Evaluation of the Li transit kernel for BRDF modeling

Kernel‐driven bidirectional reflectance distribution function (BRDF) models are widely used in the description of BRDF properties of land types. To further improve the ability of kernel‐driven semiempirical models to capture the BRDF of the surface, a geometrical kernel, the LiTransit kernel, has recently been derived. The LiTransit kernel strives to improve the physical parameterization of geometric‐optical effects while keeping the ability to fit the data well and to calculate accurate albedos. It is part of our continuing work on the enhancement of semiempirical kernel‐driven BRDF models. We tested the new kernel's performance using ground, airborne and spaceborne bidirectional measurements obtained from various sources. The results show that the LiTransit kernel retains the ability to fit BRDF shapes in sparsely vegetated regions as well as the LiSparse‐Reciprocal kernel while providing continuity in the transition from sparse to dense vegetation covers. We have also tested the new kernel combination RossThick/LiTransit to ascertain its capacity to predict bidirectional reflectance and directional hemispherical reflectance (black‐sky albedo) when presented with data with a MODIS‐like sampling. The new kernel combination performs better in modeling reflectance at high view zenith angles, particularly under high illumination zenith angles than the Li‐Sparse‐Reciprocal kernel which is currently in operational use. The bihemispherical reflectance (white‐sky albedo) derived using spaceborne POLDER (Polarization and Directionality of the Earth's Radiation instrument) samples with the RossThick‐LiSparse‐Reciprocal and the RossThick‐LiTransit models are very similar. Their correlation coefficient is larger then 0.99. This demonstrates that the RossThick‐LiTransit model retains the best features of RossThick‐LiSparse‐Reciprocal model while incorporating more realistic physics into the formulation. An analysis of the noise sensitivity of the RossThick‐LiTransit model shows that the noise propagation factors of both the RossThick‐LiSparseReciprocal and RossThick‐LiTransit models are smaller than 50 percent for data with MODIS like sampling when the number of looks vary from 7 to 16.

[1]  Retrieving albedo in small sample size , 1998, IGARSS '98. Sensing and Managing the Environment. 1998 IEEE International Geoscience and Remote Sensing. Symposium Proceedings. (Cat. No.98CH36174).

[2]  J. Muller,et al.  Sampling the surface bidirectional reflectance distribution function (BRDF): 1. evaluation of current and future satellite sensors , 1994 .

[3]  Donald W. Deering,et al.  Shinnery oak bidirectional reflectance properties and canopy model inversion , 1992, IEEE Trans. Geosci. Remote. Sens..

[4]  Thomas F. Eck,et al.  Reflectance anisotropy for a spruce-hemlock forest canopy , 1994 .

[5]  John C. Gille,et al.  Algorithm Theoretical Basis Document , 1999 .

[6]  W. Lucht.  Expected retrieval accuracies of bidirectional reflectance and albedo from EOS-MODIS and MISR angular sampling , 1998 .

[7]  Ranga B. Myneni,et al.  A Three-Dimensional Radiative Transfer Method for Optical Remote Sensing of Vegetated Land Surfaces , 1991, [Proceedings] IGARSS'91 Remote Sensing: Global Monitoring for Earth Management.

[8]  A. Strahler,et al.  Geometric-Optical Bidirectional Reflectance Modeling of a Conifer Forest Canopy , 1986, IEEE Transactions on Geoscience and Remote Sensing.

[9]  Charles L. Walthall,et al.  Prairie grassland bidirectional reflectances measured by different instruments at the FIFE site , 1992 .

[10]  J. Muller,et al.  MODIS BRDF / Albedo Product : Algorithm Theoretical Basis Document Version 5 . 0 , 1999 .

[11]  Michael J. Barnsley,et al.  Global retrieval of bidirectional reflectance and albedo over land , 1997 .

[12]  Alan H. Strahler,et al.  Geometric-optical bidirectional reflectance modeling of the discrete crown vegetation canopy: effect of crown shape and mutual shadowing , 1992, IEEE Trans. Geosci. Remote. Sens..

[13]  Ross Nelson,et al.  Directional Reflectance Distributions of a Hardwood and Pine Forest Canopy , 1986, IEEE Transactions on Geoscience and Remote Sensing.

[14]  H. Rahman,et al.  Coupled surface-atmosphere reflectance (CSAR) model: 2. Semiempirical surface model usable with NOAA advanced very high resolution radiometer data , 1993 .

[15]  D. Deering,et al.  A sphere-scanning radiometer for rapid directional measurements of sky and ground radiance: The PARABOLA field instrument , 1984 .

[16]  J. Roujean,et al.  A bidirectional reflectance model of the Earth's surface for the correction of remote sensing data , 1992 .

[17]  J. Privette,et al.  Estimating spectral albedo and nadir reflectance through inversion of simple BRDF models with AVHRR/MODIS‐like data , 1997 .

[18]  J. Ross.  The radiation regime and architecture of plant stands , 1981, Tasks for vegetation sciences 3.

[19]  Alan H. Strahler,et al.  Validation of Kernel-Driven Semiempirical Models for the Surface Bidirectional Reflectance Distribution Function of Land Surfaces , 1997 .

[20]  John V. Martonchik,et al.  Determination of aerosol optical depth and land surface directional reflectances using multiangle imagery , 1997 .

[21]  François-Marie Bréon,et al.  Angular signatures of surface reflectances from airborne POLDER data , 1996 .

[22]  Alan H. Strahler,et al.  An algorithm for the retrieval of albedo from space using semiempirical BRDF models , 2000, IEEE Trans. Geosci. Remote. Sens..

[23]  Alan H. Strahler,et al.  Derivation and validation of a new kernel for kernel-driven BRDF models , 1999, Remote Sensing.

[24]  D. Kimes.  Dynamics of directional reflectance factor distributions for vegetation canopies. , 1983, Applied optics.

[25]  J. Roujean,et al.  Retrieval of atmospheric properties and surface bidirectional reflectances over land from POLDER/ADEOS , 1997 .

[26]  A. Strahler,et al.  On the derivation of kernels for kernel‐driven models of bidirectional reflectance , 1995 .

[27]  Wolfgang Lucht,et al.  Theoretical noise sensitivity of BRDF and albedo retrieval from the EOS-MODIS and MISR sensors with respect to angular sampling , 2000 .