Bidirectional NDVI and atmospherically resistant BRDF inversion for vegetation canopy

The normalized difference vegetation index (NDVI) has been widely applied in optical remote sensing. However, it has been demonstrated that NDVI is still partially affected by atmospheric path scattering and bidirectional (illumination and viewing geometry) effects. In this paper we present the benefit of using a bidirectional NDVI, and we discuss the problems in using the maximum NDVI composite method. Based on the assumption that a clear day has a larger NDVI value and a cloudy day has a smaller NDVI value (smaller reflectance in the near-infrared band and larger reflectance in red band due to atmospheric path scattering), the ratio of squared observed NDVI values and calculated NDVI values is used as a weight in our inversion method. The calculated NDVI values are derived from previously inverted bidirectional reflectance distribution functions (BRDFs). The inversion process will loop until all weights converge. Our research on the early Terra/MODIS data using a semiempirical kernel-driven BRDF model (the RossThick-LiTransit model) shows that this new method can improve inversion results whenever some cloudy pixels are not filtered out. As cloud detection and subpixel cloudiness are always a problem, this technique should still be very useful in improving the quality of BRDF inversion.

[1]  A. Huete,et al.  Normalization of multidirectional red and NIR reflectances with the SAVI , 1992 .

[2]  Compton J. Tucker,et al.  Maximum normalized difference vegetation index images for sub-Saharan Africa for 1983-1985 , 1986 .

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

[4]  Philip N. Slater,et al.  Discrimination of growth and water stress in wheat by various vegetation indices through clear and turbid atmospheres , 1983 .

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

[6]  R. 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.

[7]  B. Holben.  Characteristics of maximum-value composite images from temporal AVHRR data , 1986 .

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

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

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

[11]  Feng Gao,et al.  Acquiring a priori knowledge from ground and spaceborne BRDF measurements , 2000, IGARSS 2000. IEEE 2000 International Geoscience and Remote Sensing Symposium. Taking the Pulse of the Planet: The Role of Remote Sensing in Managing the Environment. Proceedings (Cat. No.00CH37120).

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

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

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

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

[16]  C. Tucker.  Red and photographic infrared linear combinations for monitoring vegetation , 1979 .

[17]  Alan H. Strahler,et al.  The Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF and albedo product: preliminary results , 2000, IGARSS 2000. IEEE 2000 International Geoscience and Remote Sensing Symposium. Taking the Pulse of the Planet: The Role of Remote Sensing in Managing the Environment. Proceedings (Cat. No.00CH37120).

[18]  N. C. Strugnell,et al.  An algorithm to infer continental-scale albedo from AVHRR data, land cover class, and field observations of typical BRDFs , 2001 .

[19]  C. Justice,et al.  Analysis of the phenology of global vegetation using meteorological satellite data , 1985 .

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

[21]  Jindi Wang,et al.  A priori knowledge accumulation and its application to linear BRDF model inversion , 2001 .

[22]  Alan H. Strahler,et al.  Evaluation of the Li transit kernel for BRDF modeling , 2000 .

[23]  Didier Tanré,et al.  Second Simulation of the Satellite Signal in the Solar Spectrum, 6S: an overview , 1997, IEEE Trans. Geosci. Remote. Sens..

[24]  C. Justice,et al.  An examination of spectral band ratioing to reduce the topographic effect on remotely sensed data , 1981 .

[25]  Feng Gao,et al.  Parameter error propagation in BRDF derived by fitting multiple angular observations at single sun position , 2000, IGARSS 2000. IEEE 2000 International Geoscience and Remote Sensing Symposium. Taking the Pulse of the Planet: The Role of Remote Sensing in Managing the Environment. Proceedings (Cat. No.00CH37120).

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

[27]  P. Bicheron,et al.  Bidirectional reflectance distribution function signatures of major biomes observed from space , 2000 .

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