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1 MODIS BRDFèAlbedo Product: Algorithm Theoretical Basis Document Version 5.0 Principal Investigators: A. H. Strahler, J.P. Muller, MODIS Science Team Members Development Team Alan H. Strahler 1, Wolfgang Lucht 3, Crystal Barker Schaaf 1, Trevor Tsang 1,Feng Gao 1, Xiaowen Li 1;4, JanPeter Muller 2, Philip Lewis 2, Michael J. Barnsley 5 1 Boston University 2 University College London 3 Potsdam Institut fíur Klimafolgenforschung 4 Beijing Normal University 5 University of Wales, Swansea Contributors Nick Strugnell, Baoxin Hu, Andrew Hyman, Robert P d'entremont, Liangzan Chen, Yi Liu, Doug McIver, Shunlin Liang, Matt Disney, Paul Hobson, Mike Dunderdale, Gareth Roberts MODIS Product ID: MOD43 Version 5.0 í April 1999
2 2 Abstract The MODIS BRDFèAlbedo product combines registered, multidate, multiband, atmospherically corrected surface reæectance data from the MODIS and MISR instruments to æt a Bidirectional Reæectance Distribution Function èbrdfè in seven spectral bands at a 1 km spatial resolution on a 16day cycle. From this characterization of the surface anisotropy, the algorithm performs angular integrations to derive intrinsic land surface albedos for each spectral band and three broad bands covering the solar spectrum. The albedo measures are a directional hemispherical reæectance èblack sky albedoè obtained by integrating the BRDF over the exitance hemisphere for a single irradiance direction, and a bihemispherical reæectance èwhite sky albedoè obtained by integrating the BRDF over all viewing and irradiance directions. Because these albedo measures are purely properties of the surface and do not depend on the state of the atmosphere, they can be used with any atmospheric speciæcation to provide true surface albedo as an input to regional and global climate models. The atlaunch algorithm makes use of a linear kernelbased model í the semiempirical reciprocal RossThickLiSparse model. Validation of the BRDF model and its performance under conditions of sparse angular sampling and noisy reæectances shows that the retrievals obtained are generally reliable. The solarzenith angle dependence of albedo may be parameterized by a simple polynomial that makes it unnecessary for the user to be familiar with the underlying BRDF model. Spectraltobroadband conversion is achieved using banddependent weighting factors. The intrinsic land surface albedos may be used to derive actual albedo by taking into account the prevailing distribution of diæuse skylight. Albedo is a fundamental parameter for climate modeling, since it is a property that drives much of the energy æux at the land boundary layer. Global maps of land surface albedo, which can be provided at æne to coarse scales using the BRDFèAlbedo Product, will be extremely useful to the global and regional climate modeling communities.
3 3 Contents I ALGORITHM AND DATA PRODUCT IDENTIFICATION 5 II INTRODUCTION 5 III DEFINITIONS 7 IV THEORETICAL FRAMEWORK 9 IVAKernelBased BRDF Model and Inversion IVB Albedo From the BRDF Model IVC Basic Polynomial Representation of Albedo IVDAtmospheric Eæects IVE SpectraltoBroadband Albedo Conversion V ALGORITHM DETAILS 13 VA The RossLi BRDF model VB RossLi Polynomial Albedo Representation VC Atmospheric eæects VD SpectraltoBroadband Albedo Conversion VI ALGORITHM VALIDATION 17 VIARossLi BRDF Model Validation VIB Inversion Accuracy with Sparse Angular Sampling VIC Noise Sensitivity VIDAccuracy of Diæuse Skylight Approximation VIE Accuracy of SpectraltoBroadband Conversion VIF Albedo Field Validation VII MODIS BRDFèALBEDO PROCESSING ALGORITHM 27 VIIAMODIS Albedo Data Basis and Algorithm VIIBMODIS BRDFèAlbedo Product VIICAlbedo Derivation From Very Sparse Angular Sampling VIIDBRDF Model Alternatives VIIEMODIS Albedo Prototyping VIIF Biophysical Interpretation VIII CONCLUSION 35
4 4 IX SPECIFICATION of MODIS BRDFèALBEDO PRODUCT èmod43bè 37 X ACKNOWLEDGEMENTS 38 XI REFERENCES 38 XII FIGURES 44
