposed method to measure objects can accurately adjust the projection intensity and substantially improve the measurement accuracy of high-reflective surface.

Temporal variation of surface reflectance and cloud fraction used to identify background aerosol retrieval information over East Asia

Temporal variation in cloud cover and surface conditions greatly affects estimation errors associated with reference surface reflectance and atmospheric composition retrievals. In this study, to determine an optimal temporal window for clear-sky composition methods that are used to identify surface reflectance for the retrieval of atmospheric properties, we analyzed temporal variation in surface reflectance and cloud fractions using long-term daily observations from the Moderate Resolution Imaing Spectroradiometer (MODIS) satellite. For the temporal variation in surface reflectance, gridded pixels with a standard deviation less than 0.025 represented 87.0%, 84.5%, 80.5%, and 77.3% of the total pixels for periods of 15, 20, 30, and 40 days, respectively. The temporal variability of surface reflectance was lowest in summer and highest in winter due to vegetation and snow cover changes over land surface in East Asia. For the temporal variation in cloud fractions, pixels with a cloud fraction <10% ranged from 91.2% (15-day) to 98.1% (40-day). Only temporal windows of 30 and 40 days satisfied the criterion of 95% cumulative distribution in the 10% cloud fraction range. Thus, and appropriate temporal window for clear-sky composition methods must be selected in consideration of the seasonal dependency of surface types and cloud cover variation. The temporal window for the clear-sky composition must be longer than 30 days considering the temporal variability of cloud cover, and shorter than 30 days considering that of surface reflectance. However, seasonal dependencies of surface reflectance and cloud fraction are also additionally considered to select the appropriated temporal window for the clear-sky composition.

Retrieval uncertainty and consistency of Suomi-NPP VIIRS Deep Blue and Dark Target aerosol products under diverse aerosol loading scenarios over South Asia

Retrieval accuracy and stability of two operational aerosol retrieval algorithms, Deep Blue (DB) and Dark Target (DT), applied on Visible Infrared Imaging Radiometer Suite (VIIRS) on-board Suomi National Polar-orbiting Partnership (S-NPP) satellite were evaluated over South Asia. The region is reported to be highly challenging to accurate estimation of satellite-based aerosol optical properties due to variations in surface reflectance, complex aerosol system and regional meteorology. Performance of both algorithms were initially evaluated by comparing their ability to retrieve aerosol signal over the complex geographical region under specific air pollution emission scenario. Thereafter, retrieval accuracy was investigated against 10 AERONET sites across South Asia, selected based on their geography and predominance aerosol types, from year 2012–2021. Geo-spatial analysis indicates DB to efficiently retrieve fine aerosol features over bright arid surfaces, and for smoke/dust dominating events whereas DT was better to identify small fire events under dark vegetated surface. Both algorithms however, indicate unsatisfactory retrieval accuracy against AERONET having 56–59% of valid retrievals with high RMSE (0.30–0.33) and bias. Overall, DB slightly underpredicted AOD with −0.02 mean bias (MB) whereas DT overpredicted AOD (MB: 0.13), with seasonality in their retrieval efficiency against AERONET. Time-series analysis indicates stability in retrieving AOD and match-up number for both algorithms. Retrieval bias of DB and DT AOD against AERONET AOD under diverse aerosol loading, aerosol size, scattering/absorbing aerosol, and surface vegetation coverage scenarios revealed DT to be more influenced by these conditions. Error analysis indicates at low AOD (≤0.2), accuracy of both DB and DT were subject to underlying vegetation coverage. At AOD>0.2, DB performed well in retrieving coarse aerosols whereas DT was superior when fine aerosols dominated. Overall, accuracy of both VIIRS algorithms require further refinement to continue MODIS AOD legacy over South Asia.

