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Institute of Electrical and Electronics Engineers, IEEE Transactions on Geoscience and Remote Sensing, 6(44), p. 1555-1565, 2006

DOI: 10.1109/tgrs.2006.871564

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Validation of the MODIS bidirectional reflectance distribution function and albedo retrievals using combined observations from the aqua and terra platforms.

This paper is available in a repository.
This paper is available in a repository.

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Abstract

We evaluate the performance of the MODerate Resolution Imaging Spectroradiometer (MODIS) Bidirectional Reflectance Distribution Function (BRDF)/Albedo algorithm using observations from MODIS instruments aboard NASA's Terra (EOS AM-1) and Aqua (EOS PM-1) platforms. This "combined" albedo product is evaluated against continuous field measurements from SURFace RADiation Budget Network (SURFRAD) and Atmospheric Radiation Measurement Southern Great Plains (ARM/SGP) stations, and through an internal analysis of the product's quality assurance (QA) fields. The combined product is compared to the initial MODIS albedo product, which used observations from the Terra satellite only. During the spring and summer months, the combined product showed a slight improvement over the original Terra-only albedo product, with a root mean square error (RMSE) of 0.0130 and a bias of about -0.02. As with the Terra-only product, accuracy drops during the fall and winter months at some sites. Jin et al. found that increased heterogeneity of validation sites during the fall and winter months is partially responsible for this drop in accuracy. The additional data provided by the Aqua platform changes high-quality albedo estimations only slightly, which underscores the stability of the MODIS algorithm. The most significant benefit of the combined product is a near 50% decrease in lower quality backup algorithm retrievals for the entire globe. A decrease in backup algorithm retrievals improves the overall accuracy of the MODIS albedo product, as it reduces algorithm reliance upon an a priori determination of the underlying surface anisotropy that is not entirely data derived.