Snow Bidirectional Reflectance Distribution Function Research Articles

Non-Lambertian snow surface reflection models for simulated top-of-the-atmosphere radiances in the NIR and SWIR wavelengths

Accurately modeling snow reflectance is one way to improve satellite observations in the high-latitude regions. Snow surfaces are known to be challenging for atmospheric retrievals in the short-wave infrared (SWIR) wavelength regime due to their low reflectance. For example, current algorithms for satellite-based remote sensing of atmospheric carbon dioxide (CO2) do not take into account the unique reflective properties of snow surfaces. In this paper, we present a measurement-based snow surface reflectance model in the near-infrared (NIR; 755–775 nm) and SWIR (1590–1620 nm, 2040–2080 nm) bands used in remote sensing of atmospheric CO2. We study snow reflectance in detail using a novel atmospheric radiative transfer model (RTM) software and a measurement-based model for snow bi-directional reflectance distribution function (BRDF) to identify how the observations could be optimized in regards of observation geometry and wavelengths. The novel simulation software for NIR-SWIR atmospheric radiative transfer, Raysca, is presented and validated. Top-of-the-atmosphere radiance simulations show that forward-viewing geometries over snow-covered surfaces yield higher radiances than the traditional nadir-viewing geometries, which could indicate the preferability of forward-viewing observation modes in the retrieval of atmospheric CO2. Similarly, atmospheric observations in the 1.6 μm CO2 absorption band might be preferable to the 2.0 μm CO2 absorption band due to higher radiances in the 1.6 μm band.

A Practical Approach to Improve the MODIS MCD43A Products in Snow-Covered Areas

The MODerate Resolution Imaging Spectroradiometer (MODIS) MCD43A products have been extensively applied in the remote sensing field, but recent researchers have demonstrated that these products still had the potential to be further improved by using the latest development of the kernel-driven model [RossThick-LiSparseReciprocal-Snow (RTLSRS)] in snow-covered areas, since the MCD43A product algorithm [RossThick-LiSparseReciprocal (RTLSR)] needed to be improved for the accurate simulation of snow bidirectional reflectance distribution function (BRDF) signatures. In this paper, we proposed a practical approach to improve the MCD43A products, which used the Polarization and Directionality of the Earth's Reflectance (POLDER) observations and random forest algorithm to establish the relationship between the BRDF parameters (MCD43A1) estimated by the RTLSR and RTLSRS models. We applied this relationship to correct the MCD43A1 product and retrieve the corresponding albedo (MCD43A3) and nadir reflectance (MCD43A4). The results obtained highlight several aspects: (a) The proposed approach can perform well in correcting BRDF parameters [root mean square error (RMSE) = ~0.04]. (b) The corrected BRDF parameters were then used to retrieve snow albedo, which matched up quite well with the results of the RTLSRS model. (c) Finally, the snow albedo retrieved by the proposed approach was assessed using ground-based albedo observations. Results indicated that the retrieved snow albedo showed a higher accuracy as compared to the station measurements (RMSE = 0.055, bias = 0.005), which was better than the results of the MODIS albedo product (RMSE = 0.064, bias = −0.018), especially at large angles. These results demonstrated that this proposed approach presented the potential to further improve the MCD43A products in snow-covered areas.

A new snow bidirectional reflectance distribution function model in spectral regions from UV to SWIR: Model development and application to ground-based, aircraft and satellite observations