Carbonaceous aerosols remote sensing from geostationary satellite observation, Part I: Algorithm development using critical reflectance

Current aerosol remote sensing without using multi-view satellite sensors still fail to retrieve its components directly, such as black carbon (BC) and organic carbon (OC) simultaneously due to the limitations on available observations. In this study, A new general carbonaceous aerosol retrieval strategy for geostationary single-view satellite observations is implemented based on a method of critical reflectance, to retrieve the parameters of BC and extra OC concentration from measured radiance without prior quantification of Aerosol Optical Thickness (AOT). The method is based on measuring the change in upward reflectance between clear and hazy days over a varying surface reflectance, with the aim of finding a balance between brightening due to scattering and darkening due to absorption. Since aerosols in the proposed algorithm are considered as a mixture of BC, OC and clustered shell aerosols, this balance can be used to retrieve concentration parameters of these components via radiative transfer models. Sensitivity tests indicate that different scenarios of BC and OC volume fractions can introduce certain sensitivity pattern on critical reflectance. The retrieval is achieved by minimizing the differences between measured critical reflectance and a set of look-up tables (LUTs). Random sampling consensus (RANSAC) is also used in the strategy to reduce the influence of clouds and anomalous pixels on the retrievals. Since critical reflectance should be calculated by comparing geometrically consistent clear and hazy images, the algorithm is more suitable for geostationary satellites with constant viewing geometries. An initial validation and application applied to Himawari images over the North China Plain (NCP) shows that the carbonaceous aerosol retrievals are highly consistent with the expectations in terms of spatial and temporal patterns. The retrievals of BC and OC concentration follow the daily fluctuations of the Aethalometers observations and feature small differences in mean values throughout the month. Additionally, the AOTs at 0.55 μm can be also satisfactorily reproduced based on the carbonaceous aerosol retrievals, resulting in a mean absolute error of 0.089 and a correlation coefficient of 0.870. The main errors in the method arise from shell model assumptions, RANSAC fitting bias, inconsistent clear reference AOT in LUT, and geometric inconsistency between the clear reference and hazy images. This work indicates that the BC and OC concentration in high resolution can be acquired through the geostationary single-view remote sensing for the further air quality and climate studies.

Luminance contrast provides metric depth information.

The perception of depth from retinal images depends on information from multiple visual cues. One potential depth cue is the statistical relationship between luminance and distance; darker points in a local region of an image tend to be farther away than brighter points. We establish that this statistical relationship acts as a quantitative cue to depth. We show that luminance variations affect depth in naturalistic scenes containing multiple cues to depth. This occurred when the correlation between variations of luminance and depth was manipulated within an object, but not between objects. This is consistent with the local nature of the statistical relationship in natural scenes. We also showed that perceived depth increases as contrast is increased, but only when the depth signalled by luminance and binocular disparity are consistent. Our results show that the negative correlation between luminance and distance, as found under diffuse lighting, provides a depth cue that is combined with depth from binocular disparity, in a way that is consistent with the simultaneous estimation of surface depth and reflectance variations. Adopting more complex lighting models such as ambient occlusion in computer rendering will thus contribute to the accuracy as well as the aesthetic appearance of three-dimensional graphics.

Towards uninterrupted canopy-trait time-series: A Bayesian radiative transfer model inversion using multi-sourced satellite observations

Canopy traits play an important role in plant growth, carbon sequestration, and the land surface water and energy balance, and thus it is important to understand and monitor how these traits change over time. Canopy radiative transfer models, which use canopy traits to predict spectra, provide a powerful means to understand canopy trait variation and these models can be inverted, allowing us to use remotely sensed data to monitor canopy traits. However, a single inversion represents a snapshot in time for a single sensor. In this study, we used a Bayesian approach to assimilate snow-free observations from three different sensors (Landsat 8, Sentinel-2A and MODIS) over the course of 2019 by inverting the ProSAIL radiative transfer model, producing a robust estimate of six canopy traits and their joint uncertainties at each time point. Next, we used a state-space time series model to integrate observations from multiple sensors to capture variation in vegetation optical traits throughout the year. The results demonstrate that our model can capture seasonal variation of surface reflectance with less than 5% RMSE compared with observed reflectance. Moreover, linking observations in time narrows the uncertainty intervals compared to the posterior distributions for individual time points. We are also able to make statistical inferences on dates when no data is available, and provide reasonable and reliable time-series inversion results compared with LAI ground measurements.