5 5 MODIS BRDFèAlbedo Product: Algorithm Theoretical Basis Document Version 5.0 í MOD43 AtLaunch: I. ALGORITHM AND DATA PRODUCT IDENTIFICATION æ MOD43, Surface Reæectance; Parameter 3669, Bidirectional Reæectance æ MOD43, Surface Reæectance; Parameter 4332, Albedo PostLaunch: æ MOD43, Surface Reæectance, Parameter 3665, BRDF with Topographic Correction æ MOD43, Surface Reæectance, Parameter 4333, Albedo withtopographic Correction II. INTRODUCTION LAND surface albedo is one of the most important parameters characterizing the earth's radiative regime and its impact on biospheric and climatic processes ë1ëíë3ë. Albedo is related to land surface reæectance by directional integration and is therefore dependent on the bidirectional reæectance distribution function èbrdfè, which describes how the reæectance depends on view and solar angles ë4ë. Speciæcation of the BRDF provides land surface reæectance explicitly in terms of its spectral, directional, spatial and temporal characteristics. Figure 1 illustrates key causes for land surface reæectance anisotropy and the resulting nonlinear relationship between albedo and the reæectance observed by a satellite from a given direction èsee ë5ë, ë6ë for examplesè. Albedo quantiæes the radiometric interface between the land surface and the atmosphere. On the one hand it deænes the lower boundary for atmospheric radiative transfer ë7ë, ë8ë, on the other it details the total shortwave energy input into the biosphere and is a key inæuence on the surface energy balance ë9ë. BRDF and albedo have recently received much attention in advanced remote sensing data analysis ë10ëíë12ë. This is due to the fact that the BRDF may be used to standardize reæectance observations with varying sunview geometries to a common standard geometry ë13ë, ë14ë, a problem frequently encountered in image mosaicking and temporal intercomparisons. The BRDF is also a potential source of biophysical information about the land surface viewed ë15ëíë17ë. Most importantly,however, it allows speciæcation of land surface albedo ë18ëíë21ë. Both BRDF and albedo are determined by land surface structure and optical properties. Sur
6 6 face structure inæuences the BRDF for example by shadowcasting, mutual view shadowing, and the spatial distribution of vegetation elements. Surface optical characteristics determine the BRDF for example through vegetationsoil contrasts and the optical attributes of canopy elements. As a consequence, the spatial and temporal distribution of these land surface properties as seen in BRDF and albedo features reæect a variety of natural and human inæuences on the surface that are of interest to global change research. Such inæuences on albedo are for example due to agricultural practices, deforestation and urbanization; the phenological cycle, for example seasonal dependence and agricultural greenupèharvesting; meteorological parameters, for example soil wetness and snowfall distribution; and climatological trends, for example desertiæcation, vegetation cover changes, and snowfall patterns. Climate models currently employ albedo values derived from the land cover type of each grid box ë1ë. The underlying albedo tables go back tovarious æeld measurements ë22ë, coarseresolution topofatmosphere ERBE observations ë23ëíë25ë or are computed from models ë26ë. However, there is a need for a more accurate speciæcation of albedo ë22ë, ë27ë as a function of land cover type, season and solar zenith angle. This can only be achieved from accurate derivations of kilometerscale albedo datasets for large areas, allowing the study of the magnitude distribution and of the spatial aggregation of values to coarser resolution for each land cover type and land cover mosaic ë28ëíë30ë. Remote sensing is the most suitable technique for deriving large consistent data sets of land surface parameters ë31ë. This paper presents an algorithm for the derivation of land surface albedo from atmospherically corrected cloudcleared multiangular reæectance observations from space. Symbols are deæned in section III. Section IV provides a generic mathematical outline of the algorithm, while section V gives a speciæc realization of the algorithm, especially of the models used. Section VI validates the algorithm by investigating in detail the practical properties of all components, especially of the BRDF model. Section VII gives details of the implementation of this algorithm for BRDFèalbedo processing of data from NASA's Moderate Resolution Imaging Spectroradiometer èmodisè, providing an atlaunch status of this algorithm. It also demonstrates the algorithm on a remotely sensed data set. Section VIII summarizes conclusions. Section IX provides a speciæcation of the product for the user. ènote: This document is modiæed from a technical paper submitted to IEEE Transactions on Geoscience and Remote Sensing entitled "An Algorithm for the Retrieval of Albedo From Space Using Semiempirical BRDF Models" by W. Lucht, C.B. Schaaf and A.H. Strahlerè.
