Estimation of BRDF model kernel weights under an a priori knowledge-aided constraint

doi:10.1080/2150704X.2020.1823036

YIC-IR > 中国科学院海岸带环境过程与生态修复重点实验室 > 海岸带信息集成与战略规划研究中心

	Estimation of BRDF model kernel weights under an a priori knowledge-aided constraint
	Tian, Xinpeng 1,2; Gao, Zhiqiang1,2 ; Liu, Qiang 3,4,5; Wang, Yueqi 1,2; Li, Xiuhong 3,4,5
发表期刊	REMOTE SENSING LETTERS
ISSN	2150-704X
	2021-02-01
卷号	12 期号:2 页码:146-155
DOI	10.1080/2150704X.2020.1823036
通讯作者	Gao, Zhiqiang(zqgao@yic.ac.cn)
英文摘要	The reflectance anisotropy of land surface serves as an important bridge between surface biophysical parameters and remote sensing observations. It can characterize by the linear kernel-driven bidirectional reflectance distribution function (BRDF), which is the combination of several kernel functions and kernel weights. These kernel weights can be estimated by remote sensing; however, the stability of current kernel weights products is still challenging, especially in urban areas with complex aerosol properties and heterogeneous surfaces. In this paper, we propose a method for robust estimation of kernel weights from the Moderate Resolution Imaging Spectroradiometer (MODIS) surface spectral reflectance products (MxD09GA) data based on the constrained least-squares method (CLSM) and a priori knowledge. The kernel weights data were obtained by the CLSM from 2014 to 2017 in Beijing region of China. Validations were carried out using the MxD09GA and BRDF/Albedo products (MCD43A1). The results show that the time series of kernel weights by the CLSM show small variability over different land cover types. The kernel weights estimated by the CLSM can clearly show the phenological signal and fitting ability of surface spectral reflectance is better than that of the MCD43A1 products in Beijing urban area. Experimental results demonstrate that the CLSM has the potential for the robust estimation of kernel weights in urban areas.
资助机构	National Natural Science Foundation of China ; NSFC-Shandong joint fund project ; Key Deployment Project of Centre for Ocean Mega-Research of Science, Chinese Academy of Science ; Key Research Program of Frontier Science, Chinese Academy of Sciences ; National Key Research and Development Program ; Open Fund of CAS Key Laboratory of Marine Ecology and Environmental Sciences ; National Key Research and Development Program of China
收录类别	SCI
语种	英语
研究领域[WOS]	Remote Sensing ; Imaging Science & Photographic Technology
WOS记录号	WOS:000609535800001
引用统计
文献类型	期刊论文
条目标识符	http://ir.yic.ac.cn/handle/133337/27412
专题	中国科学院海岸带环境过程与生态修复重点实验室_海岸带信息集成与战略规划研究中心中国科学院海岸带环境过程与生态修复重点实验室
通讯作者	Gao, Zhiqiang
作者单位	1.Chinese Acad Sci, CAS Key Lab Coastal Environm Proc & Ecol Remediat, Yantai Inst Coastal Zone Res, Yantai, Peoples R China 2.Chinese Acad Sci, Shandong Key Lab Coastal Environm Proc, Yantai Inst Coastal Zone Res, Yantai, Peoples R China 3.Beijing Normal Univ, Coll Global Change & Earth Syst Sci, Beijing, Peoples R China 4.Beijing Normal Univ, State Key Lab Remote Sensing Sci, Beijing, Peoples R China 5.Chinese Acad Sci, State Key Lab Remote Sensing Sci, Inst Remote Sensing & Digital Earth, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Tian, Xinpeng,Gao, Zhiqiang,Liu, Qiang,et al. Estimation of BRDF model kernel weights under an a priori knowledge-aided constraint[J]. REMOTE SENSING LETTERS,2021,12(2):146-155.
APA	Tian, Xinpeng,Gao, Zhiqiang,Liu, Qiang,Wang, Yueqi,&Li, Xiuhong.(2021).Estimation of BRDF model kernel weights under an a priori knowledge-aided constraint.REMOTE SENSING LETTERS,12(2),146-155.
MLA	Tian, Xinpeng,et al."Estimation of BRDF model kernel weights under an a priori knowledge-aided constraint".REMOTE SENSING LETTERS 12.2(2021):146-155.

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