A generalized and flexible snow Bidirectional Reflectance Distribution Function (BRDF) model is proposed to describe the snow anisotropic properties in the spectral ranges from UltraViolet (UV) to Short-Wave InfraRed (SWIR). The new development belongs to the Fast and Accurate Semi-analytical Model of Atmosphere-surface Reflectance (FASMAR) family. FASMAR is developed, applied to and validated by ground-, aircraft- and satellite observations. FASMAR is a kernel-driven BRDF model. It contains four major kernels: isotropy kernel, asymptotic radiative transfer kernel, single-scattering kernel, and auxiliary kernels. These kernels enable FASMAR to characterize the angular-dependent scattering features and wavelength-dependent absorbing patterns of the snow BRDF properties. The comparison between FASMAR and RossThick-LiSparseReciprocal-Snow (RTLSRS) model were performed using radiative transfer simulations, ground-, aircraft- and satellite observations at selected wavelengths 380, 480, 670, 870, 1220, and 2200 nm. For Solar Zenith Angle (SZA) smaller than 70°, FASMAR shows somehow better accuracy compared to the RTLSRS model. FASMAR’s accuracy keeps quite stable for large SZAs (SZA larger than 70°). The relative difference between FASMAR calculations and radiative transfer simulations is within 5% for wavelengths between 380 and 2200 nm. A similar accuracy of FASMAR is observed compared to aircraft BRDF measurements, with a relative difference of less than 5%. FASMAR also shows good agreement with ground-based and satellite observations, with correlation coefficients larger than 0.9 and relative errors smaller than 2% for UV–vis and 4% for NIR-SWIR spectral ranges. The comparisons additionally reveal that the accuracy of FASMAR is not strongly affected by the a priori knowledge of the snow grain size. FASMAR can provide snow BRDF estimation with good accuracy in spectral regions from UV to SWIR under all observation/illumination geometries. FASMAR is used support the operational retrieval of the snow properties from satellite observations.

A New Index for Snow/Ice/Ice‐Snow Discrimination Based on BRDF Characteristic Observation Data

AbstractThe field observation data of ice‐snow (the ice covered by snow) is scarce, causing a poor understanding of its characteristics and some uncertainty in ice process simulation. Using a multi‐angle observation instrument, we conducted a field investigation and measured the snow/ice/ice‐snow Bidirectional Reflectance Distribution Function. Both snow and ice have the typical feature of forwarding scattering, and the scattering intensity is governed by their properties (i.g., snow grain size, liquid water content, and transparency). The characteristics of ice‐snow are similar to snow when a small amount of fresh snow is deposited on the ice surface, but will change significantly when the surface snow is metamorphic (melt or refreezing). This change can affect the monitoring accuracy of snow and ice using remote sensing data. In this study, a Normalized Difference Ice‐Snow Index (NDISI) was developed using two bands (i.e., 1,080–1,120 nm and 1,760–1,800 nm). We applied spectra from different regions to verify the ability of NDISI and its application for distinguishing ice‐snow. This work should be helpful for the fine classification between snow, ice, and ice‐snow. It can support a more accurate simulation of ice phenology.

Retrieval of Aerosol Optical Thickness in the Arctic Snow-Covered Regions Using Passive Remote Sensing: Impact of Aerosol Typing and Surface Reflection Model

Currently, no aerosol optical thickness (AOT) data set over the Arctic snow/ice-covered regions derived from space-borne passive remote sensing is available. The challenge is to develop an accurate and robust technique to derive AOT above highly variable and bright snow/ice surfaces. To extend data coverage of the eXtensible Bremen Aerosol/cloud and surfacE Retrieval (XBAER) AOT data product in the future, we propose a new algorithm for the retrieval of AOT and surface properties over snow/ice simultaneously. The algorithm utilizes the linear perturbation theory and does not use any simplified atmospheric correction techniques. Key issues like the selection of a proper aerosol type and optimal surface parameterization method for the retrieval of AOT over the Arctic have been investigated. The aerosol type is investigated using the aerosol climatology microphysical properties derived from four Aerosol Robotic Network (AERONET) sites (Barrow, Hornsund, Kangerlussuaq, and Tiksi). The three-parametric Ross-Li linear kernel model is used to describe the snow bidirectional reflectance distribution function (BRDF). The a priori knowledge of wavelength-dependent features of the coefficients in the Ross-Li linear kernel model is derived from Polarization and Directionality of the Earth's Reflectances (POLDER) measurements over the Arctic and utilized as constraints in the retrieval. The studies show that the combination of Ross-Li surface model and weakly absorbing aerosol parameterization provides an optimal way to derive AOT over the Arctic snow/ice-covered regions from passive remote sensing observations. The retrieved AOTs using POLDER show good agreement with AERONET observations.