7 7 III. DEFINITIONS The following is a summary of symbols used in deæning the algorithm. Spectral and directional parameters: ç = solar zenith angle è1è è = view zenith angle è2è ç = viewsun relative azimuth angle è3è ç = wavelength è4è æ = waveband æ of width æç è5è Atmospheric parameters: ç èçè = atmospheric optical depth è6è Sèç; çèçèè = fraction of diæuse skylight, è7è assumed isotropic Dèç; ç; ç èçèè = bottomofatmosphere è8è downwelling radiative æux BRDFrelated quantities: çèç; è; ç; æè = an atmospherically corrected è9è observed reæectance Rèç; è; ç; æè = bidirectional reæectance è10è distribution function èbrdfè in waveband æ K k èç; è; çè = BRDF model kernel k è11è h k èçè = integral of BRDF model è12è kernel k over è and ç H k = integral of h k èçè over ç è13è f k èæè = BRDF kernel model è14è parameter k in waveband æ Albedos: a bs èç; çè = spectral blacksky albedo è15è
8 8 a ws èçè = spectral whitesky albedo è16è aèç; çè = spectral albedo è17è Aèçè = broadband albedo è18è Polynomial representations of albedo: P j èçè = polynomial expression term j è19è g jk = coeæcient j of a polynomial è20è representation of h k èçè p j èæè = coeæcient j ofapoynomial è21è representation of a bs èç; æè, where p j èæè = P k g jk f k èæè
9 9 IV. THEORETICAL FRAMEWORK The following provides a mathematical outline of an algorithm for the derivation of BRDF and albedo from atmospherically corrected multiangular reæectance observations. A. KernelBased BRDF Model and Inversion The BRDF is expanded into a linear sum of terms èthe socalled kernelsè characterizing diæerent scattering modes. The superposition assumes that these modes are either spatially distinct within the scene viewed with little crosscoupling, physically distinct within a uniform canopy with negligible interaction, or empirically justiæed. The resulting BRDF model is called a kernelbased BRDF model ë32ë, ë33ë: X Rèç; è; ç; æè = f k èæèk k èç; è; çè è22è k Given reæectance observations çèæè made at angles èç l ;è l ;ç l è, 2 k =0of a leastsquares error function e 2 èæè = 1 d X l èçèç l ;è l ;ç l ; æè, Rèç l ;è l ;ç l ; æèè 2 w l èæè leads to analytical solutions for the model parameters f k, f k èæè = X i æ 8 éx : j è X l çèç j ;è j ;ç j ; æèk i èç j ;è j ;ç j è w j èæè K i èç l ;è l ;ç l èk k èç l ;è l ;ç l è w l èæè!,1 9 = ; ; where w l èæè is a weight given to each respective observation èe.g., w l èæè = 1 or w l èæè = çèç l ;è l ;ç l ; æèè, and d are the degrees of freedom ènumber of observations minus number of parameters f k è. The term in brackets that is to be inverted is the inversion matrix of the linear system which states the minimization problem. It is interesting to note that this inversion problem can also be formulated in terms of the variances and covariances of the kernel functions, and the covariances of the kernel functions with the observations ècf. ë34ëè. The fact that a simple BRDF model of this type is linear in its parameters and possesses an analytical solution is a tremendous advantage in largescale operational data processing and analysis ë33ë, ë35ë. Other types of BRDF models require numerical procedures that tend to be costly. B. Albedo From the BRDF Model Albedo is deæned as the ratio of upwelling to downwelling radiative æux at the surface. Downwelling æux may be written as the sum of a direct component and a diæuse component. Blacksky albedo is è23è è24è
10 10 deæned as albedo in the absence of a diæuse component and is a function of solar zenith angle. Whitesky albedo is deæned as albedo in the absence of a direct component when the diæuse component is isotropic. It is a constant. Mathematically, the directionalhemispherical and bihemispherical integrals of the BRDF model kernels are deæned as h k èçè = H k = 2 Z 2ç Z ç=2 0 0 Z ç=2 0 K k èç; è; çè sinèèè cosèèèdèdç; h k èçè sinèçè cosèçèdç: è25è è26è Blacksky and whitesky albedo are then given by a bs èç; æè = X k a ws èæè = X k f k èæèh k èçè; f k èæèh k : è27è è28è Note that the kernel integrals h k èçè and H k do not depend on the observations; they may therefore be precomputed and stored. This is a feature speciæc to kernelbased BRDF models. C. Basic Polynomial Representation of Albedo Given model parameters f k retrieved from multiangular reæectance observations, albedo is given by integrals of the BRDF model. Since analytical expressions for these integrals are most likely not available, they have to be tabulated. However, in many circumstances a simple analytical expression is preferable. Since the directional dependence of albedo is much less complex than that of the BRDF, a simple parameterization by polynomials of the solar zenith angle should suæce: h k èçè = X j g jk P j èçè; è29è a bs èç; æè = X k f k èæè X j g jk P j èçè = X j p j èæèp j èçè: è30è Due to the linearity of the BRDF model, there is a linear relationship between the polynomial coeæcients for the kernel integrals and those for the blacksky albedo, p j èæè = X k g jk f k èæè: è31è This allows to link the polynomial representation of individual kernel integrals to that of a speciæc albedo function.
