Validation of temporal BRDFs of paddy fields estimated from MODIS reflectance data

The effect of the bidirectional reflectance distribution function (BRDF) is one of the most important factors in correcting and validating the reflectance obtained from remotely sensed data. While the importance of BRDF has become widely recognized, bidirectional reflectance factor (BRF) data measured for correction and validation are insufficient because of the technical difficulty of the measurement. The primary objective of the present research is to estimate BRDF effects from Moderate Resolution Imaging Spectroradiometer (MODIS) data. Temporal ground-based BRDFs of rice paddy fields were estimated from ground measurements conducted in June and August 2002. MODIS-derived BRDFs obtained from MODIS reflectance data and ground-based BRDFs were estimated using the reciprocal form of the RossThick and LiSparse (RossThick-LiSparse-R) kernels, a semiempirical BRDF model adopted for the operational MODIS BRDF product. The MODIS-derived band 1 (620-680 nm) and band 2 (841-876 nm) BRDFs were compared with the ground-based BRDFs corresponding to the same waveband, respectively. The comparison results demonstrate that BRDFs of paddy fields change in accordance with paddy growth and that MODIS-derived BRDFs are closely related to ground-based BRDFs in most of the cases. It was also revealed that MODIS-derived BRDFs can be estimated to a high degree of accuracy when MODIS data necessary for the estimation are available.

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

[2]  Michel M. Verstraete,et al.  Potential and limitations of information extraction on the terrestrial biosphere from satellite remote sensing , 1996 .

[3]  Mark Chopping,et al.  Large-Scale BRDF Retrieval over New Mexico with a Multiangular NOAA AVHRR Dataset , 2000 .

[4]  Gregory P. Asner,et al.  Ecological Research Needs from Multiangle Remote Sensing Data , 1998 .

[5]  Compton J. Tucker,et al.  Directional reflectance factor distributions for cover types of Northern Africa , 1985 .

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

[7]  Alan H. Strahler,et al.  Retrieval of red spectral albedo and bidirectional reflectance using AVHRR HRPT and GOES satellite observations of the New England region , 1999 .

[8]  J. Cihlar,et al.  AVHRR bidirectional reflectance effects and compositing , 1994 .

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

[10]  Peter J. Rousseeuw,et al.  Robust Regression and Outlier Detection , 2005, Wiley Series in Probability and Statistics.

[11]  Mark Chopping,et al.  Testing a LiSK BRDF Model with in Situ Bidirectional Reflectance Factor Measurements over Semiarid Grasslands , 2000 .

[12]  Klaus I. Itten,et al.  A field goniometer system (FIGOS) for acquisition of hyperspectral BRDF data , 1999, IEEE Trans. Geosci. Remote. Sens..

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

[14]  Chad J. Shuey,et al.  Validating MODIS land surface reflectance and albedo products: methods and preliminary results , 2002 .

[15]  N. C. Strugnell,et al.  First operational BRDF, albedo nadir reflectance products from MODIS , 2002 .

[16]  W. Lucht,et al.  Considerations in the parametric modeling of BRDF and albedo from multiangular satellite sensor observations , 2000 .

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

[18]  Albert Rango,et al.  Improved semi-arid community type differentiation with the NOAA AVHRR via exploitation of the directional signal , 2002, IEEE Trans. Geosci. Remote. Sens..

[19]  Azriel Rosenfeld,et al.  Robust regression methods for computer vision: A review , 1991, International Journal of Computer Vision.

[20]  Roselyne Lacaze,et al.  G-function and HOt SpoT (GHOST) reflectance model: application to multi-scale airborne POLDER measurements , 2001 .

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

[22]  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).

[23]  G. Guyot,et al.  2 – OPTICAL PROPERTIES OF VEGETATION CANOPIES , 1990 .

[24]  Junichi Susaki,et al.  Robust estimation of BRDF model parameters , 2004 .

[25]  Wenge Ni,et al.  A Coupled Vegetation-Soil Bidirectional Reflectance Model for a Semiarid Landscape , 2000 .

[26]  Alan H. Strahler,et al.  Using a multikernel least-variance approach to retrieve and evaluate albedo from limited bidirectional measurements , 2001 .

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

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

[29]  Bernard Pinty,et al.  Extracting information on surface properties from bidirectional reflectance measurements , 1991 .

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

[31]  Didier Tanré,et al.  Retrieval of land surface parameters from airborne POLDER bidirectional reflectance distribution function during HAPEX‐Sahel , 1997 .

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