Evaluation of the Snow Albedo Retrieved from the Snow Kernel Improved the Ross-Roujean BRDF Model

The original kernel-driven bidirectional reflectance distribution function (BRDF) models were developed based on soil-vegetation systems. To further improve the ability of the models to characterize the snow surface scattering properties, a snow kernel was derived from the asymptotic radiative transfer (ART) model and used in the kernel-driven BRDF model framework. However, there is a need to further evaluate the influence of using this snow kernel to improve the original kernel-driven models in snow albedo retrieval applications. The aim of this study is to perform such an evaluation using a variety of snow BRDF data. The RossThick-Roujean (RTR) model is used as a framework for taking in the new snow kernel (hereafter named the RTS model) since the Roujean geometric-optical (GO) kernel captures a neglectable hotspot effect and represents a more prominent dome-shaped BRDF, especially at a small solar zenith angle (SZA). We obtained the following results: (1) The RTR model has difficulties in reconstructing the snow BRDF shape, especially at large SZAs, which tends to underestimate the reflectance in the forward direction and overestimate reflectance in the backward direction for various data sources. In comparison, the RTS model performs very well in fitting snow BRDF data and shows high accuracy for all data. (2) The RTR model retrieved snow albedos at SZAs = 30°–70° are underestimated by 0.71% and 0.69% in the red and near-infrared (NIR) bands, respectively, compared with the simulation results of the bicontinuous photon tracking (bic-PT) model, which serve as “real” values. However, the albedo retrieved by the RTS model is significantly improved and generally agrees well with the simulation results of the bic-PT model, although the improved model still somewhat underestimates the albedo by 0.01% in the red band and overestimates the albedo by 0.05% in the NIR band, respectively, at SZAs = 30°–70°, which may be negligible. (3) The albedo derived by these two models shows a high correlation (R2 > 0.9) between the field-measured and Polarization and Directionality of the Earth's Reflectances (POLDER) data, especially for the black-sky albedo. However, the albedo derived using the RTR model is significantly underestimated compared with the RTS model. The RTR model underestimates the black-sky albedo (white-sky albedo) retrievals by 0.62% (1.51%) and 0.93% (2.08%) in the red and NIR bands, respectively, for the field-measured data. The shortwave black-sky and white-sky albedos derived using the RTR model for the POLDER data are underestimated by 1.43% and 1.54%, respectively, compared with the RTS model. These results indicate that the snow kernel in the kernel-driven BRDF model frame is more accurate in snow albedo retrievals and has the potential for application in the field of the regional and global energy budget.

Aerosol Optical Depth over the Arctic Snow-Covered Regions Derived from Dual-Viewing Satellite Observations

Aerosol properties over the Arctic snow-covered regions are sparsely provided by temporal and spatially limited in situ measurements or active Lidar observations. This introduces large uncertainties for the understanding of aerosol effects on Arctic climate change. In this paper, aerosol optical depth (AOD) is derived using the advanced along-track scanning radiometer (AATSR) instrument. The basic idea is to utilize the dual-viewing observation capability of AATSR to reduce the impacts of AOD uncertainties introduced by the absolute wavelength-dependent error on surface reflectance estimation. AOD is derived assuming that the satellite observed surface reflectance ratio can be well characterized by a snow bidirectional reflectance distribution function (BRDF) model with a certain correction direct from satellite top of the atmosphere (TOA) observation. The aerosol types include an Arctic haze aerosol obtained from campaign measurement and Arctic background aerosol (maritime aerosol) types. The proper aerosol type is selected during the iteration step based on the minimization residual. The algorithm has been used over Spitsbergen for the spring period (April–May) and the AOD spatial distribution indicates that the retrieval AOD can capture the Arctic haze event. The comparison with AERONET observations shows promising results, with a correlation coefficient R = 0.70. The time series analysis shows no systematical biases between AATSR retrieved AOD and AERONET observed ones.

An assessment of the performance of two snow kernels in characterizing snow scattering properties