11 11 D. Atmospheric Eæects Blacksky albedo and whitesky albedo mark the extreme cases of completely direct and completely diæuse illumination. Actual albedo is a value interpolated between these two depending on the aerosol optical depth ç. If diæuse skylight is assumed to be an isotropic fraction Sèçè of total illumination, then ë36ë aèç; æè = f1, Sèç; çèæèèga bs èç; æè è32è +Sèç; çèæèèa ws èæè X X = f1, Sèç; çèæèègg jk f k èæèp j èçè k j +Sèç; çèæèèf k èæèh k ; where f k èæè are from BRDF observations, H k, P j èçè and g jk are known precomputed mathematical quantities independent of the surface observations and the atmosphere, and Sèç; çèæèè is the atmospheric state. E. SpectraltoBroadband Albedo Conversion Earth scanning remote sensing instruments generally acquire data in narrow spectral bands. However, the total energy reæected by the earth's surface in the shortwave domain is characterized by the shortwave è0.3í5.0 çmè broadband albedo. Frequently, the visible è0.3í0.7 çmè and nearinfrared è0.7í5.0 çmè broadband albedos are also of interest due to the marked diæerence of the reæectance of vegetation in these two spectral regions. Spectraltobroadband conversion may proceed in two steps. The ærst is a spectral interpolation and extrapolation, aèç; çè =F èaèç; æèè; è34è where F is a function or procedure supplying albedo at any wavelength in the broadband range based on the given spectral values. This function may, for example, be a splining function with adequately chosen tiedown points at either end of the range, or a typical spectrum characterizing the scene type that is ætted to the observed spectral albedos. The second step is then the conversion of spectral albedo aèç; çè to broadband albedo Aèçè. The latter is deæned as the ratio of broadband upwelling radiative æux U BB to broadband downwelling æux, D BB. Therefore, U BB = R Uèçèdç = AD BB = A R Dèçèdç = R aèçèuèçèdç, from which follows that spectral albedo is related to broadband albedo through spectral integration weighted by the bottomofatmosphere downwelling spectral solar æux D, è33è Aèçè = R ç2 ç 1 aèç; çèdèç; ç; çèçèèdç : è35è Dèç; ç; çèçèèdç R ç2 ç 1
12 12 Since D is dependent on atmospheric state, broadband albedo is also dependent on atmospheric state in addition to the dependence of spectral albedo upon the diæuse skylight. If a standard atmosphere with given çèçè is assumed to represent a typical situation, the two steps may be combined by determining empirical conversion coeæcients c i that directly convert narrowband spectral albedos into broadband albedos: X Aèçè = c i aèç; æ i è; è36è i where c i are appropriately chosen weights reæecting the distribution of downwelling radiative æux with respect to the location of the available spectral bands. They may be empirically determined from computer simulation ë37ë.
