当前位置： 首页»科研成果» 2014 刘强、梁顺林与合作者在IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING发表论文 全球院

 

Ying Qu, Qiang Liu, Shunlin Liang, Fellow, IEEE, Lizhao Wang, Nanfeng Liu, and Suhong Liu

 

School of Geography, Beijing Normal University, Beijing 100875, China;

State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Beijing Normal University and Institute of Remote Sensing Applications of Chinese Academy of Sciences, Beijing 100875, China;

College of Global Change and Earth System Science, Beijing Normal University, Beijing 100875, China;

Institute of Remote Sensing Applications, Chinese Academy of Sciences, Beijing 100875, China;

Department of Geography, University of Maryland, College Park, Maryland, USA.

 

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 Spectroradiometer (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.

 

KEY WORDS: Angular bin regression, direct-estimation algorithm, land-surface broadband albedo, Moderate Resolution Imaging Spectroradiometer (MODIS), polarization and directionality of the Earth’s reflectance (POLDER) bidirectional

reflectance distribution function (BRDF) database

 

PUBLISHED BY: IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2014, 52 (2): 907-919.

 

SOURCE: http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6514500&tag=1