Evaluating the contribution of Sentinel-2 view and illumination geometry to the accuracy of retrieving essential crop parameters

ABSTRACT Wide field-of-view (FOV) sensors such as Sentinel-2 exhibit per-pixel view and illumination geometry variation that may influence the retrieval accuracy of essential crop biophysical and biochemical variables (BVs) for precision agriculture. However, this aspect is rarely studied in the existing literature. Hence, the current study aimed to evaluate the contribution of view and illumination geometries to the accuracy of retrieving Leaf Chlorophyll a and b (LC ab ), Canopy Chlorophyll Content (CCC), and Leaf Area Index (LAI) using the Random Forest (RF). The experiments were performed on various input variable scenarios where per-pixel geometric covariates, i.e. View and Sun Zenith Angles (VZA and SZA, respectively), and Relative Azimuth Angle (RAA), are excluded and included in spectral bands (SB) and spectral vegetation indices (SVIs), respectively, in two semi-arid areas. The results showed that spectral bands or vegetation indices combined with geometric covariates improved the R 2 by 10–15% for LAI and 3–5% for CCC. In contrast, negligible improvements of 1–2% were achieved for LC ab with cross-validation test data and independent held-out dataset, respectively. In agreement with previous studies, VZA and SZA were among the topmost influential variables in the RF models for estimating LAI, LC ab , and CCC. Collectively, per-pixel geometric variables explained more than 30% of the variability in surface reflectance for all Sentinel-2 spectral bands (p < 2.2e-16). Overall, the results showed that incorporating geometric covariates improved the accuracy of retrieving BVs; thus, it provided additional information that improves the predictive power of SB and SVIs. The significant benefits of the geometric variables were mainly realized for canopy-level BVs (i.e. LAI and CCC) than for LC ab . Therefore, it is recommended to incorporate per-pixel view and illumination geometry in estimating LAI and CCC, especially when using wide-view sensors such as Sentinel-2. However, further testing in different phenology, site and acquisition conditions is needed to confirm the contribution of the geometric covariates to facilitate reliable retrieval of BVs from remotely sensed data and aid better agronomic decisions.

Low-Coherence Measurement Methods for Industrial Parts With Large Surface Reflectance Variations

Low-Coherence Measurement Methods for Industrial Parts With Large Surface Reflectance Variations

Actualization of Semi-Empirical Equation for UV Index

Abstract UV radiation at ground level undergoes many variations in the present, due to the ozone column, aerosols, clouds, surface reflectivity, both positive and negative variations, depending on latitude. This work reports on the updating of the semi-empirical equation proposed by Madronich in 2007 for estimating the UV index, by updating its coefficients, as they encapsulate the state of the atmosphere. Data for Bucharest in 2019-2021 was used for fitting the equation. It was found that the new equation improves the accuracy of the estimation of the UVI in Bucharest five times for 2021 (from nRMSE = 17.3% to nRMSE=3.3%). The validity of the obtained equation was assessed in various locations, showing a great improvement of the estimation accuracy in most of them (in 12 of the 14 chosen locations), regardless of latitude. The equation produces a negative bias for locations having Koppen climate types A and B, a positive bias for the investigated locations situated in climate type D and no definite bias for temperate climates (type C). More studies are needed for drawing a definite conclusion and further improving the applicability of the equation.

The effects of lamp types and surface reflectance combinations on the subjective perception of a simulated lit hospital ward environment

PurposeGood illumination creates an aesthetic environment that may positively influence patients’ well-being and provide comfort to the hospital staff. This study aims to focus on exploring the energy efficiency of lighting and subjective perception of the lit environment in a hospital ward to assess quality indicators of ambient lighting conditions.Design/methodology/approachThe existing conventional tubular fluorescent lamp–based lighting system in the surveyed patients’ ward was retrofitted with light-emitting diode (LED) luminaires to explore illumination and energy parameters. Thereafter, a software lighting model was created, simulated and analyzed. A Web-based survey with five bipolar adjective pairs in a semantic differential scale was conducted with 48 participants to record and analyze their subjective responses pertaining to the variations in lamp types and surface reflectance combinations.FindingsThe findings imply that the LED tubular lamp–based illumination was deemed more adequate compared to other lamp types and the effects of variations in room surface reflectance combinations on the participants’ responses were statistically significant at α = 0.05 level. The simulated horizontal work plane average illuminance level varied from 131 to 171 lx, mean room surface exitance (MRSE) levels remained between 30 and 90 lm/m2 and overall uniformity of illuminance remained between 0.5 and 0.7.Originality/valueIn a hospital ward illuminated by LED tubular lamps, variations in room surface reflectance combinations for a constant luminous flux package output from the lamps may affect the subjective perception of users and the correlation between horizontal work plane average illuminance and MRSE is found to be highly linear (coefficient of determination > 0.97).