ABSTRACTThe kernel-driven RossThick-LiSparseReciprocal (RTLSR) bidirectional reflectance distribution function (BRDF) model has been widely used in the quantitative remote sensing community. However, the performance of this model is challenged when modelling the optical scattering properties of pure snow surfaces. Recently, two snow kernels have been developed to improve the snow anisotropic reflectance in the kernel-driven RTLSR model framework. However, the performances of these two snow kernels must be assessed to identify their potential applications. Therefore, we assess the performances of these two kernels using various BRDF data sources. Our findings demonstrate their differences in several aspects. (1) These two kernels differ in characterizing the variability in BRDF shape as a function of the solar zenith angle (SZA). As the SZA increases, the shape of snow kernel derived by the asymptotic radiative transfer (ART) model (hereinafter named the ART method) changes from a dome shape to bowl shape, which agrees well with the simulation data of the bicontinuous photon tracking (bic-PT) model. The shape of the snow kernel proposed by Qu et al. (hereinafter named the Qu method) based on the Rahman-Pinty-Verstraete (RPV) model maintains a bowl shape for all SZAs. These differences in the kernel performances affect their abilities to fit snow BRDF data with different SZAs. (2) The corrected RTLSR models, with their respective snow kernels, are generally able to model the forward-scattering properties of snow surfaces compared with the original RTLSR model. However, the ART method performs better in capturing the BRDF variations in snow surfaces than the Qu method. This assessment provides an improved understanding of the performance of these two snow kernels and, thus, suggests further applications for the ART snow kernel in the kernel-driven BRDF model framework in the near future.

Development of a snow kernel to better model the anisotropic reflectance of pure snow in a kernel-driven BRDF model framework

The linear kernel-driven RossThick-LiSparseReciprocal (RTLSR) bidirectional reflectance distribution function (BRDF) model was originally developed from the simplified scenarios of continuous and discrete vegetation canopies, and has been widely used to fit multiangle observations of vegetation-soil systems of the land surface in many fields. Although this model was not developed explicitly for snow surfaces, it can capture the geometric-optical effect caused by the shadowing of rugged or undulating snow surfaces. However, in this study, this model has been further developed to better characterize the scattering properties of snow surface, which can also exhibit strongly forward-scattering behavior. This study proposes a new snow kernel to characterize the reflectance anisotropy of pure snow based on the asymptotic radiative transfer (ART) model that assumes snow can be modeled as a semi-infinite, plane-parallel, weakly absorbing light scattering layer. This new snow kernel adopts a correction term with a free parameter α to correct the analytic form of the ART model that has been reported to underestimate observed snow reflectance in the forward-scattering direction in the principal plane (PP), particularly in cases of a large viewing zenith angle (>60°). This snow kernel has now been implemented in the kernel-driven RTLSR BRDF model framework in conjunction with two additional kernels (i.e., the volumetric scattering kernel and geometric-optical scattering kernel) and is validated using observed and simulated multiangle data from three data sources. Pure snow targets were selected from the extensive archive of the Polarization and Directionality of the Earth's Reflectance (POLDER) BRDF data. Antarctic snow field measurements, which were taken from the top of a 32-m-tall tower at Dome C Station and include 6336 spectral bidirectional reflectance factors (BRFs), were also utilized. Finally, a set of simulated BRFs, generated by a hybrid scattering snow model that combines the geometric optics with vector radiative transfer theory, were used to further assess the proposed method. We first retrieve the value of the free parameter α for a comprehensive analysis using single multiangle snow data with a sufficient BRDF sampling. Then, we determine the optimally fixed value of the α parameter as prior information for potential users. The new snow kernel method is shown to be quite accurate, presenting a high correlation coefficient (R2 = ~0.9) and a negligible bias between the modeled BRFs and the various snow BRDF validation data. The finding demonstrates that this snow kernel provides an improved potential compared to that of the original kernel-driven model framework for a pure snow surface in many applications, particularly those involving the global water cycle and radiation budget, where snow cover plays an important role.

Simultaneous Estimation of Multiple Land-Surface Parameters From VIIRS Optical-Thermal Data

