@article{
 title = {Direct-estimation algorithm for mapping daily land-surface broadband albedo from modis data},
 type = {article},
 year = {2014},
 keywords = {FR_FON,FR_HES,FR_PUE},
 pages = {907-919},
 volume = {52},
 id = {16a07bc4-04da-3f92-b9d1-eca4e228ebdd},
 created = {2016-03-08T11:01:32.000Z},
 file_attached = {false},
 profile_id = {5c1040db-25e3-36ea-a919-0994a44709e7},
 group_id = {c4af41cc-7e3c-3fd3-9982-bdb923596eee},
 last_modified = {2017-03-14T17:16:18.928Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {Qu2014a},
 private_publication = {false},
 abstract = {Land surface albedo is a critical parameter in surface-energy budget studies. Over the past several decades, many albedo products are generated from remote-sensing data sets. The Moderate Resolution Imaging Spectroradiome- ter (MODIS) bidirectional reflectance distribution function (BRDF)/Albedo algorithm is used to routinely produce eight day (16-day composite), 1-km resolution MODIS albedo products. When some natural processes or human activities occur, the land-surface broadband albedo can change rapidly, so it is necessary to enhance the temporal resolution of albedo product. We present a direct-estimation algorithm for mapping daily land- surface broadband albedo from MODIS data. The polarization and directionality of the Earth’s reflectance-3/polarization and anisotropy of reflectances for atmospheric sciences coupled with observations from a Lidar BRDF database is employed as a training data set, and the 6S atmospheric radiative transfer code is used to simulate the top-of-atmosphere (TOA) reflectances. Then a relationship between TOA reflectances and land-surface broadband albedos is developed using an angular bin regression method. The robustness of this method for different angular bins, aerosol conditions, and land-cover types is analyzed. Simulation results show that the absolute error of this algorithm is ∼0.009 for vegetation, 0.012 for soil, and 0.030 for snow/ice. Validation of the direct-estimation algorithm against in situ measurement data shows that the proposed method is capable of characterizing the temporal variation of albedo, especially when the land-surface BRDF changes rapidly. Index},
 bibtype = {article},
 author = {Qu, Ying and Liu, Qiang and Liang, Shunlin and Wang, Lizhao and Liu, Nanfeng and Liu, Suhong},
 doi = {10.1109/TGRS.2013.2245670},
 journal = {IEEE Transactions on Geoscience and Remote Sensing},
 number = {2}
}

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Then a relationship between TOA reflectances and land-surface broadband albedos is developed using an angular bin regression method. The robustness of this method for different angular bins, aerosol conditions, and land-cover types is analyzed. Simulation results show that the absolute error of this algorithm is ∼0.009 for vegetation, 0.012 for soil, and 0.030 for snow/ice. Validation of the direct-estimation algorithm against in situ measurement data shows that the proposed method is capable of characterizing the temporal variation of albedo, especially when the land-surface BRDF changes rapidly. 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Then a relationship between TOA reflectances and land-surface broadband albedos is developed using an angular bin regression method. The robustness of this method for different angular bins, aerosol conditions, and land-cover types is analyzed. Simulation results show that the absolute error of this algorithm is ∼0.009 for vegetation, 0.012 for soil, and 0.030 for snow/ice. Validation of the direct-estimation algorithm against in situ measurement data shows that the proposed method is capable of characterizing the temporal variation of albedo, especially when the land-surface BRDF changes rapidly. 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