Forward a Small-Timescale BRDF/Albedo by Multisensor Combined BRDF Inversion Model
作者:Wen, JG (Wen, Jianguang)[ 1,2 ] ; Dou, BC (Dou, Baocheng)[ 1,3 ] ; You, DQ (You, Dongqin)[ 1 ] ; Tang, Y (Tang, Yong)[ 1 ] ; Xiao, Q (Xiao, Qing)[ 1 ] ; Liu, Q (Liu, Qiang)[ 2,3 ] ; Qinhuo, L (Qinhuo, Liu)[ 1,2 ]
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
卷: 55 期: 2 页: 683-697
出版年: FEB 2017
In this paper, the land surface bidirectional reflectance distribution function (BRDF) and albedo on a small timescale are retrieved by the multisensor combined BRDF inversion (MCBI) model with improved accuracy. The accumulation period for this BRDF/albedo retrieval is shortened to 8 and 4 days with data from four satellite sensors, the Moderate Resolution Imaging Spectraradiometer (MODIS), Advanced Very High Resolution Radiometer (AVHRR), Visible Infrared Imaging Radiometer (VIIRS), and Medium Resolution Spectral Imager (MERSI), to obtain the dynamic features of land surfaces. All the four sensors have high revisit frequencies and dense angular sampling. The MCBI model provides an algorithm to form a virtual MODIS observation network with these four sensors, resulting in a multiband and multiangle sampling reflectance data set. It also provides a multisensor reflectance quality control index, the net information index (NII), for a robust BRDF/albedo retrieval. The performance of the MCBI is assessed by comparisons withMODIS BRDF/albedo product and the in situ measurement. The results show that the highly frequent angular sampling with four sensors allows for a full retrieval of BRDF/albedo with a shorter accumulation period of 8 and 4 days. The NII reduces the uncertainties when using different sensors' reflectance and allows for a high-quality BRDF/albedo retrieval. It reveals that the MCBI has the potential to generate a multisensor-based BRDF/albedo on a small timescale. The MCBI is a key algorithm for the BRDF/albedo product in China's multisource data synergized quantitative remote sensing production system and operationally implemented to generate a global product.
通讯作者地址: Xiao, Q (通讯作者)
Chinese Acad Sci, Inst Remote Sensing & Digital Earth, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China.
[ 1 ] Chinese Acad Sci, Inst Remote Sensing & Digital Earth, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China
[ 2 ] Joint Ctr Global Change Studies, Beijing 100875, Peoples R China
[ 3 ] Beijing Normal Univ, Coll Global Change & Earth Syst Sci, Beijing 100875, Peoples R China