13 13 V. ALGORITHM DETAILS Following the outline given, what is required for deriving albedo from satellite observations are realizations of the following: 1. Multiangular reæectance observations that are atmospherically corrected. 2. A kerneldriven BRDF model. 3. The polynomial representation h k èçè = P j p j P j èçè for each BRDF kernel. 4. A simple model relating S to ç as a function of wavelength ç. 5. The interpolation function spectraltobroadband, F, and some representation of downwelling æux Dèç; ç; çèçèè, or conversion coeæcients c i for a given atmosphere. A. The RossLi BRDF model A BRDF model is required for deriving albedo from multiangular reæectance observations. Several models of the form given by equation è22è are available. The modiæed Walthall model ë19ë, ë38ë is an empirical kerneldriven model with simple trigonometric expressions as kernels. The model by Roujean et al. ë32ë and those by Wanner et al. ë33ë are semiempirical. The kernels in these models are derived from more complex physical theory through simplifying assumptions and approximations. Since these models provide a simple parameterization of an otherwise potentially complicated function, they sometimes are also called parametric models. The theoretical basis of these semiempirical models is that the land surface reæectance is modeled as a sum of three kernels representing basic scattering types: isotropic scattering, radiative transfertype volumetric scattering as from horizontally homogeneous leaf canopies, and geometricoptical surface scattering as from scenes containing threedimensional objects that cast shadows and are mutually obscurred from view at oænadir angles. Equation è22è then takes on the speciæc form given by Roujean et al. ë32ë, Rèç; è; ç; æè = f iso èæè + f vol èæèk vol èç; è; çè è37è +f geo èæèk geo èç; è; çè: One may think of the volumescattering term as expressing eæects caused by the small èinterleafè gaps in a canopy whereas the geometricoptical term expresses eæects caused by the larger èintercrownè gaps. A suitable expression for K vol was derived by Roujean et al. ë32ë. It is called the RossThick kernel for its assumption of a dense leaf canopy. It is a singlescattering approximation of radiative transfer theory by Ross ë39ë consisting of a layer of small scatterers with uniform leaf angle distribution, a Lambertian background, and equal leaf transmittance and reæectance. Its form, normalized to zero for ç =0,è =0,is K vol = k RT = èç=2, çè cos ç + sin ç cos ç + cos è, ç 4 è38è
14 14 A suitable expression for k geo that has been found to work well with observed data was derived by Wanner et al. ë33ë. It is called the LiSparse kernel for its assumption of a sparse ensemble of surface objects casting shadows on the background, which is assumed Lambertian. The kernel is derived from the geometricoptical mutual shadowing BRDF model by Li and Strahler ë40ë. It is given by the proportions of sunlit and shaded scene components in a scene consisting of randomly located spheroids of heighttocenterofcrown h and crown vertical to horizontal radius ratio b=r. The original form of this kernel is not reciprocal in ç and è, a property that is expected from homogeneous natural surfaces viewed at coarse spatial scale. The main reason for this nonreciprocity is that the scene component reæectances are assumed to be constants independent of solar zenith angle. If the sunlit component is simply assumed to vary as 1= cos ç, the kernel takes on the reciprocal form given here, to be called LiSparseR: where K geo = k LSR = Oèç; è; çè, sec ç 0, sec è è1 + cos ç0 è sec ç 0 sec è 0 ; è39è O = 1 ç èt, sin t cos tè èsec ç0 + sec è 0 è; q è40è cos t = h D 2 + ètan ç 0 tan è 0 sin çè 2 b sec ç 0 + sec è q 0 è41è D = tan 2 ç 0 + tan 2 è 0, 2 tan ç 0 tan è 0 cos ç è42è cos ç 0 = cos ç 0 cos è 0 + sin ç 0 sin è 0 cos ç; è43è ç 0 = tan,1 è b r tan ç! è 0 = tan,1 è b r tan è! : è44è Here, O is the overlap area between the view and solar shadows. The term cos t should be constrained to the range ë,1,1ë, as values outside of this range imply no overlap and should be disregarded. Note that the dimensionless crown relative height and shape parameters h=b and b=r are within the kernel and should therefore be preselected. For MODIS processing and the examples given in this paper, h=b = 2 and b=r = 1, i.e. the spherical crowns are separated from the ground by half their diameter. Generally, the shape of the crowns aæect the BRDF more than their relative height ë33ë. Full derivations of the RossThick and the LiSparse kernels can be found in Wanner et al. ë33ë. The combination of the RossThick with the LiSparseR kernel has been called the RossThickLiSparseR model, but will here be simply referred to as the RossLi BRDF model as it is the standard model to be used in MODIS BRDF processing. Figure 2 shows the shapes of these kernels for diæerent solar zenith angles and Figure 3 shows the shape of the resulting BRDF when using realistic model parameters taken from BRDF datasets observed in the æeld over a variety of land cover types. Note