Warming/cooling effect of cropland expansion during the 1900s ~ 2010s in the Heilongjiang Province, Northeast of China.

Land cover change (LCC) significantly changed the local/regional temperature. This paper attempts to reveal the effects of cropland expansion in different ways on temperature change from the 1900s to 2010s in Heilongjiang Province. To reach this goal, we conducted four simulation research schemes with the coupled Weather Research and Forecast (WRF)-Noah model to investigate the warming/cooling effect of cropland expansion. The results show that cropland expansion exerted different effects with different land-use type conversions. In the last century, the areas with grassland-to-cropland and wetland-to-cropland transition show the warming effect, and the average surface temperature in Heilongjiang Province increased by 0.023 ℃ and 0.024 ℃, respectively. The areas with forest-to-cropland transition show the cooling effect, in which the average temperature decreased by 0.103 ℃. The variation of air temperature is mainly caused by the variation of surface reflectance and surface net radiation flux. The results provide evidence that cropland expansion changes to biophysical landscape characteristics, warming/cooling the land surface and thus enhancing/reducing the temperature, and lead to regional climate change eventually.

The Interplay between Canopy Structure and Topography and Its Impacts on Seasonal Variations in Surface Reflectance Patterns in the Boreal Region of Alaska—Implications for Surface Radiation Budget

Forests play an essential role in maintaining the Earth’s overall energy balance. The variability in forest canopy structure, topography, and underneath vegetation background conditions create uncertainty in modeling solar radiation at the Earth’s surface, particularly for boreal regions in high latitude. The purpose of this study is to analyze seasonal variation in visible, near-infrared, and shortwave infrared reflectance with respect to land cover classes, canopy structures, and topography in a boreal region of Alaska. We accomplished this investigation by fusing Landsat 8 images and LiDAR-derived canopy structural data and multivariate statistical analysis. Our study shows that canopy structure and topography interplay and influence reflectance spectra in a complex way, particularly during the snow season. We observed that deciduous trees, also tall with greater rugosity, are more dominant on the southern slope than on the northern slope. Taller trees are typically seen in higher elevations regardless of vegetation types. Surface reflectance in all studied wavelengths shows similar relationships with canopy cover, height, and rugosity, mainly due to close connections between these parameters. Visible and near-infrared reflectance decreases with canopy cover, tree height, and rugosity, especially for the evergreen forest. Deciduous forest shows more considerable variability of surface reflectance in all studied wavelengths, particularly in March, mainly due to the mixing effect of snow and vegetation. The multivariate statistical analysis demonstrates a significant tree shadow effect on surface reflectance for evergreen forests. However, the topographic shadow effect is prominent for deciduous forests during the winter season. These results provide great insight into understanding the role of vegetation structure and topography in surface radiation budget in the boreal region.

SENTINEL-2 SURFACE REFLECTANCE PRODUCTS GENERATED BY CNES AND DLR: METHODS, VALIDATION AND APPLICATIONS

Abstract. To allow for a robust and automatic exploitation of Sentinel-2 data, Analysis Ready Data (ARD) products are requested by most users. The processors of ARD products take care of the common burdens necessary for most applications, that include precise orthorectification, cloud detection and atmospheric correction steps, as well as the generation of periodic syntheses of cloud free surface reflectances. The French Theia land data center, and the German Earth Observation Center (EOC) started delivering Sentinel-2 surface reflectance products to users in 2016 in France and 2019 in Germany respectively. Both centers produce and distribute these data sets in near real time, over large regions requested by French users such as Western Europe, Maghreb, Sahel, Madagascar… Theia’s and EOC products include an instantaneous surface reflectance product (Level-2A), and a monthly cloud free synthesis of surface reflectance (Level-3A). This article shortly describes the methods used to generate the Level-2A products with the MAJA processor, and the Level-3A products with theWASP processor. The MAJA processor is based on multi-temporal methods, that use the slow variation of surface reflectance to detect clouds and estimate aerosol depth, while WASP, thanks to the quality of MAJA cloud mask, calculates a weighted average of all the cloud free observations over 45 days, every month. The article also provides validation results for Level-2A and Level-3A products, resulting from comparison with in-situ data and with other methods. A last section gives first insights from the monitoring of user uptake of the distributed products.

