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Zhang H.,Beijing Normal University | Zhang H.,Key Laboratory of Environmental Remote Sensing and Digital City | Jiao Z.,Beijing Normal University | Jiao Z.,Key Laboratory of Environmental Remote Sensing and Digital City | And 6 more authors.
Journal of Remote Sensing | Year: 2013

This paper estimated HJ-1 land surface albedos in the Heihe region using the backup algorithm of Moderate Resolution Imaging Spectroradiometer (MODIS) Bidirectional Reflectance Distribution Function (BRDF)/Albedo product (method I), the Bayesian inference-based algorithm (method II) and the Lambertian surface algorithm (method III). Compared inversed albedos with surface observations, statistical analysis results showed that: (1) The high resolution albedos from HJ-1 CCD data can provide spatial distribution of underlying surface as well as surface details. Different land cover types' statistic values indicates that method I and method II capture similar results, with the absolute error of 0.01 and the relative error of 4% as compared with albedos from MODIS, while method III has the absolute error of 0.03 and relative error of 13%.(2) The improvement in the albedo by method I and method II is almost independent to land cover types, capturing relative error between 2% and 8%; However, the temporal reliance of estimated albedo is more significant, and the improvement is more obvious in the maturity than in the dormancy. (3) Surface albedos estimated by method I and method II have better consistency with field observations.The root mean square errors are less than 0.05, and relative errors are less than 23%, while results of method III are 0.069 and 36.3%, respectively. (4) The retrieval of albedos based on the prior knowledge may depend on the geometry of the sun and the observation, and thus depend on the season and the latitude, as well as sensor specifications. This study will provide significant understanding for space-borne albedo retrieval which lacks of sufficient multi-angular observations.


Dong Y.,Beijing Normal University | Jiao Z.,Beijing Normal University | Jiao Z.,State Key Laboratory of Remote Sensing Science | Jiao Z.,Key Laboratory of Environmental Remote Sensing and Digital City | And 4 more authors.
Journal of Remote Sensing | Year: 2014

An algorithm for modeling bidirectional reflectance anisotropics of land surfaces has been developed as a surrogate for the operational MODIS Bidirectional Reflectance Distribution Function (BRDF) and albedo product for user community. This algorithm is a set of kernel-driven BRDF models extensively used in several space-borne remotely sensed BRDF/albedo products. Among these models, RossThick (RT)-LiSparseR (RTLSR) has been selected as the current operational MODIS BRDF/albedo algorithm. However, the hotspot effect has not been considered in RT kernel. As such, the use of an RTLSR model underestimates hotspot reflectance, thereby influencing the accuracy of the retrieval of vegetation structures, such as clumping index. On the basis of Breon's hotspot factor, Maignan corrected RT kernel to generate a RTMaignan (RTM) kernel. For producers, a 13-year MODIS BRDF/albedo product is reprocessed using this corrected model, but this task is time consuming. For users, the direct use of this corrected model for MODIS observations is complicated because the equivalent inputs of the operational RTLSR algorithm are not easily available. In this study, a method was developed to correct the hotspot effect for the operational MODIS BRDF product, which is available for users. Based on the effective validation using POLDER-3/BRDF data and the selected MODIS data, this study shows that (1) an improvement of approximately 10.12% of relative error between our method and the RTLSR model can be obtained by estimating the hotspot reflectance; (2) a relative error of approximately 2.10% occurs between this method and the RTM-LiSparseR (RTMLSR) model, but this difference is not significant; (3) relative error reaches approximately 4.99% between this method and the RTLSR model to simulate NDHD but decreases to approximately 1.32% between this method and the RTMLSR model.

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