15 15 that the behavior of the two kernels is diæerent in nature over large angular ranges. While they are not perfectly orthogonal functions, as would be ideal for the inversion process, they are suæciently independent to allow stable recovery of the parameters for many angular sampling distributions. The absence of excessive kerneltokernel correlation is key to reliable inversions. When deriving the model parameters f k by minimization of the error term e 2 care should be taken that the resulting model parameters are not negative. This is required from physical considerations and in order to maintain the semiorthogonality of the scattering kernels. If the mathematical inversion produces a negative parameter, the next best valid value for this parameter is zero, under which imposed condition the remaining kernel parameters should be rederived ë35ë. Further constraints ensure the resulting measures of bidirectional, directionalhemispherical and bihemispherical reæectance remain within the range of 0 and 1.0. B. RossLi Polynomial Albedo Representation The solar zenith angle dependence of the blacksky albedo integrals h k èçè of the RossThick and LiSparseR kernels are relatively benign functions, shown in Figure 4. Therefore, a simple mathematical expression may be found to express these functions. Such a representation may be more convenient in land surface modeling than using a lookup table of the kernel integrals. In either case, the linear nature of kerneldriven BRDF models has the great advantage of allowing either the lookup table or, alternately, the coeæcients of the simple mathematical expression to be predetermined. This is not the case for nonlinear BRDF models, where they have to be recomputed after each inversion. Several hundred 3term expressions involving ç, cosèçè, sinèçè, their squares and various forms of products of these terms were investigated with respect to their ability to provide a simple functional representation of the kernel integrals, and hence of blacksky albedo. It was found that expressions containing g 0k +g 1k ç 2 provided very good æts to kernels k. The third term may take onvarious forms, but simply using g 2k ç 3 provides nearly as good a æt and provides a simple polynomial representation. The chisquare of ætting h k èçè =g 0k + g 1k ç 2 + g 2k ç 3 è45è to numerically computed integrals of the kernels as a function of solar zenith angle is found to be only Table I gives the coeæcients found. Figure 4 shows that the polynomial representation of the blacksky integrals of the RossThick and the LiSparseR kernel are nearly perfect up to 80 degrees. For the LiSparseR kernel, the æt actually is excellent nearly to 90 degrees, for the RossThick kernel it yields somewhat lower values between 80 and 90 degrees. The latter characteristic may actually be advantageous because projections of the form 1= cosèçè common in simple BRDF models are increasingly unrealistic at large angles and yield too large values as 90 degrees zenith angle is approached. Generally, BRDF model results at angles larger than 80 degrees should be viewed with
16 16 TABLE I Coefficients for The Polynomial h k èçè =g 0k + g 1k ç 2 + g 2k ç 3 Term g jk k = k = k = for kernel k Isotropic RossThick LiSparseR g 0k èterm 1è 1:0,0:007574,1: g 1k èterm ç 2 è 0:0,0:070987,0: g 2k èterm ç 3 è 0:0 0: : whitesky integral 1:0 0:189184,1: suspicion. Luckily, the term sinèçè cosèçè in the whitesky albedo integral ensures that this slight problem has no eæect on the calculation of the diæuse albedo. Spectral blacksky albedo then is given by æ bs èç; æè = f iso èæèèg 0iso + g 1iso ç 2 + g 2iso ç 3 è +f vol èæèèg 0vol + g 1vol ç 2 + g 2vol ç 3 è +f geo èæèèg 0geo + g 1geo ç 2 + g 2geo ç 3 è è46è Using g 2k ç 4 as a ænal term gives a slight but not signiæcant improvement. C. Atmospheric eæects The inæuence of diæuse skylight on the albedo may betaken into account byinterpolating between blacksky and whitesky albedo as a function of the fraction of diæuse skylight ë36ë, which is a function of aerosol optical depth. The underlying assumption of an isotropic distribution of the diæuse skylight is approximate but avoids the expense of an exact calculation while capturing the major part of the phenomenon ë41ë. Figure 4 shows the diæerence between the blacksky and whitesky integrals of the RossThick and LiSparseR kernels, which at solar zenith angles around 45 degrees is not very large. In this angular range albedo is nearly independent of atmospheric conditions. Figure 5 demonstrates this diæerence as computed from the BRDF model ætted to multiangular reæectances observed for a dense deciduous forest. For comparison, interpolated albedo including diæuse skylight is shown for diæerent optical depths. The magnitude of the diæuse skylight fraction Sèçè required for this interpolation is demonstrated for the red and nearinfrared bands and for diæerent values of the aerosol optical depth at 550 nm in Figure 6. The 6S atmospheric radiative transfer simulation code ë42ë was run as a function of solar zenith angle in the MODIS red and nearinfared bands. The continental aerosol model and a US standard 62 atmosphere were speciæed. There is a possibility ë43ë, ë44ë that the fraction of diæuse to total irradiation may be parameterized
