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Analysis of BRDF and Albedo Retrieved by Kernel-Driven Models Using Field Measurements

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5 Author(s)
Xingying Huang ; Sch. of Geogr., Beijing Normal Univ., Beijing, China ; Ziti Jiao ; Yadong Dong ; Hu Zhang
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The algorithm for Model Bidirectional Reflectance Anisotropies of the Land Surface (AMBRALS) includes a series of kernel-driven bidirectional reflectance distribution function (BRDF) models. Among these models, the RossThick-LiSparse-Reciprocal (RTLSR) model has been selected as the operational MODIS BRDF/Albedo algorithm. With the newly developed LiTransit kernel and Ross kernel with the hotspot effect by Bréon, there is a need to further evaluate models' potentials in retrieving land surface properties for user community. In this paper, 16 kernel-driven models in reciprocal form have been analyzed using 70 measurements mainly from ground campaigns of the First International Satellite Land Surface Climatology Project (ISLSCP) Field Experiment (FIFE) and the Boreal Ecosystem Atmosphere Study (BOREAS). Results show that all models under investigation generally fit the ground measurements with reasonable accuracy-although the “sparse” geometric-optical (GO) kernels appear to present better behaviors compared with the “dense” GO kernels-in combining with volumetric shapes. Estimated albedos from these models show high correlations, while the black-sky albedos (BSAs) present higher correlations in small solar zenith angles than in large ones, indicating that the difference in these kernels may arise from large solar geometry. A further investigation shows that the hotspot effects for these models display a significant discrepancy. Although the additional hotspot factor for Ross kernels significantly improves the hotspot effects, LiTransit kernel can well characterize the hotspot effects in combination with Ross kernels without the hotspot factor. Such an assessment may be helpful in understanding the models' potentials in retrieving vegetation structural characteristics.

Published in:

Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of  (Volume:6 ,  Issue: 1 )

Date of Publication:

Feb. 2013

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