Retrieval and Validation of AOD from Himawari-8 Data over Bohai Rim Region, China

The geostationary satellite Himawari-8, possessing the Advanced Himawari Imager (AHI), which features 16 spectral bands from the visible to infrared range, is suitable for aerosol observations. In this study, a new algorithm is introduced to retrieve aerosol optical depth (AOD) over land at a resolution of 2 km from the AHI level 1 data. Considering the anisotropic effects of complex surface structures over land, Moderate Resolution Imaging Spectroradiometer (MODIS) bidirectional reflectance distribution function (BRDF) model parameters product (MCD19A3) is used to calculate the surface reflectance for Himawari-8’s view angle and band. In addition, daily BRDF model parameters are calculated in areas with dense vegetation, considering the rapid variation of surface reflectance caused by vegetation growth. Moreover, aerosol models are constructed based on long duration Aerosol Robotic Network (AERONET) single scattering albedo (SSA) values to stand for aerosol types in the retrieval algorithm. The new algorithm is applied to AHI images over Bohai Rim region from 2018 and is evaluated using the newest AERONET version 3 AOD measurements and the latest MODIS collection 6.1 AOD products. The AOD retrievals from the new algorithm show good agreement with the AERONET AOD measurements, with a correlation coefficient of 0.93 and root mean square error (RMSE) of 0.12. In addition, the new algorithm increases AOD retrievals and retrieval accuracy compared to the Japan Aerospace Exploration Agency (JAXA) aerosol products. The algorithm shows stable performance during different seasons and times, which makes it possible for use in climate or diurnal aerosol variation studies.

Theoretical Evaluation of Anisotropic Reflectance Correction Approaches for Addressing Multi-Scale Topographic Effects on the Radiation-Transfer Cascade in Mountain Environments

Research involving anisotropic-reflectance correction (ARC) of multispectral imagery to account for topographic effects has been ongoing for approximately 40 years. A large body of research has focused on evaluating empirical ARC methods, resulting in inconsistent results. Consequently, our research objective was to evaluate commonly used ARC methods using first-order radiation-transfer modeling to simulate ASTER multispectral imagery over Nanga Parbat, Himalaya. Specifically, we accounted for orbital dynamics, atmospheric absorption and scattering, direct- and diffuse-skylight irradiance, land cover structure, and surface biophysical variations to evaluate their effectiveness in reducing multi-scale topographic effects. Our results clearly reveal that the empirical methods we evaluated could not reasonably account for multi-scale topographic effects at Nanga Parbat. The magnitude of reflectance and the correlation structure of biophysical properties were not preserved in the topographically-corrected multispectral imagery. The CCOR and SCS+C methods were able to remove topographic effects, given the Lambertian assumption, although atmospheric correction was required, and we did not account for other primary and secondary topographic effects that are thought to significantly influence spectral variation in imagery acquired over mountains. Evaluation of structural-similarity index images revealed spatially variable results that are wavelength dependent. Collectively, our simulation and evaluation procedures strongly suggest that empirical ARC methods have significant limitations for addressing anisotropic reflectance caused by multi-scale topographic effects. Results indicate that atmospheric correction is essential, and most methods failed to adequately produce the appropriate magnitude and spatial variation of surface reflectance in corrected imagery. Results were also wavelength dependent, as topographic effects influence radiation-transfer components differently in different regions of the electromagnetic spectrum. Our results explain inconsistencies described in the literature, and indicate that numerical modeling efforts are required to better account for multi-scale topographic effects in various radiation-transfer components.