Traditional methods for estimating land-surface parameters from remotely sensed data generally focus on a single parameter with a specific spectral region, resulting in physical and spatiotemporal inconsistencies in current satellite products. We recently proposed a unified inversion scheme to estimate a suite of parameters simultaneously from both visible and near-infrared and thermal-infrared MODIS data. In this letter, we implemented this scheme to estimate six time-series parameters [leaf area index, fraction of absorbed photosynthetically active radiation, surface albedo, land-surface emissivity, land-surface temperature (LST), and upwelling longwave radiation (LWUP)] from the Visible Infrared Imaging Radiometer Suite (VIIRS) data. Several components of these schemes are refined, including the incorporation of a snow bidirectional reflectance distribution function model, determination of the best band combination, and better estimation of the snow-covered surface emissivity by accounting for the snow-cover fraction. Validation using the measurements at 12 sites of SURFRAD, CarboEuropeIP, and FLUXNET, and intercomparisons with MODIS and Global Land-Surface Satellite products, are carried out: the retrieved albedo, LST, and LWUP achieved accuracies ( $R^{2}$ ) of 0.77, 0.96, and 0.95, root mean square errors of 0.06, 2.9 K, and 18.3 W/m2, and biases of 0.01, 0.09 K, and −0.08 W/m2, respectively. The retrieved parameters can achieve comparable or higher accuracy than existing products, which indicates that the unified algorithm can be applied effectively to the VIIRS data with high physical and temporal consistency and accuracy.

The VIS/NIR Land and Snow BRDF Atlas for RTTOV: Comparison between MODIS MCD43C1 C5 and C6

A monthly mean land and snow Bidirectional Reflectance Distribution Function (BRDF) atlas for visible and near infrared parts of the spectrum has been developed for Radiative Transfer for Television Infrared Observation Satellite (TIROS) Operational Vertical sounder (TOVS) (RTTOV). The atlas follows the methodology of the RTTOV University of Wisconsin infrared land surface emissivity (UWIREMIS) atlas, i.e., it combines satellite retrievals and a principal component analysis on a dataset of hyper-spectral surface hemispherical reflectance or albedo. The current version of the BRDF atlas is based on the Collection 5 of the Moderate Resolution Imaging (MODIS) MCD43C1 Climate Modeling Grid BRDF kernel-driven model parameters product. The MCD43C1 product combines both Terra and Aqua satellites over a 16-day period of acquisition and is provided globally at 0.05° of spatial resolution. We have improved the RTTOV land surface BRDF atlas by using the last Collection 6 of MODIS product MCD43C1. We firstly found that the MODIS C6 product improved the quality index of the BRDF model as compared with that of C5. When compared with clear-sky top of atmosphere (TOA) reflectance of Spinning Enhanced Visible and InfraRed Imagers (SEVIRI) solar channels over snow-free land surfaces, we showed that the reflectances are simulated with an absolute accuracy of 3% to 5% (i.e., 0.03–0.05 in reflectance units) when either the satellite zenith angle or the solar zenith angle is below 70°, regardless of the MODIS collection. For snow-covered surfaces, we showed that the comparison with in situ snow spectral albedo is improved with C6 with an underestimation of 0.05 in the near infrared.

Computation of a distributed glacier surface albedo proxy using airborne laser scanning intensity data and in-situ spectro-radiometric measurements

In recent years, multi-temporal topographic measurements from airborne laser scanning (ALS) have been increasingly used as a source of spatially explicit and accurate information to calculate geodetic glacier mass balances. Simultaneous to collecting topographic data, most ALS instruments record the backscattered intensity for each laser emission and therefore provide additional information on the reflectance characteristics of the surveyed surface. Along with air temperature, the surface albedo of snow and ice was identified as a major driving factor of glacier melt. Consequently, better knowledge on the spatial distribution of the glacier albedo could substantially improve energy balance based glacier melt modeling. In this study, we collected on-glacier spectro-radiometric and albedometer measurements to serve as a ground reference to radiometrically calibrate high resolution ALS intensity data into a distributed albedo proxy map. This method resulted in an albedo proxy with values between 0.6 on the glacier tongue and 0.9 on fresh snow in high altitudes. 99.6% of all values fell within the albedo boundary conditions, i.e. values between 0 and 1. Corrected near-infrared ALS intensity data provided a distributed product that allows simulating albedo in glacier energy and mass balance models more realistically. Remaining challenges are (i) a different surface albedo response in the visual part of the electro-magnetic spectrum, (ii) the low radiometric resolution of the ALS system for higher intensity values, and (iii) an insufficient correction of the snow bi-directional reflectance distribution function (BRDF).