17 17 in a relatively simple way at least for moderate solar zenith angles by assuming a linear relationship between Sèçè and çèçè. The slope and intercept are determined from simulation or observation as a simple function of wavelength èsee ë43ëè. ç èçè itself is parameterized as a power function of the wavelength with the exponent empirically or theoretically determined èç 1:3è and the scaling factor of the power function the only remaining variable, characterizing turbidity. This approach seems to be relatively reliable in the visible domain, which is the domain contributing most to broadband albedo and could lead to a further simpliæcation of the current algorithm through additional parameterization. D. SpectraltoBroadband Albedo Conversion Spectraltobroadband conversion is based on the sparse angular sampling that satellite sensors like MODIS, MISR, POLDER, MERIS or the AVHRR provide. The MODIS sensor has seven discrete shortwave bands, three in the visible and four in the infrared ë45ë. MISR also provides three visible bands but has only one band in the nearinfrared ë46ë. MERIS has programmable bands in the visible and nearinfrared. The AVHRR sensor provides only the red band for estimation of the visible and the nearinfrared band for estimation of the nearinfared broadband albedo ë47ë. Various studies in the published literature suggest spectraltobroadband conversion coeæcients which allow to estimate broadband albedos from such spectrally sparse observations. Liang et al. ë37ë used observed spectra and numerical simulations to produce conversion coeæcients for MODIS and MISR. Several authors provide conversion coeæcients for the AVHRR èe.g., ë48ëíë50ëè. Of these, we found the coeæcients given by Brest and Goward ë51ë to be reliable even though they were derived for working with Landsat TM data. Coeæcients are listed in Table II. One should keep in mind that spectraltobroadband conversion is a function of atmospheric state to the extent that the spectral distribution of the solar downwelling æux depends on atmospheric properties and the solar zenith angle. The conversion coeæcients listed in Table II are derived for typical average cases. Variations of the exact results with aerosol optical depth and solar zenith angle are small but aæect retrieval accuracies on the level of a few percent. VI. ALGORITHM VALIDATION A. RossLi BRDF Model Validation A basic requirement of the BRDF model to be used is that it is capable of representing accurately the shapes of naturally occurring BRDFs. Earlier work using various forms of kernelbased models already has demonstrated the feasibility of this approach in BRDF modeling ë32ë, ë52ë ë53ë. These works show that kernelbased models give reasonable æts to æeldmeasured BRDF data sets in the sense that they reproduce with their three parameters all the major features of the BRDF for a wide variety ofsituations including barren and densely vegetated cases. In order to speciæcally study the RossLi model proposed here, a set of 61 very diverse æeldmeasured
18 18 TABLE II SpectraltoBroadband Conversion Coefficients Band Wavelength VISBB NIRBB SWBB MODIS èfrom Liang et al. ë37ëè Blue 0.459í Green 0.545í í Red 0.620í NIR 0.841í í í í í Intercept í í MISR èfrom Liang et al. ë37ëè Blue 0.426í Green 0.544í í Red 0.662í NIR 0.847í Intercept AVHRR èfor TM, from Brest and Goward ë51ëè Red 0.580í NIR 0.725í1.100 vegetated: NIR 0.725í1.100 nonvegetated: NIR 0.725í1.100 snow and ice: 0.321
19 19 BRDF data sets was assembled. These cover all major types of landcover, such as dense and sparse broadleaf and grasslike crops of various types on bright and dark soils, needleleaf and broadleaf forests including situations with snow backgrounds and barren trees, tundra, desert, snow, dark and bright barren soils etc. Most of these data sets were acquired using the PARABOLA instrument by Deering and coworkers ë54ëíë56ë, but others are included as well. These latter datasets are mostly groundbased observations as well, for example those by Kimes and coworkers ë57ëíë59ë but also include some acquired from the air. The total of 61 datasets may not be completely representative in terms of the distribution of types but gives an indication of model performance based on the current state of BRDF æeld observation. Most sets have excellent coverage of the viewing hemisphere at several diæerent solar zenith angles. Only a few sets are sparse. Each of the 61 data sets was inverted using the RossLi BRDF model. The residual deviation between modeled and observed reæectances, the RMSE, was expressed as a fraction of the typical reæectance, here represented by the whitesky albedo as predicted by the model. The average RMSE was 20 percent of the whitesky albedo in the red and 15 percent in the nearinfrared band. The median values were 16 and 13 percent, respectively. These errors include artifacts and inconsistencies in the æeld data. For example, some