A neural network-based four-band model for estimating the total absorption coefficients from the global oceanic and coastal waters

doi:10.1002/2014JC010461

	A neural network-based four-band model for estimating the total absorption coefficients from the global oceanic and coastal waters
	Chen, Jun 1,2; Cui, Tingwei 3; Quan, Wenting 4
刊名	JOURNAL OF GEOPHYSICAL RESEARCH-OCEANS
	2015-01
卷号	120 期号:1 页码:36-49
关键词	remote sensing inherent optical property global oceanic and coastal waters neural network optical activity constituents
ISSN号	2169-9275
DOI	10.1002/2014JC010461
英文摘要	In this study, a neural network-based four-band model (NNFM) for the global oceanic and coastal waters has been developed in order to retrieve the total absorption coefficients a(). The applicability of the quasi-analytical algorithm (QAA) and NNFM models is evaluated by five independent data sets. Based on the comparison of a() predicted by these two models with the field measurements taken from the global oceanic and coastal waters, it was found that both the QAA and NNFM models had good performances in deriving a(), but that the NNFM model works better than the QAA model. The results of the QAA model-derived a(), especially in highly turbid waters with strong backscattering properties of optical activity, was found to be lower than the field measurements. The QAA and NNFM models-derived a() could be obtained from the MODIS data after atmospheric corrections. When compared with the field measurements, the NNFM model decreased by a 0.86-24.15% uncertainty (root-mean-square relative error) of the estimation from the QAA model in deriving a() from the Bohai, Yellow, and East China seas. Finally, the NNFM model was applied to map the global climatological seasonal mean a(443) for the time range of July 2002 to May 2014. As expected, the a(443) value around the coastal regions was always larger than the open ocean around the equator. Viewed on a global scale, the oceans at a high latitude exhibited higher a(443) values than those at a low latitude.
资助项目	Serial Maps of Geology and Geophysics on China Seas and Land on the Scale of 1: 1000000[200311000001]
WOS关键词	INHERENT OPTICAL-PROPERTIES ; QUASI-ANALYTICAL ALGORITHM ; ATMOSPHERIC CORRECTION ; CHLOROPHYLL-A ; SEMIANALYTICAL MODEL ; MERIS DATA ; COLOR DATA ; VALIDATION ; INVERSION ; REFLECTANCE
WOS研究方向	Oceanography
语种	英语
出版者	AMER GEOPHYSICAL UNION
WOS记录号	WOS:000349890000003
内容类型	期刊论文
源URL	[http://ir.fio.com.cn:8080/handle/2SI8HI0U/26091]
专题	自然资源部第一海洋研究所
通讯作者	Chen, Jun
作者单位	1.China Univ Geosci, Sch Ocean Sci, Beijing, Peoples R China 2.Qingdao Inst Marine Geol, Qingdao, Peoples R China 3.State Ocean Adm, Inst Oceanog 1, Qingdao, Peoples R China 4.Shanxi Agr Remote Sensing Informat, Xian, Peoples R China
推荐引用方式 GB/T 7714	Chen, Jun,Cui, Tingwei,Quan, Wenting. A neural network-based four-band model for estimating the total absorption coefficients from the global oceanic and coastal waters[J]. JOURNAL OF GEOPHYSICAL RESEARCH-OCEANS,2015,120(1):36-49.
APA	Chen, Jun,Cui, Tingwei,&Quan, Wenting.(2015).A neural network-based four-band model for estimating the total absorption coefficients from the global oceanic and coastal waters.JOURNAL OF GEOPHYSICAL RESEARCH-OCEANS,120(1),36-49.
MLA	Chen, Jun,et al."A neural network-based four-band model for estimating the total absorption coefficients from the global oceanic and coastal waters".JOURNAL OF GEOPHYSICAL RESEARCH-OCEANS 120.1(2015):36-49.