Aerosol optical depth retrieval over snow using AATSR data

Aerosol observations over the Arctic are important because of the effects of aerosols on Arctic climate, such as their direct and indirect effects on the Earth's radiation balance and on snow albedo. Although information on aerosol properties is available from ground-based measurements, passive remote sensing using satellite measurements would offer the advantage of large spatial coverage with good temporal resolution, even though, due to light limitations, this is only available during the Arctic summer. However, aerosol optical depth (AOD) retrieval over the Arctic region is a great challenge due to the high reflectance of snow and ice and due to the high solar zenith angle. In this article, we describe a retrieval algorithm using Advanced Along-Track Scanning Radiometer (AATSR) data, a radiometer flying on the European Space Agency (ESA) Environmental Satellite (ENVISAT), which offers two views (near nadir and at 55° forward) at seven wavelengths in the visible thermal-infrared (VIS-TIR). The main idea of the Dual-View Multi-Spectral (DVMS) approach is to use the dual view to separate contributions to reflectance measured at the top of the atmosphere (TOA) due to atmospheric aerosol and the underlying surface. The algorithm uses an analytical snow bidirectional reflectance distribution function (BRDF) model for the estimation of the ratio of snow reflectances in the nadir and forward views, as well as an estimate of the atmospheric contribution to TOA reflectance obtained using the dark pixel method over the adjacent ocean surface, assuming that this value applies over nearby land surfaces in the absence of significant sources across the coastline. An iteration involving all four AATSR wavebands in the visible near-infrared (VIS-NIR) is used to retrieve the relevant information. The method is illustrated for AATSR overpasses over Greenland with clear sky in April 2009. Comparison of the retrieved AOD with AErosol Robotic Network (AERONET) data shows a correlation coefficient of 0.75. The AODs retrieved from AATSR using the DVMS approach and those obtained from AERONET data show similar temporal trends, but the AERONET results are more variable and the highest AOD values are mostly missed by the DVMS approach. Limitations of the DVMS method are discussed. The pure-snow BRDF model needs further correction in order to obtain a better estimation for mixtures of snow and ice.

Aerosol optical depth retrieval in the Arctic region using MODIS data over snow

The Arctic is vulnerable to the long-term transport of aerosols because they affect the surface albedo when particles are deposited on snow and ice. However, aerosol observations for this area are sparse and hence there is considerable uncertainty in the knowledge on the properties of the Arctic aerosol. Atmospheric remote sensing using satellite-based instruments offers an opportunity to obtain information on aerosol properties, in particular the aerosol optical depth (AOD) on an extended spatial scale as determined by the instrument's swath width. However, AOD retrieval over a bright surface is a difficult task because it is difficult to separate and explicitly describe the contribution of the surface and that due to back-scattering by aerosols to the radiance observed by a satellite at the top of the atmosphere (TOA), especially at large Solar Zenith Angles (SZA). In this paper, an approach to achieve this is presented based on a synergetic approach using data from both Moderate Resolution Imaging Spectroradiometer (MODIS) instruments flying on the TERRA and AQUA satellites. The approached also uses prior knowledge for aerosol properties retrieval over snow as well as a Snow Bidirectional Reflectance Distribution Function (BRDF) model. The detailed analysis of the model results demonstrates that the Aerosol Properties Retrieval over Snow (APRS) algorithm is suitable for Arctic region Aerosol Optical Depth (AOD) retrieval. The study periods include April 2010 and April 2011, when the Arctic haze mostly occurs. Six AERONET stations at high latitude (Andenes, Barrow, Ittoqqortoormiit, OPAL, Thule, and PEARL) were used for comparison. The correlation coefficient between retrieved AODs and AERONET AODs was 0.8 and the relative error is between 10% and 20%, demonstrating the potential of the APRS method to retrieve AOD over the Arctic, with highly reflective snow/ice surfaces and large solar zenith angles.

Algorithm for retrieval of the effective snow grain size and pollution amount from satellite measurements

We present an algorithm for retrieval of the effective Snow Grain Size and Pollution amount (SGSP) from satellite measurements. As well as our previous version (Zege et al., 2008, 1998), the new algorithm is based on the analytical solution for snow reflectance within the asymptotic radiative transfer theory. The SGSP algorithm does not use any assumptions on snow grain shape and allows for the snow pack bidirectional reflectance distribution function (BRDF). The algorithm includes a new atmospheric correction procedure that allows for snow BRDF. This SGSP algorithm has been thoroughly validated with computer simulations. Its sensitivity to the atmosphere model has been investigated. It is shown that the inaccuracy of the snow characteristic retrieval due to the uncertainty in the aerosol and molecular atmosphere model is negligible, as compared to that due to the measurement errors at least for aerosol loads typical for polar regions. The significant advantage of the SGSP over conventional algorithms, which use a priori assumptions about particle shape and (or) not allow for the BRDF of the individual snow pack, is that the developed retrieval still works at low sun elevations, which are typical for polar regions.