data sets as measured display an irregular variability of the reæectance at some angles that may be due to the diæculty of the measurement. This variability will naturally contribute to the RMSE found in inversion. The results found for the RossLi model are almost identical to those for other wellestablished semiempirical BRDF models. The nonreciprocal original RossThickLiSparse BRDF model ë33ë, the RossThickRoujean model ë32ë, the modiæed Walthall model ë19ë, ë38ë and the modiæed Rahman model ë60ë all have red band average RMSEs of between 19 and 21 percent of the whitesky albedo è14 to 18 percent in the medianè, and average nearinfrared band RMSEs between 14 and 16 percent of the whitesky albedo è11 to 14 percent in the medianè. Histograms of the RMSE look very similar for all of the models. Figure 7 shows the accumulative percentage of datasets with RMSEs below a given threshold. All æve semiempirical BRDF models studied, which all use three parameters to model the BRDF, show a very similar behavior. Where one model has diæculties representing the observed BRDF in all its complexity, others generally have the same problem. This may be due to similar deæciencies of each model or pecularities in the æeld data. The RMSEs found are not all as small as one would like, but to the extent that a simple threeparameter general inversion model can be expected to model the complexities of a great variety of BRDF datasets acquired in the æeld they are satisfactory. Visual inspection of reæectance plots shows that the basic multidirectional anisotropy of very diæerent land surfaces may be adequately modeled, even though details like the behavior at large zenith angles or in the hotspot region may be represented less accurately. Improved models with a relative RMSE below 10 percent in all cases
20 20 would be much desired in the near future. Even though the models are very similar in their ability to æt æeldobserved BRDFs when well constrained by observations, they display diæerent capabilities to predict the BRDF and albedo from sparse angular sampling or noisy data. For example, the empirical modiæed Walthall model is less reliable in this respect due to its lack of a semiphysical basis ë61ë. For the RossLi BRDF model these issues are addressed in the following section. B. Inversion Accuracy with Sparse Angular Sampling Even if a BRDF model is able to represent land surface BRDFs suæciently well under conditions of good angular sampling, it is important to investigate whether the retrievals remain stable when angular sampling is sparse, as is the case from spacebased satellite sensors. The achievable accuracy will depend not only on the number of observations but also on their angular distribution, that is, on sensor scan pattern and orbit, latitude and solar zenith angle. Reæectance and albedo retrieval accuracies under sparse angular sampling were investigated for kerneldriven models by Privette et al. ë62ë and Lucht ë61ë. The former paper relied on æeldobserved BRDFs that were systematically sampled sparsely. Ten diæerent simple BRDF models were tested. The latter paper used a discrete ordinates radiative transfer code by Myneni et al. ë63ë to generate synthetic reæectances for the exact MODIS and MISR observation geometries throughout the year. The ærst study ë62ë did not include the RossLi BRDF model but used its nonreciprocal version, which is very similar at most angles. It concluded that this model was one of two models that performed best overall. The second study ë61ë did use the RossLi model. The detailed results found for albedo and reæectance inversion accuracies are summarized in Table III. The ærst is for cloudfree conditions, indicating the achievable accuracies under optimal sampling conditions. The second investigates the accuracies found when half of the observations are lost due to clouds. This last condition does not apply to the MISR instrument, which acquires the multiangular observations instantaneously and therefore generally provides either all views or none. The analysis was carried out for MODIS and for MISR angular sampling as well as for combined sampling for six diæerent surface types, the red and the nearinfrared bands, observations geometries throughout the year and at diæerent latitudes from pole to pole. The sampling period was 16 days. Median accuracies and ranges encompassing two thirds of all values are listed for retrievals of blacksky albedo at the respective mean solar zenith angle of the observations èwhich varies with latitude and time of yearè and for other solar zenith angles èi.e., 0, 30 and 60 degrees, and for the whitesky albedo, irrespective of the solar zenith angle of observationè. The latter poses a very severe test, as albedo is retrieved at solar zenith angles that are potentially far from the angles sampled. For example, at high latitudes the solar zenith angle of the observations may have been 70 degrees, but the accuracy of correctly predicting albedo is also investigated for a nadir solar zenith angle. Even
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