High-accuracy measurements of snow Bidirectional Reflectance Distribution Function at visible and NIR wavelengths – comparison with modelling results

Abstract. High-accuracy measurements of snow Bidirectional Reflectance Distribution Function (BRDF) were performed for four natural snow samples with a spectrogonio-radiometer in the 500–2600 nm wavelength range. These measurements are one of the first sets of direct snow BRDF values over a wide range of lighting and viewing geometry. They were compared to BRDF calculated with two optical models. Variations of the snow anisotropy factor with lighting geometry, wavelength and snow physical properties were investigated. Results show that at wavelengths with small penetration depth, scattering mainly occurs in the very top layers and the anisotropy factor is controlled by the phase function. In this condition, forward scattering peak or double scattering peak is observed. In contrast at shorter wavelengths, the penetration of the radiation is much deeper and the number of scattering events increases. The anisotropy factor is thus nearly constant and decreases at grazing observation angles. The whole dataset is available on demand from the corresponding author.

Efficient use of an improved radiative transfer code to simulate near-global distributions of satellite-measured radiances.

Two new extension modules that give the water-leaving radiance from the ocean and the snow bidirectional reflectance distribution function were implemented in the latest radiative transfer code. In addition, to simulate the near-global distributions of satellite-measured radiances by using the improved radiative transfer code, we tested and applied the look-up table method together with the process-separation technique of the radiative transfer calculation. The computing time was reduced from 1 year to 20 s to simulate one channel, one scene of the Global Imager image by use of an Alpha 21164A-2 (600-MHz) machine. The error analyses showed that the radiances were simulated with less than 1% error for the nonabsorbing visible channels and approximately 2% error for absorbing channels by use of this method.

New methods to infer snow albedo from the MISR instrument with applications to the Greenland ice sheet

Snow-covered surfaces have a very high surface albedo, thereby allowing little energy to be absorbed by the snowpack. As the snowpack ages and/or begins to melt, the snow albedo decreases and more solar energy is absorbed by the snowpack. Therefore, accurate estimation of snow albedo is essential for monitoring the state of the cryosphere. This paper examines the retrieval of snow albedo using data from the Multi-angle Imaging SpectroRadiometer (MISR) instrument over the Greenland ice sheet. Two different methods are developed and examined to derive the snow albedo: one based on the spectral information from MISR and one utilizing the angular information from the MISR instrument. The latter method is based on a statistical relationship between in situ albedo measurements and the MISR red channel reflectance at all MISR viewing angles and is found to give good agreement with the ground-based measurements. Good agreement is also found using the spectral information, although the method is more sensitive to instrument calibration, snow bidirectional reflectance distribution function models, and narrowband-to-broadband relationships. In general, using either method retrieves snow surface albedo values that are within about 6% of that measured at the stations in Greenland.

Remote sensing of snow grain-size and impurities from Airborne Multispectral Scanner data using a snow bidirectional reflectance distribution function model

AbstractAlgorithms to retrieve the snow grain-size and the concentration of snow impurities were developed using a theoretical bidirectional reflectance distribution function (BRDF) model of the snow surface. In this model, snow grains are assumed to be independent spherical ice particles, and the BRDF is calculated with multiple scattering by snow particles. Using these algorithms, the snow grain-size and snow impurities were retrieved from Airborne Multispectral Scanner (AMSS) images at the visible (λ = 0.545 μm) and near-infrared (λ =1.24,1.64 and 2.23 μm) wavelengths observed over the flat snowfield in eastern Hokkaido, Japan, in February 1998. The estimated snow grain-size and impurities were consistent with the results of in situ measurements on the snow surface. For snow grain-size, measured reflectances in the different near-infrared AMSS channels indicated grain-size differences in the vertical profile of the snowpack.